AutoKeras_Heart_Disease.ipynb

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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# SETUP"
      ],
      "metadata": {
        "id": "x2H5QiCQ6Brv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## IMPORT LIBRARIES"
      ],
      "metadata": {
        "id": "dZE4XpRIlRFw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zXvBMa-r57wc"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.metrics import classification_report, confusion_matrix, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# autokeras\n",
        "!pip install autokeras\n",
        "import autokeras as ak"
      ],
      "metadata": {
        "id": "kKKoAT3tQNqE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7f96094-d41f-4d79-b4f9-b1bc8efdb9fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting autokeras\n",
            "  Downloading autokeras-1.1.0-py3-none-any.whl (148 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from autokeras) (1.5.3)\n",
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            "\u001b[?25hCollecting keras-tuner>=1.1.0\n",
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            "\u001b[?25hCollecting tensorflow-text\n",
            "  Downloading tensorflow_text-2.12.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.0 MB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow>=2.8.0->autokeras) (3.2.2)\n",
            "Installing collected packages: kt-legacy, keras-tuner, tensorflow-text, keras-nlp, autokeras\n",
            "Successfully installed autokeras-1.1.0 keras-nlp-0.4.1 keras-tuner-1.3.5 kt-legacy-1.0.5 tensorflow-text-2.12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1\n",
        "# don't know if need - added during process of trying to get additional metrics - did not work"
      ],
      "metadata": {
        "id": "WndWVZB1_7QA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras\n",
        "import keras as keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uX7QqaTACx7h",
        "outputId": "85d33c43-73a1-4977-f1fb-e7934e16731c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.9/dist-packages (2.12.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOAD DATAFILES"
      ],
      "metadata": {
        "id": "ptJbkZyVlWbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load Cleveland datafile\n",
        "cleveland_csv_path = \"processed.cleveland.data\"\n",
        "cleveland_data = pd.read_csv(cleveland_csv_path, header = None)\n",
        "# assign column names\n",
        "cleveland_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "XCVoL6th6RgI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "316c331d-dc56-43b7-cfac-0c37cd4cb888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-f9b1f45b3555>:5: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
            "  cleveland_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load Hungarian datafile\n",
        "hungarian_csv_path = \"reprocessed.hungarian.data\"\n",
        "hungarian_data = pd.read_csv(hungarian_csv_path, delimiter = ' ', header = None)\n",
        "# assign column names\n",
        "hungarian_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "ArOUzcYh_8QY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccfb78a8-0ff0-4806-e734-2a75a3456984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-682e5198959a>:5: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
            "  hungarian_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load Switzerland datafile\n",
        "switzerland_csv_path = \"processed.switzerland.data\"\n",
        "switzerland_data = pd.read_csv(switzerland_csv_path, header = None)\n",
        "# assign column names\n",
        "switzerland_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "QoXybLsS2DLO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ec6b4d-f4a2-493c-ba38-3e6dfb1c7ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-5091b7e6b8f1>:5: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
            "  switzerland_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load VA datafile\n",
        "va_csv_path = \"processed.va.data\"\n",
        "va_data = pd.read_csv(va_csv_path, header = None)\n",
        "# assign column names\n",
        "va_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n",
        "                  'restecg', 'thalach', 'exang', 'oldpeak', 'slope',\n",
        "                  'ca', 'thal', 'num'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "XnYsimmk2Xkd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b05d403-b4a0-4d95-91cd-54eb2abbf91a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-d85eae1a383b>:5: FutureWarning: DataFrame.set_axis 'inplace' keyword is deprecated and will be removed in a future version. Use `obj = obj.set_axis(..., copy=False)` instead\n",
            "  va_data.set_axis(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs',\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DEFINE LABEL COLUMN"
      ],
      "metadata": {
        "id": "msgXBwyalaN_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define label column\n",
        "heart_label = 'num'"
      ],
      "metadata": {
        "id": "uk7ZN8fO78WW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CLEVELAND"
      ],
      "metadata": {
        "id": "nGc6o1iv-H_Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SIMPLEST OF MODELS\n",
        "zero data preprocessing performed"
      ],
      "metadata": {
        "id": "PR2Zvk3Kphjv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TAKE A PEEK"
      ],
      "metadata": {
        "id": "YAdmsaUZ70l2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "07zYOUDs8A9V",
        "outputId": "aea1c79a-17ea-4b04-b3dd-fe34beb53899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca thal  num  \n",
              "0    3.0  0.0  6.0    0  \n",
              "1    2.0  3.0  3.0    2  \n",
              "2    2.0  2.0  7.0    1  \n",
              "3    3.0  0.0  3.0    0  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qn1NVt0unAxB",
        "outputId": "3c2458ef-ecd7-4513-99ef-4f7bc6b0e451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 33.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODELING\n",
        "(Note: \"Cleveland\" label here would need to be revised because used in Slightly Better Model below when split the data, if care.)"
      ],
      "metadata": {
        "id": "ggNQJeV47eEO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### PREPARE DATA"
      ],
      "metadata": {
        "id": "ArPpoyZTiY0H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy for binary labeling\n",
        "cleveland_binary = cleveland_data.copy()\n",
        "cleveland_binary['num'][cleveland_binary['num'] > 0] = 1\n",
        "cleveland_binary.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "U_UxlTkU8FbU",
        "outputId": "29337f76-e645-43a5-9ce0-39a5abb4b11a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-100db5bdd73b>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cleveland_binary['num'][cleveland_binary['num'] > 0] = 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca thal  num  \n",
              "0    3.0  0.0  6.0    0  \n",
              "1    2.0  3.0  3.0    1  \n",
              "2    2.0  2.0  7.0    1  \n",
              "3    3.0  0.0  3.0    0  \n",
              "4    1.0  0.0  3.0    0  "
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d9b1ebc8-b44f-4865-8f97-a188197f6d64 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-d9b1ebc8-b44f-4865-8f97-a188197f6d64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the binary data\n",
        "from sklearn.model_selection import train_test_split\n",
        "cleveland_train, cleveland_test = train_test_split(cleveland_binary, test_size=0.2, random_state=42)\n",
        "cleveland_test_labels = cleveland_test[heart_label]\n",
        "cleveland_test_nolabel = cleveland_test.drop([heart_label], axis=1)\n",
        "cleveland_train_labels = cleveland_train[heart_label]\n",
        "cleveland_train_nolabel = cleveland_train.drop([heart_label], axis=1)\n",
        "cleveland_train.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CscKCVrX72qk",
        "outputId": "01e2d1fe-0bbb-4579-f437-3defd073719b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "195  67.0  1.0  4.0     100.0  299.0  0.0      2.0    125.0    1.0      0.9   \n",
              "162  54.0  0.0  3.0     110.0  214.0  0.0      0.0    158.0    0.0      1.6   \n",
              "230  52.0  0.0  3.0     136.0  196.0  0.0      2.0    169.0    0.0      0.1   \n",
              "52   44.0  1.0  4.0     112.0  290.0  0.0      2.0    153.0    0.0      0.0   \n",
              "30   69.0  0.0  1.0     140.0  239.0  0.0      0.0    151.0    0.0      1.8   \n",
              "\n",
              "     slope   ca thal  num  \n",
              "195    2.0  2.0  3.0    1  \n",
              "162    2.0  0.0  3.0    0  \n",
              "230    2.0  0.0  3.0    0  \n",
              "52     1.0  1.0  3.0    1  \n",
              "30     1.0  2.0  3.0    0  "
            ],
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              "  </thead>\n",
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              "      <th>195</th>\n",
              "      <td>67.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>230</th>\n",
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              "      <th>52</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-833899c0-0bda-41be-b974-463c0da1e884');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MODEL - TRAIN & EVALUATE"
      ],
      "metadata": {
        "id": "ABOukEn7icE_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate classifier\n",
        "clf = ak.StructuredDataClassifier(overwrite=True, max_trials = 3)"
      ],
      "metadata": {
        "id": "SoJeZedR7hhV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit model\n",
        "clf.fit(cleveland_train_nolabel, cleveland_train_labels)"
      ],
      "metadata": {
        "id": "BlZSZZ2w7nQB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict with best model\n",
        "cleveland_pred_y = clf.predict(cleveland_test_nolabel)"
      ],
      "metadata": {
        "id": "jDZyLihb7ra_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate best model with testing data\n",
        "print(clf.evaluate(cleveland_test_nolabel, cleveland_test_labels))\n",
        "# loss: 0.4278 & accuracy: 0.8689 with max_trials=3 & epochs=10 (elapsed time = 14s; one run took as long as 1m 24s)\n",
        "#       but rerunning may produce drastically different results (i.e. 0.8033)\n",
        "#       too few trials and epochs; too much \"luck of the draw\" (not likely ... see default settings notes)\n",
        "# default settings (100 max_trials, 1000 epochs) (39m 9s)\n",
        "# loss: 0.3654 & accuracy: 0.8361\n",
        "#       comparable/same accuracies as was getting with very simple version\n",
        "#       no reason to run model for 39m when get similar results in 14s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmIVx1m77ujO",
        "outputId": "9489eccf-f630-40c8-ee3e-fdd7fc5bb3f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.3567 - accuracy: 0.8361\n",
            "[0.35674744844436646, 0.8360655903816223]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### TABLES, PLOTS, AND SUCH"
      ],
      "metadata": {
        "id": "T2URP89GiSul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "confusion_matrix(cleveland_test_labels, cleveland_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLZo5ENgiA4i",
        "outputId": "12c51e50-bc95-44a2-9355-f8e7d0669fc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[26,  3],\n",
              "       [ 7, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MULTICLASS MODELING\n",
        "\n",
        "(only ~50% accuracy so far!)\n",
        "\n",
        "\n",
        "Future research thought: Multiclass classification. OneVsAll for have:don't have heart disease and then Mutliclass for specific type/severity of heart disease may be more effective."
