fish_model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyKBYrOpuPIW",
        "outputId": "b83892c2-bb50-4b50-a87c-a6a3ce1f9c98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fastai in /usr/local/lib/python3.10/dist-packages (2.7.12)\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from fastai) (23.1.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from fastai) (23.1)\n",
            "Requirement already satisfied: fastdownload<2,>=0.0.5 in /usr/local/lib/python3.10/dist-packages (from fastai) (0.0.7)\n",
            "Requirement already satisfied: fastcore<1.6,>=1.5.29 in /usr/local/lib/python3.10/dist-packages (from fastai) (1.5.29)\n",
            "Requirement already satisfied: torchvision>=0.8.2 in /usr/local/lib/python3.10/dist-packages (from fastai) (0.15.2+cu118)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from fastai) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from fastai) (1.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from fastai) (2.31.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from fastai) (6.0.1)\n",
            "Requirement already satisfied: fastprogress>=0.2.4 in /usr/local/lib/python3.10/dist-packages (from fastai) (1.0.3)\n",
            "Requirement already satisfied: pillow>6.0.0 in /usr/local/lib/python3.10/dist-packages (from fastai) (9.4.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from fastai) (1.2.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from fastai) (1.11.2)\n",
            "Requirement already satisfied: spacy<4 in /usr/local/lib/python3.10/dist-packages (from fastai) (3.6.1)\n",
            "Requirement already satisfied: torch<2.1,>=1.7 in /usr/local/lib/python3.10/dist-packages (from fastai) (2.0.1+cu118)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.0.7)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.4.7)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.0.9)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.23.5)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.10.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (67.7.2)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (2023.7.22)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=1.7->fastai) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=1.7->fastai) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=1.7->fastai) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=1.7->fastai) (3.1)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch<2.1,>=1.7->fastai) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch<2.1,>=1.7->fastai) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch<2.1,>=1.7->fastai) (16.0.6)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (4.42.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->fastai) (2023.3.post1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->fastai) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->fastai) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->fastai) (1.16.0)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<4->fastai) (0.7.10)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<4->fastai) (0.1.2)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<4->fastai) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<4->fastai) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<2.1,>=1.7->fastai) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "%pip install fastai\n",
        "import pandas as pd\n",
        "import torch\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IiOzuaB5V6ZQ"
      },
      "outputs": [],
      "source": [
        "from fastai.tabular.all import *\n",
        "\n",
        "pd.options.display.float_format = '{:.2f}'.format\n",
        "set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "1uaEa1enjJFA"
      },
      "outputs": [],
      "source": [
        "fish_data = pd.read_csv(\"/content/drive/MyDrive/Data/new_clean_fish_data.csv\", index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vtPI4x3MuQjI",
        "outputId": "60daab99-5f23-4791-8bb0-d419e60f2a6c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-aa65131e-bf67-47f5-8576-16b143c08cdd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>presence</th>\n",
              "      <th>origin</th>\n",
              "      <th>seasonal</th>\n",
              "      <th>event_year</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>order_as_num</th>\n",
              "      <th>family_as_num</th>\n",
              "      <th>genus_as_num</th>\n",
              "      <th>category_as_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1976.00</td>\n",
              "      <td>-15.38</td>\n",
              "      <td>35.55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1995.00</td>\n",
              "      <td>-9.55</td>\n",
              "      <td>33.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1993.00</td>\n",
              "      <td>21.52</td>\n",
              "      <td>-87.74</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1979.00</td>\n",
              "      <td>8.92</td>\n",
              "      <td>-69.51</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>6.62</td>\n",
              "      <td>80.22</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-aa65131e-bf67-47f5-8576-16b143c08cdd')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-aa65131e-bf67-47f5-8576-16b143c08cdd 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-aa65131e-bf67-47f5-8576-16b143c08cdd');\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-9384875b-971b-4a8a-a080-4504cfc27647\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9384875b-971b-4a8a-a080-4504cfc27647')\"\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-9384875b-971b-4a8a-a080-4504cfc27647 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   presence  origin  seasonal  event_year  latitude  longitude  order_as_num  \\\n",