      ],
      "metadata": {
        "id": "dfUtrD2ETRmn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_binary.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AdHdB2-fboIM",
        "outputId": "97e32467-fae0-4e5f-c8bd-b56578386681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca thal  num  \n",
              "0    3.0  0.0  6.0    0  \n",
              "1    2.0  3.0  3.0    1  \n",
              "2    2.0  2.0  7.0    1  \n",
              "3    3.0  0.0  3.0    0  \n",
              "4    1.0  0.0  3.0    0  "
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              "      <th>0</th>\n",
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              "      <td>145.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.3</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
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              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
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              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-284bea15-6e58-46ab-8bfc-21addeca792c 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-284bea15-6e58-46ab-8bfc-21addeca792c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aU8zv00lWgWS",
        "outputId": "4ddc2ef7-8909-49a6-fa54-11adf3938409"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca thal  num  \n",
              "0    3.0  0.0  6.0    0  \n",
              "1    2.0  3.0  3.0    2  \n",
              "2    2.0  2.0  7.0    1  \n",
              "3    3.0  0.0  3.0    0  \n",
              "4    1.0  0.0  3.0    0  "
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              "\n",
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              "          const element = document.querySelector('#df-94d456d6-8bd3-4339-b730-cddcf6ed937a');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split the multiclass data\n",
        "from sklearn.model_selection import train_test_split\n",
        "mccleveland_train, mccleveland_test = train_test_split(cleveland_data, test_size=0.2, random_state=42)\n",
        "mccleveland_test_labels = mccleveland_test[heart_label]\n",
        "mccleveland_test_nolabel = mccleveland_test.drop([heart_label], axis=1)\n",
        "mccleveland_train_labels = mccleveland_train[heart_label]\n",
        "mccleveland_train_nolabel = mccleveland_train.drop([heart_label], axis=1)\n",
        "mccleveland_train.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9Kmg6IAgTZjW",
        "outputId": "1fb2e063-ef09-45a3-f43a-4d8f91a4c738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "156  51.0  1.0  4.0     140.0  299.0  0.0      0.0    173.0    1.0      1.6   \n",
              "291  55.0  0.0  2.0     132.0  342.0  0.0      0.0    166.0    0.0      1.2   \n",
              "108  61.0  1.0  4.0     120.0  260.0  0.0      0.0    140.0    1.0      3.6   \n",
              "188  54.0  1.0  2.0     192.0  283.0  0.0      2.0    195.0    0.0      0.0   \n",
              "296  59.0  1.0  4.0     164.0  176.0  1.0      2.0     90.0    0.0      1.0   \n",
              "\n",
              "     slope   ca thal  num  \n",
              "156    1.0  0.0  7.0    1  \n",
              "291    1.0  0.0  3.0    0  \n",
              "108    2.0  1.0  7.0    2  \n",
              "188    1.0  1.0  7.0    1  \n",
              "296    2.0  2.0  6.0    3  "
            ],
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              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>51.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>342.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>61.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>59.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate classifier\n",
        "mcclf = ak.StructuredDataClassifier(overwrite=True, max_trials=3)"
      ],
      "metadata": {
        "id": "yDG8layDSymt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit model\n",
        "mcclf.fit(mccleveland_train_nolabel, mccleveland_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1auRKDyJUStk",
        "outputId": "b1be36be-92f6-4522-b383-229d4f99fa70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 3 Complete [00h 00m 09s]\n",
            "val_accuracy: 0.6666666865348816\n",
            "\n",
            "Best val_accuracy So Far: 0.7222222089767456\n",
            "Total elapsed time: 00h 00m 22s\n",
            "Epoch 1/6\n",
            "8/8 [==============================] - 1s 4ms/step - loss: 1.4720 - accuracy: 0.4628\n",
            "Epoch 2/6\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 1.3421 - accuracy: 0.5413\n",
            "Epoch 3/6\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 1.2397 - accuracy: 0.5620\n",
            "Epoch 4/6\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 1.1606 - accuracy: 0.5785\n",
            "Epoch 5/6\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 1.1022 - accuracy: 0.5744\n",
            "Epoch 6/6\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 1.0589 - accuracy: 0.5868\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb4a3ab20a0>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict with best model\n",
        "mccleveland_pred_y = clf.predict(mccleveland_test_nolabel)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnGxzoShUq45",
        "outputId": "b8eb1a59-3293-46c1-c41d-ad8bd804d6b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 4ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate best model with testing data\n",
        "print(mcclf.evaluate(mccleveland_test_nolabel, mccleveland_test_labels))\n",
        "\n",
        "# loss: 1.2442 & accuracy: 0.5082 ... !!!!!!!!!!!\n",
        "# May be best to stick with binary label, like everyone else has previously."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRUybfnbUzGF",
        "outputId": "01785c07-435f-4041-a19c-65eee315bc66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 8ms/step - loss: 1.2090 - accuracy: 0.4918\n",
            "[1.2090299129486084, 0.49180328845977783]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "#from sklearn.metrics import classification_report, confusion_matrix\n",
        "#confusion_matrix(mccleveland_test_labels, mccleveland_pred_y)\n",
        "\n",
        "# ERROR WHEN TRY TO RUN THIS CONFUSION MATRIX"
      ],
      "metadata": {
        "id": "NpaAvtBCjwQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SLIGHTLY BETTER MODEL\n",
        "no missing values & stratified sampling"
      ],
      "metadata": {
        "id": "3Dio3HabHu6p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "VuYocTRhqHz5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MISSING VALUES"
      ],
      "metadata": {
        "id": "4NZ7lFaCIL2W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy of original dataframe\n",
        "cleveland = cleveland_data.copy()"
      ],
      "metadata": {
        "id": "ShywUowp4shy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values\n",
        "cleveland.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ge1KJ0t4-3o",
        "outputId": "4d78560d-2dae-46a9-cb57-f5da39611f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 33.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replace '?' values with NaN so can impute\n",
        "cleveland['thal'].replace('?', np.NaN, inplace=True)\n",
        "cleveland['thal'] = cleveland['thal'].astype(float)\n",
        "cleveland['ca'].replace('?', np.NaN, inplace=True)\n",
        "cleveland['ca'] = cleveland['ca'].astype(float)\n",
        "cleveland.tail() # to verify didn't mess up dataframe IDs"
      ],
      "metadata": {
        "id": "9-K5ouSs5Oju",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e95f70db-d800-429d-a4b9-33cd2886ab68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
              "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
              "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
              "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
              "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
              "\n",
              "     slope   ca  thal  num  \n",
              "298    2.0  0.0   7.0    1  \n",
              "299    2.0  2.0   7.0    2  \n",
              "300    2.0  1.0   7.0    3  \n",
              "301    2.0  1.0   3.0    1  \n",
              "302    1.0  NaN   3.0    0  "
            ],
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              "\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>45.0</td>\n",
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              "      <th>299</th>\n",
              "      <td>68.0</td>\n",
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              "      <td>193.0</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>130.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>236.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>173.0</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make dataset version for plotting purposes\n",
        "cleveland_for_plot = cleveland.copy()"
      ],
      "metadata": {
        "id": "80ALuMDxcMNf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# impute with mode as 'thal' and 'ca' (attributes w/ missing values) are discrete\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "imputeMode.fit(cleveland) # fit - learns the data\n",
        "imputed = imputeMode.transform(cleveland) # transform - imputes with chosen strategy\n",
        "cleveland = pd.DataFrame(imputed, columns=cleveland.columns, index=cleveland.index) # back to pandas DataFrame\n",
        "cleveland.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrCeHciD8m9e",
        "outputId": "1dfbdb6a-c7d2-4454-c11d-1028e2ce3033"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    float64\n",
            " 12  thal      303 non-null    float64\n",
            " 13  num       303 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland.head() # check for anything obviously wonky"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53PYROVrEW7Z",
        "outputId": "ad1fa256-6269-4fde-947c-039babaf54ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca  thal  num  \n",
              "0    3.0  0.0   6.0  0.0  \n",
              "1    2.0  3.0   3.0  2.0  \n",
              "2    2.0  2.0   7.0  1.0  \n",
              "3    3.0  0.0   3.0  0.0  \n",
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              "      \n",
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              "      gap: 12px;\n",
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              "          const element = document.querySelector('#df-f54cb6b6-2ae0-4c25-a5a5-a4202a8db94f');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### BINARY LABEL\n",
        "(multiclass for \"future research\")"
      ],
      "metadata": {
        "id": "RqiWdhsTIQ3H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland['num'][cleveland['num'] > 0] = 1\n",
        "cleveland.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zP1I2-0ZISki",
        "outputId": "5144f802-e2fa-4861-8edc-69911c03f758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
              "\n",
              "   slope   ca  thal  num  \n",
              "0    3.0  0.0   6.0  0.0  \n",
              "1    2.0  3.0   3.0  1.0  \n",
              "2    2.0  2.0   7.0  1.0  \n",
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              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78cc3454-2d96-4bba-82f1-65258cc8aac1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "          const element = document.querySelector('#df-78cc3454-2d96-4bba-82f1-65258cc8aac1');\n",
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              "            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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### STRATIFIED SAMPLING\n",
        "\n",
        "(due to difference in sex representation)"
      ],
      "metadata": {
        "id": "6oc55538M-Yz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in cleveland_strat.split(cleveland, cleveland[\"num\"]):\n",
        "  cleveland_strat_train = cleveland.loc[train_index]\n",
        "  cleveland_strat_test = cleveland.loc[test_index]"
      ],
      "metadata": {
        "id": "eWIXOA1-NRYE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_test[\"num\"].value_counts()/len(cleveland_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pdyTPieQRK-",
        "outputId": "8653ef89-8416-4992-8b83-3865165c7ce6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.540984\n",
              "1.0    0.459016\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_train[\"num\"].value_counts()/len(cleveland_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vx2mSr8RQce9",
        "outputId": "2b86b294-f729-4995-c963-a69da407b973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.541322\n",
              "1.0    0.458678\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Less significant for Cleveland and Statlog datasets, which have less dramatic difference in sex representation in data, but different for other three datasets. "
      ],
      "metadata": {
        "id": "xWi_wP6iQ3HI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "cleveland_strat_train_X = cleveland_strat_train.drop([heart_label], axis=1)\n",
        "cleveland_strat_train_y = cleveland_strat_train[heart_label]\n",
        "cleveland_strat_test_X = cleveland_strat_test.drop([heart_label], axis=1)\n",
        "cleveland_strat_test_y = cleveland_strat_test[heart_label]\n",
        "cleveland_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxckdahgRjW8",
        "outputId": "8375b8c2-9cc7-4299-85fa-b54ffbc053aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "136  70.0  1.0  4.0     145.0  174.0  0.0      0.0    125.0    1.0      2.6   \n",
              "265  42.0  1.0  4.0     136.0  315.0  0.0      0.0    125.0    1.0      1.8   \n",
              "11   56.0  0.0  2.0     140.0  294.0  0.0      2.0    153.0    0.0      1.3   \n",
              "200  50.0  0.0  4.0     110.0  254.0  0.0      2.0    159.0    0.0      0.0   \n",
              "77   51.0  0.0  3.0     140.0  308.0  0.0      2.0    142.0    0.0      1.5   \n",
              "\n",
              "     slope   ca  thal  \n",
              "136    3.0  0.0   7.0  \n",
              "265    2.0  0.0   6.0  \n",
              "11     2.0  0.0   3.0  \n",
              "200    1.0  0.0   3.0  \n",
              "77     1.0  1.0   3.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dc71206-a4ab-4417-96a0-00e790e6bb99\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>70.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>145.0</td>\n",
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              "    <tr>\n",
              "      <th>265</th>\n",
              "      <td>42.0</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>140.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>254.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>159.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>51.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dc71206-a4ab-4417-96a0-00e790e6bb99')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3dc71206-a4ab-4417-96a0-00e790e6bb99 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3dc71206-a4ab-4417-96a0-00e790e6bb99');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODEL"
      ],
      "metadata": {
        "id": "M8Jxh9TmH6ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clev_clf = ak.StructuredDataClassifier(overwrite=True, seed = 42) # instantiate classifier\n",
        "clev_clf.fit(cleveland_strat_train_X, cleveland_strat_train_y) # fit model"
      ],
      "metadata": {
        "id": "SF8L3KdUI25G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffb1a054-5f43-42c5-9cf6-983310bdf77b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 100 Complete [00h 00m 06s]\n",
            "val_accuracy: 0.7222222089767456\n",
            "\n",
            "Best val_accuracy So Far: 0.9444444179534912\n",
            "Total elapsed time: 00h 09m 42s\n",
            "Epoch 1/3\n",
            "8/8 [==============================] - 1s 4ms/step - loss: 4.1682 - accuracy: 0.6942\n",
            "Epoch 2/3\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.7542 - accuracy: 0.7438\n",
            "Epoch 3/3\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.8768 - accuracy: 0.7686\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9e6c4627c0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_strat_pred_y = clev_clf.predict(cleveland_strat_test_X) # predict with best model\n",
        "print(clev_clf.evaluate(cleveland_strat_test_X, cleveland_strat_test_y)) # evaluate best model with testing data\n",
        "# pretty much same accuracy achieved as before removing missing values, but makes sense to do so regardless\n",
        "# (missing values removed only)\n",
        "# (   accuracies w/ max_trials=3 & default epochs: 0.8525, 0.8852, 0.8689)\n",
        "# (also stratified sampling by sex)\n",
        "# (   accuracies w/ max_trials=3 & default epochs: 0.7377, 0.8689)\n",
        "# (   accuracies w/ max_trials=10 & default epochs: 0.6066 (1m 3s), 0.8852 (58s))\n",
        "# (   accuracies w/ max_trials=100 & default epochs: 0.8689 (13m 25s))\n",
        "# default settings 03/24 run - accuracy 0.5902 and run time 17m 49s - NOT CONFIDENT THIS ACCURACY NOT A TYPO AND ACTUALLY LOSS VALUE\n",
        "\n",
        "# default settings 04/09 run - accuracy 0.8033 and run time 6m 35s\n",
        "\n",
        "# FOR F1 SCORE\n",
        "# default settings 04/16 run1 - accuracy 0.8033 and run time 9m 42s "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHUItOysKQTi",
        "outputId": "7ab01369-1294-406f-abe7-336f10efab08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.9966 - accuracy: 0.8033\n",
            "[1.9965647459030151, 0.8032786846160889]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "clev_model_0416r1 = clev_clf.export_model()\n",
        "clev_model_0416r1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx4PMSFB_Jv0",
        "outputId": "e2cadb56-bd79-4f10-ebcb-26238a51d9cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 13)]              0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 13)               0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " normalization (Normalizatio  (None, 13)               27        \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                448       \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1024)              33792     \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 1025      \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 35,292\n",
            "Trainable params: 35,265\n",
            "Non-trainable params: 27\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#keras.metrics.AUC(curve=\"ROC\")\n",
        "\n",
        "# grrr... still struggling getting any additional metrics from autokeras or keras"
      ],
      "metadata": {
        "id": "ashhHEo-BEvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save confusion matrix\n",
        "clev_0416_r1 = confusion_matrix(cleveland_strat_test_y, cleveland_strat_pred_y)"
      ],
      "metadata": {
        "id": "rOneFlTWv7bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show confusion matrix\n",
        "clev_0416_r1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BngKKHBIzfcm",
        "outputId": "6a5d455d-b262-4906-c16a-3e3ac26cc5aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[22, 11],\n",
              "       [ 1, 27]])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute f1 score\n",
        "import sklearn"
      ],
      "metadata": {
        "id": "91ZaILqRzn77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(cleveland_strat_test_y, cleveland_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm2hZngP0lvA",
        "outputId": "7101c7d6-2cf1-4d2f-d5ce-5f0a03b38a7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8181818181818182"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY WITH F1\n",
        "having trouble getting f1 to work for autokeras (I don't seem to be alone) ... \n",
        "going to resort to manual calculation"
      ],
      "metadata": {
        "id": "UtEVyba8rl65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#def f1_score(cleveland_strat_test_y, cleveland_strat_pred_y):\n",
        "#  ..."