              "0         1       1         1     1976.00    -15.38      35.55             0   \n",
              "1         1       1         1     1995.00     -9.55      33.89             0   \n",
              "2         1       1         1     1993.00     21.52     -87.74             1   \n",
              "3         1       1         1     1979.00      8.92     -69.51             1   \n",
              "4         1       1         1     2014.00      6.62      80.22             0   \n",
              "\n",
              "   family_as_num  genus_as_num  category_as_num  \n",
              "0              0             0                0  \n",
              "1              0             0                0  \n",
              "2              1             1                0  \n",
              "3              2             2                0  \n",
              "4              3             3                1  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fish_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VA_Pb_Q2CW77",
        "outputId": "634ca57c-2434-4a4b-c8cb-d132e27c57e4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5f40c1c1-6504-4fcf-b85f-85cc3e9f251d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>presence</th>\n",
              "      <th>origin</th>\n",
              "      <th>seasonal</th>\n",
              "      <th>event_year</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>order_as_num</th>\n",
              "      <th>family_as_num</th>\n",
              "      <th>genus_as_num</th>\n",
              "      <th>category_as_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "      <td>35447.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.03</td>\n",
              "      <td>1.07</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1989.73</td>\n",
              "      <td>7.54</td>\n",
              "      <td>-32.20</td>\n",
              "      <td>2.27</td>\n",
              "      <td>13.68</td>\n",
              "      <td>116.59</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>27.24</td>\n",
              "      <td>11.50</td>\n",
              "      <td>71.15</td>\n",
              "      <td>2.02</td>\n",
              "      <td>16.06</td>\n",
              "      <td>98.39</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1700.00</td>\n",
              "      <td>-38.93</td>\n",
              "      <td>-112.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1977.00</td>\n",
              "      <td>5.49</td>\n",
              "      <td>-83.98</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1999.00</td>\n",
              "      <td>8.51</td>\n",
              "      <td>-75.29</td>\n",
              "      <td>2.00</td>\n",
              "      <td>8.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2010.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>34.40</td>\n",
              "      <td>3.00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>206.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2022.00</td>\n",
              "      <td>40.01</td>\n",
              "      <td>161.75</td>\n",
              "      <td>15.00</td>\n",
              "      <td>72.00</td>\n",
              "      <td>348.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-5f40c1c1-6504-4fcf-b85f-85cc3e9f251d')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-5f40c1c1-6504-4fcf-b85f-85cc3e9f251d 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-5f40c1c1-6504-4fcf-b85f-85cc3e9f251d');\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-a22e6383-1911-4061-811e-ec01e72ed590\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a22e6383-1911-4061-811e-ec01e72ed590')\"\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-a22e6383-1911-4061-811e-ec01e72ed590 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       presence   origin  seasonal  event_year  latitude  longitude  \\\n",
              "count  35447.00 35447.00  35447.00    35447.00  35447.00   35447.00   \n",
              "mean       1.03     1.07      1.00     1989.73      7.54     -32.20   \n",
              "std        0.30     0.37      0.00       27.24     11.50      71.15   \n",
              "min        1.00     1.00      1.00     1700.00    -38.93    -112.17   \n",
              "25%        1.00     1.00      1.00     1977.00      5.49     -83.98   \n",
              "50%        1.00     1.00      1.00     1999.00      8.51     -75.29   \n",
              "75%        1.00     1.00      1.00     2010.00     11.00      34.40   \n",
              "max        6.00     5.00      1.00     2022.00     40.01     161.75   \n",
              "\n",
              "       order_as_num  family_as_num  genus_as_num  category_as_num  \n",
              "count      35447.00       35447.00      35447.00         35447.00  \n",
              "mean           2.27          13.68        116.59             0.71  \n",
              "std            2.02          16.06         98.39             1.36  \n",
              "min            0.00           0.00          0.00             0.00  \n",
              "25%            1.00           4.00         21.00             0.00  \n",
              "50%            2.00           8.00         91.00             0.00  \n",
              "75%            3.00          15.00        206.00             1.00  \n",
              "max           15.00          72.00        348.00             7.00  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fish_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2UFBH_44UMni",
        "outputId": "d2dac90f-a4ee-41a5-9267-9c7669a607fc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-22524e88-bd2a-4b02-be74-ed2314010fda\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>presence</th>\n",