      ],
      "metadata": {
        "id": "I-Oi8V4Prr7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#clev_f1_clf = ak.StructuredDataClassifier(\n",
        "#    overwrite=True, seed=42,\n",
        "\n",
        "    # Wrap the function into a Keras Tuner Objective \n",
        "    # and pass it to AutoKeras.\n",
        "\n",
        "    # Direction can be 'min' or 'max'\n",
        "    # meaning we want to minimize or maximize the metric.\n",
        "\n",
        "    # 'val_f1_score' is just add a 'val_' prefix\n",
        "    # to the function name or the metric name.\n",
        "\n",
        "#    objective=kerastuner.Objective('val_f1_score', direction='max'),\n",
        "    # Include it as one of the metrics.\n",
        "#    metrics=[f1_score],\n",
        "#)"
      ],
      "metadata": {
        "id": "M3BHbEO-sQ5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#cleveland_f1_y = clev_f1_clf.predict(cleveland_strat_test_X) # predict with best model\n",
        "#print(clev_f1_clf.evaluate(cleveland_strat_test_X, cleveland_strat_test_y)) # evaluate best model with testing data"
      ],
      "metadata": {
        "id": "z2SpvRtBtyrW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## REDUCED FOR CORRELATION\n",
        "(incl. no missing values & stratified sampling)"
      ],
      "metadata": {
        "id": "-1FJS77dfKGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "_d8zBip0fXW9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### REMOVE UNCORRELATED ATTRIBUTES"
      ],
      "metadata": {
        "id": "9eIqr1snfZdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check correlation\n",
        "cleveland_correlation = cleveland.corr()\n",
        "cleveland_correlation['num'].sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSAH5BKsfWsG",
        "outputId": "3beb59af-809a-4114-db00-4c6ca92183e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thalach    -0.417167\n",
              "fbs         0.025264\n",
              "chol        0.085164\n",
              "trestbps    0.150825\n",
              "restecg     0.169202\n",
              "age         0.223120\n",
              "sex         0.276816\n",
              "slope       0.339213\n",
              "cp          0.414446\n",
              "oldpeak     0.424510\n",
              "exang       0.431894\n",
              "ca          0.460033\n",
              "thal        0.522057\n",
              "num         1.000000\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3zD8eOYUlUT",
        "outputId": "bd647db3-9418-4308-901c-53e356da3198"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    float64\n",
            " 12  thal      303 non-null    float64\n",
            " 13  num       303 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# eliminate columns with correlation less than abs(0.2)\n",
        "drop_columns = ['fbs', 'chol', 'trestbps', 'restecg']\n",
        "cleveland_downsized = cleveland.drop(columns = drop_columns)\n",
        "cleveland_downsized.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBs1kOL3fPT5",
        "outputId": "51b807cc-61af-4cf9-e90c-ca13746b29e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 10 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      303 non-null    float64\n",
            " 1   sex      303 non-null    float64\n",
            " 2   cp       303 non-null    float64\n",
            " 3   thalach  303 non-null    float64\n",
            " 4   exang    303 non-null    float64\n",
            " 5   oldpeak  303 non-null    float64\n",
            " 6   slope    303 non-null    float64\n",
            " 7   ca       303 non-null    float64\n",
            " 8   thal     303 non-null    float64\n",
            " 9   num      303 non-null    float64\n",
            "dtypes: float64(10)\n",
            "memory usage: 23.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### STRATIFIED SPLIT OF DATA"
      ],
      "metadata": {
        "id": "8GCT56sygyQ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_downsized_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in cleveland_downsized_strat.split(cleveland_downsized, cleveland_downsized[\"num\"]):\n",
        "  cleveland_downsized_strat_train = cleveland_downsized.loc[train_index]\n",
        "  cleveland_downsized_strat_test = cleveland_downsized.loc[test_index]"
      ],
      "metadata": {
        "id": "BBiM92vlhAyY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_downsized_strat_test[\"num\"].value_counts()/len(cleveland_downsized_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBurG9WuhBje",
        "outputId": "b3a1daa2-7db4-4d0b-9289-c5edc42149d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.540984\n",
              "1.0    0.459016\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_downsized_strat_train[\"num\"].value_counts()/len(cleveland_downsized_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_jiuKUzhDne",
        "outputId": "73c5f2db-689e-4d63-dce6-008dc6c83da2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.541322\n",
              "1.0    0.458678\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "cleveland_downsized_strat_train_X = cleveland_downsized_strat_train.drop([heart_label], axis=1)\n",
        "cleveland_downsized_strat_train_y = cleveland_downsized_strat_train[heart_label]\n",
        "cleveland_downsized_strat_test_X = cleveland_downsized_strat_test.drop([heart_label], axis=1)\n",
        "cleveland_downsized_strat_test_y = cleveland_downsized_strat_test[heart_label]\n",
        "cleveland_downsized_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p5NBNc4NhFjC",
        "outputId": "af668f02-10c1-4d89-89a8-387c5997609e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  thalach  exang  oldpeak  slope   ca  thal\n",
              "151  42.0  0.0  4.0    122.0    0.0      0.6    2.0  0.0   3.0\n",
              "58   54.0  1.0  3.0    152.0    0.0      0.5    3.0  1.0   3.0\n",
              "52   44.0  1.0  4.0    153.0    0.0      0.0    1.0  1.0   3.0\n",
              "59   51.0  1.0  1.0    125.0    1.0      1.4    1.0  1.0   3.0\n",
              "129  62.0  0.0  4.0    163.0    0.0      0.0    1.0  0.0   3.0"
            ],
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4357608a-7aa3-4193-808b-36aed1db631c 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-4357608a-7aa3-4193-808b-36aed1db631c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODELING"
      ],
      "metadata": {
        "id": "K43g2BJPhukY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clev_corr_clf = ak.StructuredDataClassifier(overwrite=True, seed=42) # instantiate classifier\n",
        "clev_corr_clf.fit(cleveland_downsized_strat_train_X, cleveland_downsized_strat_train_y) # fit model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFQ0hF3rhz24",
        "outputId": "aa443ba4-04ff-4f4f-f096-b61a58bfe871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 94 Complete [00h 00m 05s]\n",
            "val_accuracy: 0.7222222089767456\n",
            "\n",
            "Best val_accuracy So Far: 0.9444444179534912\n",
            "Total elapsed time: 00h 16m 22s\n",
            "Epoch 1/33\n",
            "8/8 [==============================] - 2s 7ms/step - loss: 4.1659 - accuracy: 0.5909\n",
            "Epoch 2/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 4.9297 - accuracy: 0.6322\n",
            "Epoch 3/33\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 5.3367 - accuracy: 0.5579\n",
            "Epoch 4/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.4798 - accuracy: 0.6777\n",
            "Epoch 5/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.7968 - accuracy: 0.5992\n",
            "Epoch 6/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 3.3147 - accuracy: 0.6116\n",
            "Epoch 7/33\n",
            "8/8 [==============================] - 0s 5ms/step - loss: 2.9192 - accuracy: 0.7066\n",
            "Epoch 8/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 1.6674 - accuracy: 0.6694\n",
            "Epoch 9/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 2.0432 - accuracy: 0.6818\n",
            "Epoch 10/33\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.7718 - accuracy: 0.7149\n",
            "Epoch 11/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.9915 - accuracy: 0.6157\n",
            "Epoch 12/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.7180 - accuracy: 0.7025\n",
            "Epoch 13/33\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1.7587 - accuracy: 0.6405\n",
            "Epoch 14/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.2460 - accuracy: 0.7355\n",
            "Epoch 15/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1444 - accuracy: 0.7314\n",
            "Epoch 16/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 1.1222 - accuracy: 0.7810\n",
            "Epoch 17/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 1.0189 - accuracy: 0.6736\n",
            "Epoch 18/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.8039 - accuracy: 0.6777\n",
            "Epoch 19/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.8493 - accuracy: 0.7562\n",
            "Epoch 20/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.7156 - accuracy: 0.7190\n",
            "Epoch 21/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.5640 - accuracy: 0.7562\n",
            "Epoch 22/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6299 - accuracy: 0.7603\n",
            "Epoch 23/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5724 - accuracy: 0.7562\n",
            "Epoch 24/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5172 - accuracy: 0.7686\n",
            "Epoch 25/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5935 - accuracy: 0.7562\n",
            "Epoch 26/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5560 - accuracy: 0.7769\n",
            "Epoch 27/33\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.4924 - accuracy: 0.7603\n",
            "Epoch 28/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5827 - accuracy: 0.7769\n",
            "Epoch 29/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.5273 - accuracy: 0.7769\n",
            "Epoch 30/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.5650 - accuracy: 0.7645\n",
            "Epoch 31/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6219 - accuracy: 0.7645\n",
            "Epoch 32/33\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.5815 - accuracy: 0.7355\n",
            "Epoch 33/33\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.5300 - accuracy: 0.7810\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbb29480040>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_downsized_strat_pred_y = clev_corr_clf.predict(cleveland_downsized_strat_test_X) # predict with best model\n",
        "print(clev_corr_clf.evaluate(cleveland_downsized_strat_test_X, cleveland_downsized_strat_test_y)) # evaluate best model with testing data\n",
        "# (accuracies w/ max_trials=3 & default epochs: 0.6066 (4m 7s), 0.8033 (13s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYJuPhLDh9eK",
        "outputId": "384b7646-7e82-43dc-dc84-720333a5a719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 6ms/step\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.4754 - accuracy: 0.5410\n",
            "[1.475428581237793, 0.5409836173057556]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "clev_corr_model = clev_corr_clf.export_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZjVbmFiA4-2",
        "outputId": "1d07683f-7137-4de2-da22-b84c63a15df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 9)]               0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 9)                0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " dense (Dense)               (None, 16)                160       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 16)               64        \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 16)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 16)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1024)              17408     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 1024)             4096      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 1024)              0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 1025      \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 22,753\n",
            "Trainable params: 20,673\n",
            "Non-trainable params: 2,080\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute F1 score\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(cleveland_downsized_strat_test_y, cleveland_downsized_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joB7-GYnuHK7",
        "outputId": "8fa35829-1a00-4fb2-cb27-31de52e8b949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6666666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HUNGARIAN"
      ],
      "metadata": {
        "id": "cXKkDabL_VgX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TAKE A PEEK"
      ],
      "metadata": {
        "id": "_fcIiSEyGKPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbLTG9pDE8Qg",
        "outputId": "dbed4184-9f83-41c4-ad2d-0e194a2f3a54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  40.0  1.0  2.0     140.0  289.0  0.0      0.0    172.0    0.0      0.0   \n",
              "1  49.0  0.0  3.0     160.0  180.0  0.0      0.0    156.0    0.0      1.0   \n",
              "2  37.0  1.0  2.0     130.0  283.0  0.0      1.0     98.0    0.0      0.0   \n",
              "3  48.0  0.0  4.0     138.0  214.0  0.0      0.0    108.0    1.0      1.5   \n",
              "4  54.0  1.0  3.0     150.0   -9.0  0.0      0.0    122.0    0.0      0.0   \n",
              "\n",
              "   slope   ca  thal  num  \n",
              "0   -9.0 -9.0  -9.0  0.0  \n",
              "1    2.0 -9.0  -9.0  1.0  \n",
              "2   -9.0 -9.0  -9.0  0.0  \n",
              "3    2.0 -9.0  -9.0  3.0  \n",
              "4   -9.0 -9.0  -9.0  0.0  "
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              "\n",
              "  <div id=\"df-9646c062-aae4-449b-bc4b-07f119608256\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>-9.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
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              "      <td>283.0</td>\n",
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              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>-9.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>-9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-9646c062-aae4-449b-bc4b-07f119608256');\n",
              "          const dataTable =\n",
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              "                                                     [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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LwDfIkNE-1u",
        "outputId": "07e2542a-d451-4af6-f641-0fd9eae216cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 295 entries, 0 to 294\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       294 non-null    float64\n",
            " 1   sex       294 non-null    float64\n",
            " 2   cp        294 non-null    float64\n",
            " 3   trestbps  294 non-null    float64\n",
            " 4   chol      294 non-null    float64\n",
            " 5   fbs       294 non-null    float64\n",
            " 6   restecg   294 non-null    float64\n",
            " 7   thalach   294 non-null    float64\n",
            " 8   exang     294 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  slope     294 non-null    float64\n",
            " 11  ca        294 non-null    float64\n",
            " 12  thal      294 non-null    float64\n",
            " 13  num       294 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 32.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SIMPLEST OF MODELS - BINARY\n",
        "(mininimal preprocessing)\n",
        "CURRENTLY STRUGGLING WITH AN ERROR HERE"
      ],
      "metadata": {
        "id": "29N3Hcl4BC3V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### PREPARE DATA"
      ],
      "metadata": {
        "id": "Y7LapSLrA4Yu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy of original dataframe\n",
        "hungarian_basic = hungarian_data.copy()"
      ],
      "metadata": {
        "id": "5CjIwYPdBpeP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert unknowns (-9.0) to NaN\n",
        "hungarian_basic.replace(-9.0, np.NaN, inplace=True)"
      ],
      "metadata": {
        "id": "VM4HRk0RBgHJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy for binary labeling\n",
        "hungarian_basic['num'][hungarian_basic['num'] > 0] = 1\n",
        "hungarian_basic.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "dffe9479-a8ab-4632-99bd-676044f8d3f0",
        "id": "QvO5Nu9CA4Yu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  40.0  1.0  2.0     140.0  289.0  0.0      0.0    172.0    0.0      0.0   \n",
              "1  49.0  0.0  3.0     160.0  180.0  0.0      0.0    156.0    0.0      1.0   \n",
              "2  37.0  1.0  2.0     130.0  283.0  0.0      1.0     98.0    0.0      0.0   \n",
              "3  48.0  0.0  4.0     138.0  214.0  0.0      0.0    108.0    1.0      1.5   \n",
              "4  54.0  1.0  3.0     150.0    NaN  0.0      0.0    122.0    0.0      0.0   \n",
              "\n",
              "   slope  ca  thal  num  \n",
              "0    NaN NaN   NaN  0.0  \n",
              "1    2.0 NaN   NaN  1.0  \n",
              "2    NaN NaN   NaN  0.0  \n",
              "3    2.0 NaN   NaN  1.0  \n",
              "4    NaN NaN   NaN  0.0  "
            ],
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              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>54.0</td>\n",
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              "      <td>3.0</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c94c3642-05c8-4137-8633-294dab1ccc46')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# try dropping slope, ca, and thal\n",
        "hungarian_basic.drop(['slope', 'ca', 'thal', ], axis=1, inplace=True) # did not fix error"
      ],
      "metadata": {
        "id": "2c0z-alQDz1L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split the binary data\n",
        "from sklearn.model_selection import train_test_split\n",
        "hungarian_basic_train, hungarian_basic_test = train_test_split(hungarian_basic, test_size=0.2, random_state=42)\n",
        "hungarian_basic_test_labels = hungarian_basic_test[heart_label]\n",
        "hungarian_basic_test_nolabel = hungarian_basic_test.drop([heart_label], axis=1)\n",
        "hungarian_basic_train_labels = hungarian_basic_train[heart_label]\n",
        "hungarian_basic_train_nolabel = hungarian_basic_train.drop([heart_label], axis=1)\n",
        "hungarian_basic_train.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6a832165-b6f9-4375-d77d-39e3aea77116",
        "id": "tkrxqPDnA4Yu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "16   38.0  1.0  4.0     110.0  196.0  0.0      0.0    166.0    0.0      0.0   \n",
              "114  49.0  0.0  3.0     130.0  207.0  0.0      1.0    135.0    0.0      0.0   \n",
              "203  42.0  1.0  3.0     160.0  147.0  0.0      0.0    146.0    0.0      0.0   \n",
              "26   53.0  1.0  4.0     124.0  260.0  0.0      1.0    112.0    1.0      3.0   \n",
              "210  54.0  1.0  4.0     125.0  216.0  0.0      0.0    140.0    0.0      0.0   \n",
              "\n",
              "     slope  ca  thal  num  \n",
              "16     NaN NaN   NaN  1.0  \n",
              "114    NaN NaN   NaN  0.0  \n",
              "203    NaN NaN   NaN  0.0  \n",
              "26     2.0 NaN   NaN  0.0  \n",
              "210    NaN NaN   NaN  1.0  "
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_basic_train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nE9JNoRtFeHB",
        "outputId": "cb499402-4d98-451e-c633-8035222eacbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97     0.0\n",
              "63     1.0\n",
              "17     0.0\n",
              "215    1.0\n",
              "219    0.0\n",
              "      ... \n",
              "188    1.0\n",
              "71     0.0\n",
              "106    0.0\n",
              "270    0.0\n",
              "102    1.0\n",
              "Name: num, Length: 236, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MODEL - TRAIN & EVALUATE"
      ],
      "metadata": {
        "id": "Cv_M7-AHA4Yu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = ak.StructuredDataClassifier(overwrite=True, seed=42) # Instantiate classifier\n",
        "clf.fit(hungarian_basic_train_nolabel, hungarian_basic_train_labels) # Fit model"
      ],
      "metadata": {
        "id": "A0jdfgRFA4Yu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict with best model\n",
        "hungarian_basic_pred_y = clf.predict(hungarian_basic_test_nolabel)\n",
        "# Evaluate best model with testing data\n",
        "print(clf.evaluate(hungarian_basic_test_nolabel, hungarian_basic_test_labels))"
      ],
      "metadata": {
        "id": "CNcobVqmA4Yv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### TABLES, PLOTS, AND SUCH"
      ],
      "metadata": {
        "id": "io5j2xeiA4Yv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "#from sklearn.metrics import classification_report, confusion_matrix\n",
        "#confusion_matrix(hungarian_basic_test_labels, hungarian_basic_pred_y)"
      ],
      "metadata": {
        "id": "QFpixF0_A4Yv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SLIGHTLY BETTER MODEL\n",
        "no missing values & stratified sampling"
      ],
      "metadata": {
        "id": "vWaCsKFd_ntB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "DnloIPWU_ntC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MISSING VALUES"
      ],
      "metadata": {
        "id": "CH0kmOfB_ntC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make copy of original dataframe\n",
        "hungarian = hungarian_data.copy()"
      ],
      "metadata": {
        "id": "jYye-mJS_ntD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert unknowns (-9.0) to NaN\n",
        "hungarian.replace(-9.0, np.NaN, inplace=True)"
      ],
      "metadata": {
        "id": "XCd-RWakA_hZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values\n",
        "hungarian.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31e53f46-c888-4f0a-8a1d-c09d55af29aa",
        "id": "B_Lx-Oah_ntD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 295 entries, 0 to 294\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       294 non-null    float64\n",
            " 1   sex       294 non-null    float64\n",
            " 2   cp        294 non-null    float64\n",
            " 3   trestbps  293 non-null    float64\n",
            " 4   chol      271 non-null    float64\n",
            " 5   fbs       286 non-null    float64\n",
            " 6   restecg   293 non-null    float64\n",
            " 7   thalach   293 non-null    float64\n",
            " 8   exang     293 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  slope     104 non-null    float64\n",
            " 11  ca        4 non-null      float64\n",
            " 12  thal      28 non-null     float64\n",
            " 13  num       294 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 32.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make dataset version for plotting purposes\n",
        "hungarian_for_plot = hungarian.copy()"
      ],
      "metadata": {
        "id": "zR6tAnPJcbZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# drop slope, ca, and thal due to >50% of values missing\n",
        "hungarian.drop(['slope', 'ca', 'thal'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "OV4sLrhuBvo6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian.tail() # check if next code block is needed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tK0fXnxF2JiM",
        "outputId": "7a1fdb03-0dda-4b6f-b419-11425d8cfc1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "290  36.0  1.0  2.0     120.0  166.0  0.0      0.0    180.0    0.0      0.0   \n",
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              "292  47.0  0.0  2.0     140.0  257.0  0.0      0.0    135.0    0.0      1.0   \n",
              "293  53.0  1.0  4.0     130.0  182.0  0.0      0.0    148.0    0.0      0.0   \n",
              "294   NaN  NaN  NaN       NaN    NaN  NaN      NaN      NaN    NaN      NaN   \n",
              "\n",
              "     num  \n",
              "290  0.0  \n",
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian.drop(294,axis=0, inplace=True) # may not be necessary later # something may have gone wrong earlier, requiring it now"
      ],
      "metadata": {
        "id": "1KtQ3slgDh26"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "# impute discrete values using mode ()\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "hungarian_disc = pd.concat([hungarian.pop(x) for x in ['fbs', 'restecg', 'exang']], axis=1) # isolate discrete\n",
        "imputeMode.fit(hungarian_disc) # fit - learns the data\n",
        "imputed_disc = imputeMode.transform(hungarian_disc) # transform - imputes with chosen strategy\n",
        "hungarian_disc_imp = pd.DataFrame(imputed_disc, columns=hungarian_disc.columns, index=hungarian.index) # back to pandas DataFrame\n",
        "hungarian_disc_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20b614b3-9483-428a-e439-74b0afbe7694",
        "id": "52u0wpti_ntE"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   fbs      294 non-null    float64\n",
            " 1   restecg  294 non-null    float64\n",
            " 2   exang    294 non-null    float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 7.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine cat, cont, and remaining\n",
        "from sklearn.impute import SimpleImputer\n",
        "# impute continuous values using mean\n",
        "imputeMean = SimpleImputer(strategy=\"mean\") # create mode imputer\n",
        "hungarian_cont = pd.concat([hungarian.pop(x) for x in ['trestbps', 'chol', 'thalach']], axis=1) # isolate continuous\n",
        "imputeMean.fit(hungarian_cont) # fit - learns the data\n",
        "imputed_cont = imputeMean.transform(hungarian_cont) # transform - imputes with chosen strategy\n",