              "      <th>origin</th>\n",
              "      <th>seasonal</th>\n",
              "      <th>event_year</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>order_as_num</th>\n",
              "      <th>family_as_num</th>\n",
              "      <th>genus_as_num</th>\n",
              "      <th>category_as_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.54</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.22</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-22524e88-bd2a-4b02-be74-ed2314010fda')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-22524e88-bd2a-4b02-be74-ed2314010fda 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-22524e88-bd2a-4b02-be74-ed2314010fda');\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-d996b85b-3cec-442c-b694-a20d312496ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d996b85b-3cec-442c-b694-a20d312496ec')\"\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-d996b85b-3cec-442c-b694-a20d312496ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   presence  origin  seasonal  event_year  latitude  longitude  order_as_num  \\\n",
              "0      0.17    0.20      1.00        0.98     -0.38       0.22          0.00   \n",
              "1      0.17    0.20      1.00        0.99     -0.24       0.21          0.00   \n",
              "2      0.17    0.20      1.00        0.99      0.54      -0.54          0.07   \n",
              "3      0.17    0.20      1.00        0.98      0.22      -0.43          0.07   \n",
              "4      0.17    0.20      1.00        1.00      0.17       0.50          0.00   \n",
              "\n",
              "   family_as_num  genus_as_num  category_as_num  \n",
              "0           0.00          0.00             0.00  \n",
              "1           0.00          0.00             0.00  \n",
              "2           0.01          0.00             0.00  \n",
              "3           0.03          0.01             0.00  \n",
              "4           0.04          0.01             0.14  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for column in fish_data.columns:\n",
        "    fish_data[column] = fish_data[column]  / fish_data[column].abs().max()\n",
        "fish_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WjJPs7ejVzYq"
      },
      "outputs": [],
      "source": [
        "#splits = RandomSplitter(seed=42)(fish_data)\n",
        "splits = RandomSplitter(valid_pct=0.2)(range_of(fish_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "t9i1Dcl5XEvt"
      },
      "outputs": [],
      "source": [
        "dls = TabularPandas(\n",
        "    fish_data, splits=splits,\n",
        "    #procs = [Categorify, FillMissing, Normalize],\n",
        "    procs = [Categorify],\n",
        "    cat_names=[\"category_as_num\",\"order_as_num\",\"family_as_num\",\"genus_as_num\", \"seasonal\", \"origin\", \"presence\"],\n",
        "    cont_names=[\"event_year\"],\n",
        "    y_names=[\"latitude\", \"longitude\"],\n",
        ").dataloaders(path=\".\", bs=256)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0Ub9rdlyYZ2H"
      },
      "outputs": [],
      "source": [
        "#dls.batch_size = 128\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "iebXHrnMYcHk"
      },
      "outputs": [],
      "source": [
        "learn = tabular_learner(dls, metrics=accuracy_multi, layers=[10,10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFtL3wOQo4nm",
        "outputId": "de858778-5cda-4bab-86cb-dd5cbc7e36bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "256\n"
          ]
        }
      ],
      "source": [
        "print(dls.bs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "LkT5vCXNYiBW",
        "outputId": "68c2f370-9a03-43ec-e42c-9cfaba1760e4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "SuggestedLRs(slide=0.033113110810518265, valley=0.0030199517495930195)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "learn.lr_find(suggest_funcs=(slide, valley))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "sO5jebcJic6K"
      },
      "outputs": [],
      "source": [
        "#??learn.fit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "de2FKYS8Ykdt",
        "outputId": "bfa5e9ee-82fc-40f6-aa1b-c02c784110db"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy_multi</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.015308</td>\n",
              "      <td>0.007325</td>\n",
              "      <td>0.570250</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.007974</td>\n",
              "      <td>0.007161</td>\n",
              "      <td>0.577303</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.006959</td>\n",
              "      <td>0.006083</td>\n",
              "      <td>0.576386</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.006701</td>\n",
              "      <td>0.006033</td>\n",
              "      <td>0.571731</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.006426</td>\n",
              "      <td>0.006210</td>\n",
              "      <td>0.573917</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.006242</td>\n",
              "      <td>0.005764</td>\n",
              "      <td>0.572648</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.005852</td>\n",
              "      <td>0.005465</td>\n",
              "      <td>0.577797</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.005873</td>\n",
              "      <td>0.005914</td>\n",
              "      <td>0.576880</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>0.005674</td>\n",
              "      <td>0.576033</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.005900</td>\n",
              "      <td>0.006183</td>\n",