        "hungarian_cont_imp = pd.DataFrame(imputed_cont, columns=hungarian_cont.columns, index=hungarian.index) # back to pandas DataFrame\n",
        "hungarian_cont_imp.info() # check for missing values"
      ],
      "metadata": {
        "id": "rOFszydmE04O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f218afe6-3d4d-4af6-aa97-39e31c03f331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  294 non-null    float64\n",
            " 1   chol      294 non-null    float64\n",
            " 2   thalach   294 non-null    float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 7.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp = pd.concat((hungarian_cont_imp, hungarian_disc_imp, hungarian), axis=1)"
      ],
      "metadata": {
        "id": "kLp-5ukt0pqr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp.head() # check for anything obviously wonky"
      ],
      "metadata": {
        "id": "mysrtNOJ_ntF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22095760-34a5-4c10-ded5-0ab9ab09a451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   trestbps        chol  thalach  fbs  restecg  exang   age  sex   cp  \\\n",
              "0     140.0  289.000000    172.0  0.0      0.0    0.0  40.0  1.0  2.0   \n",
              "1     160.0  180.000000    156.0  0.0      0.0    0.0  49.0  0.0  3.0   \n",
              "2     130.0  283.000000     98.0  0.0      1.0    0.0  37.0  1.0  2.0   \n",
              "3     138.0  214.000000    108.0  0.0      0.0    1.0  48.0  0.0  4.0   \n",
              "4     150.0  250.848708    122.0  0.0      0.0    0.0  54.0  1.0  3.0   \n",
              "\n",
              "   oldpeak  num  \n",
              "0      0.0  0.0  \n",
              "1      1.0  1.0  \n",
              "2      0.0  0.0  \n",
              "3      1.5  3.0  \n",
              "4      0.0  0.0  "
            ],
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              "      <td>49.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>130.0</td>\n",
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              "      <td>98.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>138.0</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>108.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>150.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>54.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIMPBTRd1EcM",
        "outputId": "8d71843f-61cf-49d5-da17-4da7a0d9ad52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  294 non-null    float64\n",
            " 1   chol      294 non-null    float64\n",
            " 2   thalach   294 non-null    float64\n",
            " 3   fbs       294 non-null    float64\n",
            " 4   restecg   294 non-null    float64\n",
            " 5   exang     294 non-null    float64\n",
            " 6   age       294 non-null    float64\n",
            " 7   sex       294 non-null    float64\n",
            " 8   cp        294 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  num       294 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 25.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### BINARY LABEL\n",
        "(multiclass for \"future research\")"
      ],
      "metadata": {
        "id": "A6ueN45I_ntF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp['num'][hungarian_imp['num'] > 0] = 1\n",
        "hungarian_imp.value_counts('num')"
      ],
      "metadata": {
        "id": "JuZZ40ah_ntF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a53c5eda-65f3-47ef-8f9b-0586a1c15f0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num\n",
              "0.0    188\n",
              "1.0    106\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### STRATIFIED SAMPLING\n",
        "\n",
        "(due to difference in sex representation)"
      ],
      "metadata": {
        "id": "msBJaWN4_ntF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in hungarian_imp_strat.split(hungarian_imp, hungarian_imp[\"num\"]):\n",
        "  hungarian_imp_strat_train = hungarian_imp.loc[train_index]\n",
        "  hungarian_imp_strat_test = hungarian_imp.loc[test_index]"
      ],
      "metadata": {
        "id": "fk7deLq3_ntG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_strat_test[\"num\"].value_counts()/len(hungarian_imp_strat_test)"
      ],
      "metadata": {
        "id": "stx-CP-8_ntG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdb7566c-e56f-4579-ab86-dac09d4f5a9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.644068\n",
              "1.0    0.355932\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_strat_train[\"num\"].value_counts()/len(hungarian_imp_strat_train)"
      ],
      "metadata": {
        "id": "gRkDY6zN_ntG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58c9bf81-cabf-4ce5-d30c-c10dca7af8de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.638298\n",
              "1.0    0.361702\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Less significant for Cleveland and Statlog datasets, which have less dramatic difference in sex representation in data, but different for other three datasets. "
      ],
      "metadata": {
        "id": "MP2RyHiN_ntH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "hungarian_imp_strat_train_X = hungarian_imp_strat_train.drop([heart_label], axis=1)\n",
        "hungarian_imp_strat_train_y = hungarian_imp_strat_train[heart_label]\n",
        "hungarian_imp_strat_test_X = hungarian_imp_strat_test.drop([heart_label], axis=1)\n",
        "hungarian_imp_strat_test_y = hungarian_imp_strat_test[heart_label]\n",
        "hungarian_imp_strat_train_X.sample(5)"
      ],
      "metadata": {
        "id": "Y7upksz2_ntH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d05a83fd-f821-45a2-f03e-1dbc11e2e710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     trestbps        chol  thalach  fbs  restecg  exang   age  sex   cp  \\\n",
              "150     130.0  250.848708    120.0  0.0      0.0    0.0  39.0  1.0  2.0   \n",
              "138     140.0  250.848708    118.0  0.0      0.0    1.0  54.0  1.0  4.0   \n",
              "228      92.0  117.000000    134.0  0.0      0.0    1.0  38.0  1.0  4.0   \n",
              "101     130.0  179.000000    100.0  0.0      0.0    0.0  51.0  1.0  4.0   \n",
              "61      150.0  250.848708    175.0  0.0      0.0    0.0  43.0  0.0  3.0   \n",
              "\n",
              "     oldpeak  \n",
              "150      0.0  \n",
              "138      0.0  \n",
              "228      2.5  \n",
              "101      0.0  \n",
              "61       0.0  "
            ],
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              "      <td>179.000000</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>150.0</td>\n",
              "      <td>250.848708</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODEL"
      ],
      "metadata": {
        "id": "HiABARSi_ntH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hung_clf = ak.StructuredDataClassifier(overwrite=True, seed = 42) # instantiate classifier\n",
        "hung_clf.fit(hungarian_imp_strat_train_X, hungarian_imp_strat_train_y) # fit model"
      ],
      "metadata": {
        "id": "uXDRfgxh_ntI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76443e53-10e9-4a50-cda1-b292ef10794e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 100 Complete [00h 00m 06s]\n",
            "val_accuracy: 0.7272727489471436\n",
            "\n",
            "Best val_accuracy So Far: 0.9090909361839294\n",
            "Total elapsed time: 00h 14m 47s\n",
            "8/8 [==============================] - 1s 5ms/step - loss: 0.6467 - accuracy: 0.6213\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbb288c8eb0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_strat_pred_y = hung_clf.predict(hungarian_imp_strat_test_X) # predict with best model\n",
        "print(hung_clf.evaluate(hungarian_imp_strat_test_X, hungarian_imp_strat_test_y)) # evaluate best model with testing data\n",
        "# default settings 03/27 run1 - accuracy 0.8644 and run time 7m 36s\n",
        "# default settings 03/27 run2 - accuracy 0.7966 and run time 10m 29s\n",
        "# default settings 03/27 run3 - accuracy 0.6610 and run time 52m 27s"
      ],
      "metadata": {
        "id": "S4xB534V_ntI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "683198cd-4edd-4b50-ab4e-49e2880b0adc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 7ms/step\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.5326 - accuracy: 0.8305\n",
            "[0.5326333045959473, 0.8305084705352783]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "model = hung_clf.export_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "5ZxyAKed_ntI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffac9d15-522d-483a-f7f3-b5b7f4c2ea4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 10)]              0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 10)               0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " normalization (Normalizatio  (None, 10)               21        \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                352       \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,462\n",
            "Trainable params: 1,441\n",
            "Non-trainable params: 21\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute F1 score\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(hungarian_imp_strat_test_y, hungarian_imp_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxZl5bbf0aae",
        "outputId": "c41d16cd-f20a-4069-992a-d9a2afc0856b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7058823529411765"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## REDUCED FOR CORRELATION\n",
        "(incl. no missing values & stratified sampling)"
      ],
      "metadata": {
        "id": "uMB-u3bu5ESZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "ZZUvoaR05dz2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### REMOVE UNCORRELATED ATTRIBUTES"
      ],
      "metadata": {
        "id": "skep2iuD5dz2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check correlation\n",
        "hungarian_imp_correlation = hungarian_imp.corr()\n",
        "hungarian_imp_correlation['num'].sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0658f52-0d50-4af9-cb55-11836d2373f8",
        "id": "v2DKKQJH5dz2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thalach    -0.331074\n",
              "restecg    -0.031988\n",
              "trestbps    0.139582\n",
              "age         0.159315\n",
              "fbs         0.162869\n",
              "chol        0.202372\n",
              "sex         0.272781\n",
              "cp          0.505864\n",
              "oldpeak     0.545700\n",
              "exang       0.584541\n",
              "num         1.000000\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMkNdBwHUQ3b",
        "outputId": "c600b0b7-9ca0-46c0-dcd4-1e4c0e3d954b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  294 non-null    float64\n",
            " 1   chol      294 non-null    float64\n",
            " 2   thalach   294 non-null    float64\n",
            " 3   fbs       294 non-null    float64\n",
            " 4   restecg   294 non-null    float64\n",
            " 5   exang     294 non-null    float64\n",
            " 6   age       294 non-null    float64\n",
            " 7   sex       294 non-null    float64\n",
            " 8   cp        294 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  num       294 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 25.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# eliminate columns with correlation less than abs(0.2)\n",
        "drop_columns = ['age', 'fbs', 'trestbps', 'restecg']\n",
        "hungarian_imp_downsized = hungarian_imp.drop(columns = drop_columns)\n",
        "hungarian_imp_downsized.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "572a732b-5825-4f6e-f9a1-8607e31750a7",
        "id": "j4No27wG5dz2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 7 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   chol     294 non-null    float64\n",
            " 1   thalach  294 non-null    float64\n",
            " 2   exang    294 non-null    float64\n",
            " 3   sex      294 non-null    float64\n",
            " 4   cp       294 non-null    float64\n",
            " 5   oldpeak  294 non-null    float64\n",
            " 6   num      294 non-null    float64\n",
            "dtypes: float64(7)\n",
            "memory usage: 16.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### STRATIFIED SPLIT OF DATA"
      ],
      "metadata": {
        "id": "b3aarORD5dz3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_downsized_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in hungarian_imp_downsized_strat.split(hungarian_imp_downsized, hungarian_imp_downsized[\"num\"]):\n",
        "  hungarian_imp_downsized_strat_train = hungarian_imp_downsized.loc[train_index]\n",
        "  hungarian_imp_downsized_strat_test = hungarian_imp_downsized.loc[test_index]"
      ],
      "metadata": {
        "id": "OrLPdzz25dz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_downsized_strat_test[\"num\"].value_counts()/len(hungarian_imp_downsized_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c435ba79-8b5d-4230-f482-3b4263eff362",
        "id": "PBGxMAWt5dz3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.644068\n",
              "1.0    0.355932\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_downsized_strat_train[\"num\"].value_counts()/len(hungarian_imp_downsized_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24c45f99-800f-4b84-c372-59e1029bd4e6",
        "id": "waL4iQ2m5dz3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.638298\n",
              "1.0    0.361702\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "hungarian_imp_downsized_strat_train_X = hungarian_imp_downsized_strat_train.drop([heart_label], axis=1)\n",
        "hungarian_imp_downsized_strat_train_y = hungarian_imp_downsized_strat_train[heart_label]\n",
        "hungarian_imp_downsized_strat_test_X = hungarian_imp_downsized_strat_test.drop([heart_label], axis=1)\n",
        "hungarian_imp_downsized_strat_test_y = hungarian_imp_downsized_strat_test[heart_label]\n",
        "hungarian_imp_downsized_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d622598c-d290-4ae4-b882-c9e72c7aff64",
        "id": "KB4jrbwu5dz3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      chol  thalach  exang  sex   cp  oldpeak\n",
              "5    339.0    170.0    0.0  1.0  3.0      0.0\n",
              "94   182.0    180.0    0.0  0.0  3.0      0.0\n",
              "111  255.0     92.0    1.0  1.0  4.0      3.0\n",
              "34   186.0    154.0    0.0  0.0  2.0      0.0\n",
              "78   100.0    138.0    1.0  1.0  2.0      0.0"
            ],
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              "\n",
              "  <div id=\"df-abbd2983-edf1-450f-95b1-fe1f8a8100af\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>oldpeak</th>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>339.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>182.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>255.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>186.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>100.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abbd2983-edf1-450f-95b1-fe1f8a8100af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-abbd2983-edf1-450f-95b1-fe1f8a8100af 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-abbd2983-edf1-450f-95b1-fe1f8a8100af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODELING"
      ],
      "metadata": {
        "id": "KuMr2lIi5dz3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hung_corr_clf = ak.StructuredDataClassifier(overwrite=True, seed=42) # instantiate classifier\n",
        "hung_corr_clf.fit(hungarian_imp_downsized_strat_train_X, hungarian_imp_downsized_strat_train_y) # fit model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9645f57-3630-4a07-fe42-b040776597bd",
        "id": "wUafVnnB5dz4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 61 Complete [00h 00m 06s]\n",
            "val_accuracy: 0.9090909361839294\n",
            "\n",
            "Best val_accuracy So Far: 0.9090909361839294\n",
            "Total elapsed time: 00h 08m 05s\n",
            "Epoch 1/6\n",
            "8/8 [==============================] - 1s 8ms/step - loss: 0.7066 - accuracy: 0.5234\n",
            "Epoch 2/6\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.7232 - accuracy: 0.4851\n",
            "Epoch 3/6\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 0.6712 - accuracy: 0.6213\n",
            "Epoch 4/6\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 0.6382 - accuracy: 0.6255\n",
            "Epoch 5/6\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6099 - accuracy: 0.7021\n",
            "Epoch 6/6\n",
            "8/8 [==============================] - 0s 7ms/step - loss: 0.6146 - accuracy: 0.7277\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbb28603e50>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_imp_downsized_strat_pred_y = hung_corr_clf.predict(hungarian_imp_downsized_strat_test_X) # predict with best model\n",
        "print(hung_corr_clf.evaluate(hungarian_imp_downsized_strat_test_X, hungarian_imp_downsized_strat_test_y)) # evaluate best model with testing data\n",
        "# default settings 03/27 run - accuracy 0.8136 and run time 5m 34s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1ff29a1-8685-4b2e-eae9-95d57e8aa2f7",
        "id": "HDd8ySBb5dz4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 5ms/step\n",
            "2/2 [==============================] - 0s 13ms/step - loss: 0.5295 - accuracy: 0.8305\n",
            "[0.5294677019119263, 0.8305084705352783]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "model = hung_corr_clf.export_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgKD-ibo_GmE",
        "outputId": "d3203b0d-9526-4539-f136-3b638f0edd50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 6)]               0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 6)                0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " normalization (Normalizatio  (None, 6)                13        \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                224       \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 32)                0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,326\n",
            "Trainable params: 1,313\n",
            "Non-trainable params: 13\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute F1 score\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(hungarian_imp_downsized_strat_test_y, hungarian_imp_downsized_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Qn4b4cGE0f9",
        "outputId": "c2482baa-7f1d-453b-9d9e-753895d8c55d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7058823529411765"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### OTHER"
      ],
      "metadata": {
        "id": "m2Dtwjvjv4JV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# COMBINED"
      ],
      "metadata": {
        "id": "RvY5ACg7k4gW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TAKE A PEEK"
      ],
      "metadata": {
        "id": "CWdUpfrTljml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtFcDGHOk4Qc",
        "outputId": "b11775e8-cb97-4822-842b-2a6148227993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp trestbps  chol fbs restecg thalach exang oldpeak slope ca  \\\n",
              "0   32    1   1       95     0   ?       0     127     0      .7     1  ?   \n",
              "1   34    1   4      115     0   ?       ?     154     0      .2     1  ?   \n",
              "2   35    1   4        ?     0   ?       0     130     1       ?     ?  ?   \n",
              "3   36    1   4      110     0   ?       0     125     1       1     2  ?   \n",
              "4   38    0   4      105     0   ?       0     166     0     2.8     1  ?   \n",
              "\n",
              "  thal  num  \n",
              "0    ?    1  \n",
              "1    ?    1  \n",
              "2    7    3  \n",
              "3    6    1  \n",
              "4    ?    2  "
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              "\n",
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              "<style scoped>\n",
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              "      <th>slope</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6e014580-7146-4a10-bb58-f5a60dbcda86');\n",
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              "            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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "va_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvuGZeWSk2su",
        "outputId": "c3602bc8-38eb-42e1-82bf-5210dc5933ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp trestbps chol fbs  restecg thalach exang oldpeak slope ca  \\\n",
              "0   63    1   4      140  260   0        1     112     1       3     2  ?   \n",
              "1   44    1   4      130  209   0        1     127     0       0     ?  ?   \n",
              "2   60    1   4      132  218   0        1     140     1     1.5     3  ?   \n",
              "3   55    1   4      142  228   0        1     149     1     2.5     1  ?   \n",
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          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBcJRHENlr8c",
        "outputId": "c4a37340-0acf-4977-9722-c2754eb9c2e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       123 non-null    int64 \n",
            " 1   sex       123 non-null    int64 \n",
            " 2   cp        123 non-null    int64 \n",
            " 3   trestbps  123 non-null    object\n",
            " 4   chol      123 non-null    int64 \n",
            " 5   fbs       123 non-null    object\n",
            " 6   restecg   123 non-null    object\n",
            " 7   thalach   123 non-null    object\n",
            " 8   exang     123 non-null    object\n",
            " 9   oldpeak   123 non-null    object\n",
            " 10  slope     123 non-null    object\n",
            " 11  ca        123 non-null    object\n",
            " 12  thal      123 non-null    object\n",
            " 13  num       123 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 13.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "va_data.info()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "eHadzZhYlrvg",
        "outputId": "de6c30ad-9a08-408f-c1bd-f553e0d96f70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       200 non-null    int64 \n",
            " 1   sex       200 non-null    int64 \n",
            " 2   cp        200 non-null    int64 \n",
            " 3   trestbps  200 non-null    object\n",
            " 4   chol      200 non-null    object\n",
            " 5   fbs       200 non-null    object\n",
            " 6   restecg   200 non-null    int64 \n",
            " 7   thalach   200 non-null    object\n",
            " 8   exang     200 non-null    object\n",
            " 9   oldpeak   200 non-null    object\n",
            " 10  slope     200 non-null    object\n",
            " 11  ca        200 non-null    object\n",
            " 12  thal      200 non-null    object\n",
            " 13  num       200 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 22.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "6J1AHWr3mBrq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### COPY DATASETS"
      ],
      "metadata": {
        "id": "-ZNo67KKmKtU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland = switzerland_data.copy()\n",
        "va = va_data.copy()"
      ],
      "metadata": {
        "id": "wvZLcIsYmMar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH8mKKBpQGf2",
        "outputId": "343fde00-4054-41de-c52d-9c7f73c9eb1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       123 non-null    int64 \n",
            " 1   sex       123 non-null    int64 \n",
            " 2   cp        123 non-null    int64 \n",
            " 3   trestbps  123 non-null    object\n",
            " 4   chol      123 non-null    int64 \n",
            " 5   fbs       123 non-null    object\n",
            " 6   restecg   123 non-null    object\n",
            " 7   thalach   123 non-null    object\n",
            " 8   exang     123 non-null    object\n",
            " 9   oldpeak   123 non-null    object\n",
            " 10  slope     123 non-null    object\n",
            " 11  ca        123 non-null    object\n",
            " 12  thal      123 non-null    object\n",
            " 13  num       123 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 13.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland.restecg.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbvd-stNPd74",
        "outputId": "5185e128-f2c0-4922-f4b5-310428b39eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    85\n",
              "1    30\n",
              "2     7\n",
              "?     1\n",
              "Name: restecg, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MISSING VALUES"
      ],
      "metadata": {
        "id": "FzATfjBhmDib"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### CONVERT ? TO NAN"
      ],
      "metadata": {
        "id": "KQddFPX1tmKV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland = switzerland.replace('?', np.NaN) # replace '?' values with NaN so can impute"
      ],
      "metadata": {
        "id": "QNhget-zP_zl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland[['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'restecg', 'exang']].astype(float) # convert dtype\n",
        "switzerland.tail() # to verify didn't mess up dataframe IDs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JO6Gy720q5y-",
        "outputId": "732feb04-373c-4dc5-d4e7-46a27e3408b0"
      },
      "execution_count": null,
      "outputs": [
        {
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              "     age  sex  cp trestbps  chol  fbs restecg thalach exang oldpeak slope  \\\n",
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              "\n",
              "      ca thal  num  \n",