              "      <td>0.586190</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.005557</td>\n",
              "      <td>0.005108</td>\n",
              "      <td>0.581605</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.005762</td>\n",
              "      <td>0.005295</td>\n",
              "      <td>0.581464</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.005709</td>\n",
              "      <td>0.005364</td>\n",
              "      <td>0.583721</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.005649</td>\n",
              "      <td>0.005707</td>\n",
              "      <td>0.575046</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.005392</td>\n",
              "      <td>0.005368</td>\n",
              "      <td>0.582099</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.005479</td>\n",
              "      <td>0.005262</td>\n",
              "      <td>0.585696</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "learn.fit(16, lr=0.01)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "T0sjzjhSZMyA",
        "outputId": "8f408be1-5306-4fc5-dc82-467ab475cec9"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "learn.recorder.plot_loss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NrG90DSlEyb",
        "outputId": "74795d72-3b6d-4deb-c72f-d4ed466e7780"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "presence          0.17\n",
              "origin            0.20\n",
              "seasonal          1.00\n",
              "event_year        0.98\n",
              "order_as_num      0.00\n",
              "family_as_num     0.00\n",
              "genus_as_num      0.00\n",
              "category_as_num   0.00\n",
              "Name: 0, dtype: float64"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#fish_data.head()\n",
        "pred_fish_data = fish_data.drop(columns=[\"latitude\", \"longitude\"])\n",
        "pred_fish_data.iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6qAKIz-m2VD",
        "outputId": "50ed5cfd-4019-4a79-e60c-df32710d74cf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "presence           0.17\n",
              "origin             0.20\n",
              "seasonal           1.00\n",
              "event_year         0.99\n",
              "latitude           0.54\n",
              "longitude         -0.54\n",
              "order_as_num       0.07\n",
              "family_as_num      0.01\n",
              "genus_as_num       0.00\n",
              "category_as_num    0.00\n",
              "Name: 2, dtype: float64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fish_data.iloc[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kPYAz86tm9It",
        "outputId": "e486b4ea-4750-4f99-f931-e3661831a3ba"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d67daab5-61dd-479f-961a-e845a596586f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>presence</th>\n",
              "      <th>origin</th>\n",
              "      <th>seasonal</th>\n",
              "      <th>event_year</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>order_as_num</th>\n",
              "      <th>family_as_num</th>\n",
              "      <th>genus_as_num</th>\n",
              "      <th>category_as_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.54</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.22</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-d67daab5-61dd-479f-961a-e845a596586f')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-d67daab5-61dd-479f-961a-e845a596586f 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-d67daab5-61dd-479f-961a-e845a596586f');\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-69bfe498-ed8c-43ab-abca-2ccf95e6fc53\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69bfe498-ed8c-43ab-abca-2ccf95e6fc53')\"\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-69bfe498-ed8c-43ab-abca-2ccf95e6fc53 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   presence  origin  seasonal  event_year  latitude  longitude  order_as_num  \\\n",
              "0      0.17    0.20      1.00        0.98     -0.38       0.22          0.00   \n",
              "1      0.17    0.20      1.00        0.99     -0.24       0.21          0.00   \n",
              "2      0.17    0.20      1.00        0.99      0.54      -0.54          0.07   \n",
              "3      0.17    0.20      1.00        0.98      0.22      -0.43          0.07   \n",
              "4      0.17    0.20      1.00        1.00      0.17       0.50          0.00   \n",
              "\n",
              "   family_as_num  genus_as_num  category_as_num  \n",
              "0           0.00          0.00             0.00  \n",
              "1           0.00          0.00             0.00  \n",
              "2           0.01          0.00             0.00  \n",
              "3           0.03          0.01             0.00  \n",
              "4           0.04          0.01             0.14  "
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fish_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "id": "4cSIBPb3j_z7",
        "outputId": "480d339d-db12-41e0-8539-7c2dd716f4cc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(   category_as_num  order_as_num  family_as_num  genus_as_num  seasonal  \\\n",
              " 0             1.00          0.00           0.00          0.00      1.00   \n",
              " \n",
              "    origin  presence  event_year  latitude  longitude  \n",
              " 0    0.00      0.00        0.99      0.30      -0.12  ,\n",
              " tensor([ 0.3010, -0.1163]),\n",
              " tensor([ 0.3010, -0.1163]))"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "learn.predict(pred_fish_data.iloc[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYRoI0XxoXUi"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}