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              "119  NaN    7    3  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make dataset version for plotting purposes\n",
        "switzerland_for_plot = switzerland.copy()"
      ],
      "metadata": {
        "id": "gGzmv9Oebw00"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "va = va.replace('?', np.NaN) # replace '?' values with NaN so can impute"
      ],
      "metadata": {
        "id": "NcMgmPX0R2Kk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "va[['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'restecg', 'exang']].astype(float) # convert dtype\n",
        "va.tail() # to verify didn't mess up dataframe IDs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxbuESQGmFxR",
        "outputId": "f48bfa12-3195-4cb7-81df-e3bdf1e35501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  cp trestbps chol fbs  restecg thalach exang oldpeak slope   ca  \\\n",
              "195   54    0   4      127  333   1        1     154     0       0   NaN  NaN   \n",
              "196   62    1   1      NaN  139   0        1     NaN   NaN     NaN   NaN  NaN   \n",
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              "199   62    1   2      120  254   0        2      93     1       0   NaN  NaN   \n",
              "\n",
              "    thal  num  \n",
              "195  NaN    1  \n",
              "196  NaN    0  \n",
              "197    6    2  \n",
              "198  NaN    0  \n",
              "199  NaN    1  "
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              "\n",
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              "          const element = document.querySelector('#df-f57e9acd-9188-4b6b-b27a-021c0534284c');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make dataset version for plotting purposes\n",
        "va_for_plot = va.copy()"
      ],
      "metadata": {
        "id": "tKnKSv8xb93z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### DROP IF MISSING >50%"
      ],
      "metadata": {
        "id": "GA__-F5ctvt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ce6g6hl7rX4G",
        "outputId": "65eebc20-debf-4286-91ba-64375dffe529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       123 non-null    int64 \n",
            " 1   sex       123 non-null    int64 \n",
            " 2   cp        123 non-null    int64 \n",
            " 3   trestbps  121 non-null    object\n",
            " 4   chol      123 non-null    int64 \n",
            " 5   fbs       48 non-null     object\n",
            " 6   restecg   122 non-null    object\n",
            " 7   thalach   122 non-null    object\n",
            " 8   exang     122 non-null    object\n",
            " 9   oldpeak   117 non-null    object\n",
            " 10  slope     106 non-null    object\n",
            " 11  ca        5 non-null      object\n",
            " 12  thal      71 non-null     object\n",
            " 13  num       123 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 13.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "va.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G36T7QwOrXvz",
        "outputId": "7abca18f-aab7-4380-d449-de128c0ddac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       200 non-null    int64 \n",
            " 1   sex       200 non-null    int64 \n",
            " 2   cp        200 non-null    int64 \n",
            " 3   trestbps  144 non-null    object\n",
            " 4   chol      193 non-null    object\n",
            " 5   fbs       193 non-null    object\n",
            " 6   restecg   200 non-null    int64 \n",
            " 7   thalach   147 non-null    object\n",
            " 8   exang     147 non-null    object\n",
            " 9   oldpeak   144 non-null    object\n",
            " 10  slope     98 non-null     object\n",
            " 11  ca        2 non-null      object\n",
            " 12  thal      34 non-null     object\n",
            " 13  num       200 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 22.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Switzerland - drop fbs, ca, and thal due to >50% of values missing\n",
        "# VA - drop slope, ca, and thal due to >50% of values missing\n",
        "# as combined dataset - drop fbs, slope, ca, and thal\n",
        "switzerland_mod = switzerland.copy()\n",
        "switzerland_mod.drop(['fbs', 'slope', 'ca', 'thal'], axis=1, inplace=True)\n",
        "va_mod = va.copy()\n",
        "va_mod.drop(['fbs', 'slope', 'ca', 'thal'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "Op-HCWEArP16"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### IMPUTE TO MODE OR MEAN"
      ],
      "metadata": {
        "id": "ucwbcf4dty-v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### SWITZERLAND"
      ],
      "metadata": {
        "id": "QJZ_-fnOvzJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland_mod.info()\n",
        "# to impute -\n",
        "#    to mode: restecg, exang\n",
        "#    to mean: trestbps, thalach, oldpeak"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNIgnHCwuTm8",
        "outputId": "d4aadca2-2e48-4123-9c07-0815b0cd349a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       123 non-null    int64 \n",
            " 1   sex       123 non-null    int64 \n",
            " 2   cp        123 non-null    int64 \n",
            " 3   trestbps  121 non-null    object\n",
            " 4   chol      123 non-null    int64 \n",
            " 5   restecg   122 non-null    object\n",
            " 6   thalach   122 non-null    object\n",
            " 7   exang     122 non-null    object\n",
            " 8   oldpeak   117 non-null    object\n",
            " 9   num       123 non-null    int64 \n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 9.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# impute discrete values using mode ()\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "switzerland_mod_disc = pd.concat([switzerland_mod.pop(x) for x in ['restecg', 'exang']], axis=1) # isolate discrete\n",
        "imputeMode.fit(switzerland_mod_disc) # fit - learns the data\n",
        "imputed_disc = imputeMode.transform(switzerland_mod_disc) # transform - imputes with chosen strategy\n",
        "switzerland_mod_disc_imp = pd.DataFrame(imputed_disc, columns=switzerland_mod_disc.columns, index=switzerland_mod.index) # back to pandas DataFrame\n",
        "switzerland_mod_disc_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3446c44a-2b6f-4a35-f031-c755a4bcacb3",
        "id": "UtNkfpnwtb5M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   restecg  123 non-null    object\n",
            " 1   exang    123 non-null    object\n",
            "dtypes: object(2)\n",
            "memory usage: 2.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# impute continuous values using mean\n",
        "imputeMean = SimpleImputer(strategy=\"mean\") # create mode imputer\n",
        "switzerland_mod_cont = pd.concat([switzerland_mod.pop(x) for x in ['trestbps', 'oldpeak', 'thalach']], axis=1) # isolate continuous\n",
        "imputeMean.fit(switzerland_mod_cont) # fit - learns the data\n",
        "imputed_cont = imputeMean.transform(switzerland_mod_cont) # transform - imputes with chosen strategy\n",
        "switzerland_mod_cont_imp = pd.DataFrame(imputed_cont, columns=switzerland_mod_cont.columns, index=switzerland_mod.index) # back to pandas DataFrame\n",
        "switzerland_mod_cont_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4216ff0b-c4cb-4009-a279-3cbd679a726f",
        "id": "OxkXBwIVtb5N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  123 non-null    float64\n",
            " 1   oldpeak   123 non-null    float64\n",
            " 2   thalach   123 non-null    float64\n",
            "dtypes: float64(3)\n",
            "memory usage: 3.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine cat, cont, and remaining\n",
        "switzerland_mod_imp = pd.concat((switzerland_mod_cont_imp, switzerland_mod_disc_imp, switzerland_mod), axis=1)"
      ],
      "metadata": {
        "id": "iNpYGXyftb5O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland_mod_imp.head() # check for anything obviously wonky"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2bd6c21-e736-4bd4-bfe1-320321082396",
        "id": "AVdEkhxatb5O"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     trestbps   oldpeak  thalach restecg exang  age  sex  cp  chol  num\n",
              "0   95.000000  0.700000    127.0       0     0   32    1   1     0    1\n",
              "1  115.000000  0.200000    154.0       0     0   34    1   4     0    1\n",
              "2  130.206612  0.653846    130.0       0     1   35    1   4     0    3\n",
              "3  110.000000  1.000000    125.0       0     1   36    1   4     0    1\n",
              "4  105.000000  2.800000    166.0       0     0   38    0   4     0    2"
            ],
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              "      <td>110.000000</td>\n",
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              "      <th>4</th>\n",
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              "      <td>166.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>38</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 81
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "switzerland_mod_imp.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8bb615f-a230-4a35-cfc3-6a86a03d6289",
        "id": "pDLGrb_Ntb5P"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 123 entries, 0 to 122\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  123 non-null    float64\n",
            " 1   oldpeak   123 non-null    float64\n",
            " 2   thalach   123 non-null    float64\n",
            " 3   restecg   123 non-null    object \n",
            " 4   exang     123 non-null    object \n",
            " 5   age       123 non-null    int64  \n",
            " 6   sex       123 non-null    int64  \n",
            " 7   cp        123 non-null    int64  \n",
            " 8   chol      123 non-null    int64  \n",
            " 9   num       123 non-null    int64  \n",
            "dtypes: float64(3), int64(5), object(2)\n",
            "memory usage: 9.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### VA"
      ],
      "metadata": {
        "id": "YaSxTsokwoem"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "va_mod.info()\n",
        "# to impute -\n",
        "#    to mode: exang\n",
        "#    to mean: trestbps, thalach, oldpeak, chol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WurdhVGWvOGQ",
        "outputId": "e504daf0-78f4-463f-dd73-b6fe4e8d94a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       200 non-null    int64 \n",
            " 1   sex       200 non-null    int64 \n",
            " 2   cp        200 non-null    int64 \n",
            " 3   trestbps  144 non-null    object\n",
            " 4   chol      193 non-null    object\n",
            " 5   restecg   200 non-null    int64 \n",
            " 6   thalach   147 non-null    object\n",
            " 7   exang     147 non-null    object\n",
            " 8   oldpeak   144 non-null    object\n",
            " 9   num       200 non-null    int64 \n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 15.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# impute discrete values using mode ()\n",
        "imputeMode = SimpleImputer(strategy=\"most_frequent\") # create mode imputer\n",
        "va_mod_disc = pd.concat([va_mod.pop(x) for x in ['exang']], axis=1) # isolate discrete\n",
        "imputeMode.fit(va_mod_disc) # fit - learns the data\n",
        "imputed_disc = imputeMode.transform(va_mod_disc) # transform - imputes with chosen strategy\n",
        "va_mod_disc_imp = pd.DataFrame(imputed_disc, columns=va_mod_disc.columns, index=va_mod.index) # back to pandas DataFrame\n",
        "va_mod_disc_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bdaef88-af8e-4a3d-ea48-e6ee7fb4aa7e",
        "id": "jM5tCXjbws2Y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   exang   200 non-null    object\n",
            "dtypes: object(1)\n",
            "memory usage: 1.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# impute continuous values using mean\n",
        "imputeMean = SimpleImputer(strategy=\"mean\") # create mode imputer\n",
        "va_mod_cont = pd.concat([va_mod.pop(x) for x in ['trestbps', 'chol', 'oldpeak', 'thalach']], axis=1) # isolate continuous\n",
        "imputeMean.fit(va_mod_cont) # fit - learns the data\n",
        "imputed_cont = imputeMean.transform(va_mod_cont) # transform - imputes with chosen strategy\n",
        "va_mod_cont_imp = pd.DataFrame(imputed_cont, columns=va_mod_cont.columns, index=va_mod.index) # back to pandas DataFrame\n",
        "va_mod_cont_imp.info() # check for missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f54a632-c2b3-4c96-8152-9cd0dc50e2a6",
        "id": "mpb8TeMuws2Z"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  200 non-null    float64\n",
            " 1   chol      200 non-null    float64\n",
            " 2   oldpeak   200 non-null    float64\n",
            " 3   thalach   200 non-null    float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 6.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine cat, cont, and remaining\n",
        "va_mod_imp = pd.concat((va_mod_cont_imp, va_mod_disc_imp, va_mod), axis=1)"
      ],
      "metadata": {
        "id": "HnR5DUZlws2a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "va_mod_imp.head() # check for anything obviously wonky"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea166a3e-8a26-4dcf-f5d4-1f78039432ed",
        "id": "GwsxE1ALws2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   trestbps   chol  oldpeak  thalach exang  age  sex  cp  restecg  num\n",
              "0     140.0  260.0      3.0    112.0     1   63    1   4        1    2\n",
              "1     130.0  209.0      0.0    127.0     0   44    1   4        1    0\n",
              "2     132.0  218.0      1.5    140.0     1   60    1   4        1    2\n",
              "3     142.0  228.0      2.5    149.0     1   55    1   4        1    1\n",
              "4     110.0  213.0      1.3     99.0     1   66    1   3        2    0"
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              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>228.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>149.0</td>\n",
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              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>110.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>99.0</td>\n",
              "      <td>1</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-242eca41-9bd0-4fd8-81ee-e77499d00b64')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-242eca41-9bd0-4fd8-81ee-e77499d00b64 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-242eca41-9bd0-4fd8-81ee-e77499d00b64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "va_mod_imp.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04b3431e-1ce0-4959-f1a1-23db4f32b085",
        "id": "gR967bGIws2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   trestbps  200 non-null    float64\n",
            " 1   chol      200 non-null    float64\n",
            " 2   oldpeak   200 non-null    float64\n",
            " 3   thalach   200 non-null    float64\n",
            " 4   exang     200 non-null    object \n",
            " 5   age       200 non-null    int64  \n",
            " 6   sex       200 non-null    int64  \n",
            " 7   cp        200 non-null    int64  \n",
            " 8   restecg   200 non-null    int64  \n",
            " 9   num       200 non-null    int64  \n",
            "dtypes: float64(4), int64(5), object(1)\n",
            "memory usage: 15.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY LABEL"
      ],
      "metadata": {
        "id": "zFlvS7sAmG-G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "switzerland_mod_imp['num'][switzerland_mod_imp['num'] > 0] = 1\n",
        "switzerland_mod_imp.value_counts('num')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bq2pz63PsUoJ",
        "outputId": "375a4ea8-35a4-4930-9e89-ab455122701b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-89-1df928d573d2>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  switzerland_mod_imp['num'][switzerland_mod_imp['num'] > 0] = 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num\n",
              "1    115\n",
              "0      8\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "va_mod_imp['num'][va_mod_imp['num'] > 0] = 1\n",
        "va_mod_imp.value_counts('num')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1O7WES5Fx5Zg",
        "outputId": "77ab779c-cd2f-4677-8a6a-8a91e726e138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-90-d8ef074f4d95>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  va_mod_imp['num'][va_mod_imp['num'] > 0] = 1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num\n",
              "1    149\n",
              "0     51\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CREATE COMBINED DATASET"
      ],
      "metadata": {
        "id": "qvO0KmFhzzsN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#cleveland.info()\n",
        "# for combined, drop - slope, ca, thal, fbs"
      ],
      "metadata": {
        "id": "sJcQug6kz2Qd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_comb = cleveland.copy()"
      ],
      "metadata": {
        "id": "NHcmira11rAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleveland_comb.drop(['fbs', 'slope', 'ca', 'thal'], axis=1, inplace=True)\n",
        "#cleveland_comb.info()"
      ],
      "metadata": {
        "id": "qxykvCYL1cf8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#hungarian_imp.info()\n",
        "# for combined, drop - fbs"
      ],
      "metadata": {
        "id": "7WCQ55__0gMY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_comb = hungarian_imp.copy()"
      ],
      "metadata": {
        "id": "4l3xx1zR1_-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hungarian_comb.drop(['fbs'], axis=1, inplace=True)\n",
        "#hungarian_comb.info()"
      ],
      "metadata": {
        "id": "ihnTPwRD2DoG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#switzerland_mod_imp.info()"
      ],
      "metadata": {
        "id": "kiUrP5ND0sZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#va_mod_imp.info()"
      ],
      "metadata": {
        "id": "V-GrTkNX0w_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined = pd.concat((cleveland_comb, hungarian_comb, switzerland_mod_imp, va_mod_imp), axis=0, ignore_index=True)\n",
        "combined.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ieAqO1Te2Zf2",
        "outputId": "dcb8393b-bb4c-420b-bbcb-bd3be1d1eb06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 920 entries, 0 to 919\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       920 non-null    float64\n",
            " 1   sex       920 non-null    float64\n",
            " 2   cp        920 non-null    float64\n",
            " 3   trestbps  920 non-null    float64\n",
            " 4   chol      920 non-null    float64\n",
            " 5   restecg   920 non-null    object \n",
            " 6   thalach   920 non-null    float64\n",
            " 7   exang     920 non-null    object \n",
            " 8   oldpeak   920 non-null    float64\n",
            " 9   num       920 non-null    float64\n",
            "dtypes: float64(8), object(2)\n",
            "memory usage: 72.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### STRATIFIED SAMPLING"
      ],
      "metadata": {
        "id": "7YJdwTn8zQrQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in combined_strat.split(combined, combined[\"num\"]):\n",
        "  combined_strat_train = combined.loc[train_index]\n",
        "  combined_strat_test = combined.loc[test_index]"
      ],
      "metadata": {
        "id": "DqJcC7zUzOTL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_strat_test[\"num\"].value_counts()/len(combined_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8768ccab-9956-4524-8a5c-ee675c7945db",
        "id": "OgCSkxMlzOTM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    0.554348\n",
              "0.0    0.445652\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_strat_train[\"num\"].value_counts()/len(combined_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d80beefc-8eb1-4d1f-a599-34a8f71e8f24",
        "id": "75ceUYhVzOTN"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    0.552989\n",
              "0.0    0.447011\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "combined_strat_train_X = combined_strat_train.drop([heart_label], axis=1)\n",
        "combined_strat_train_y = combined_strat_train[heart_label]\n",
        "combined_strat_test_X = combined_strat_test.drop([heart_label], axis=1)\n",
        "combined_strat_test_y = combined_strat_test[heart_label]\n",
        "combined_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "521cf419-48de-423b-f5b5-fcafc272e2b4",
        "id": "VEih5WP4zOTO"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp  trestbps        chol restecg  thalach exang  oldpeak\n",
              "299  68.0  1.0  4.0     144.0  193.000000     0.0    141.0   0.0      3.4\n",
              "328  36.0  1.0  3.0     130.0  209.000000     0.0    178.0   0.0      0.0\n",
              "393  49.0  0.0  2.0     110.0  250.848708     0.0    160.0   0.0      0.0\n",
              "381  52.0  1.0  2.0     140.0  100.000000     0.0    138.0   1.0      0.0\n",
              "885  48.0  1.0  4.0     140.0  178.746114       0    159.0     1      1.5"
            ],
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06cfe24d-e9c7-4a59-b982-de5b8e756f80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\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",
              "    }\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-06cfe24d-e9c7-4a59-b982-de5b8e756f80 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-06cfe24d-e9c7-4a59-b982-de5b8e756f80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BINARY MODEL"
      ],
      "metadata": {
        "id": "zQbb1Yj75YTt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_comb_bl = ak.StructuredDataClassifier(overwrite=True, seed = 42) # instantiate classifier\n",
        "clf_comb_bl.fit(combined_strat_train_X, combined_strat_train_y) # fit model"
      ],
      "metadata": {
        "id": "sYducbOF5Z4M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79c804ea-8718-4938-9eeb-a60653e30c21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 55 Complete [00h 00m 14s]\n",
            "val_accuracy: 0.828125\n",
            "\n",
            "Best val_accuracy So Far: 0.8828125\n",
            "Total elapsed time: 00h 14m 33s\n",
            "Epoch 1/27\n",
            "23/23 [==============================] - 2s 6ms/step - loss: 0.6127 - accuracy: 0.6671\n",
            "Epoch 2/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.4676 - accuracy: 0.7880\n",
            "Epoch 3/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4307 - accuracy: 0.8084\n",
            "Epoch 4/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4195 - accuracy: 0.8152\n",
            "Epoch 5/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.4091 - accuracy: 0.8152\n",
            "Epoch 6/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.3815 - accuracy: 0.8383\n",
            "Epoch 7/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.3603 - accuracy: 0.8478\n",
            "Epoch 8/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.3599 - accuracy: 0.8533\n",
            "Epoch 9/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.3552 - accuracy: 0.8451\n",
            "Epoch 10/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.3447 - accuracy: 0.8519\n",
            "Epoch 11/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.3277 - accuracy: 0.8696\n",
            "Epoch 12/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.3179 - accuracy: 0.8668\n",
            "Epoch 13/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.3185 - accuracy: 0.8804\n",
            "Epoch 14/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2943 - accuracy: 0.8927\n",
            "Epoch 15/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2927 - accuracy: 0.8777\n",
            "Epoch 16/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.2801 - accuracy: 0.8927\n",
            "Epoch 17/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2678 - accuracy: 0.8995\n",
            "Epoch 18/27\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.2625 - accuracy: 0.8886\n",
            "Epoch 19/27\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 0.2440 - accuracy: 0.8995\n",
            "Epoch 20/27\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.2355 - accuracy: 0.9022\n",
            "Epoch 21/27\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 0.2217 - accuracy: 0.9307\n",
            "Epoch 22/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2211 - accuracy: 0.9293\n",
            "Epoch 23/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2098 - accuracy: 0.9239\n",
            "Epoch 24/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.2054 - accuracy: 0.9321\n",
            "Epoch 25/27\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.1964 - accuracy: 0.9416\n",
            "Epoch 26/27\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 0.1922 - accuracy: 0.9266\n",
            "Epoch 27/27\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 0.1857 - accuracy: 0.9361\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbb1baba880>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_strat_pred_y = clf_comb_bl.predict(combined_strat_test_X) # predict with best model\n",
        "print(clf_comb_bl.evaluate(combined_strat_test_X, combined_strat_test_y)) # evaluate best model with testing data\n",
        "# default settings 04/02 run1 - accuracy 0.8478 and run time 18m 24s\n",
        "# default settings 04/09 run1 - accuracy 0.8207 and run time 15m 57s\n",
        "# default settings 04/09 run2 - accuracy 0.8478 and run time 12m 54s"
      ],
      "metadata": {
        "id": "qypNns2u5Z4N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66c4a300-aaae-4a93-bdac-f17e8c6e364c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 4ms/step\n",
            "6/6 [==============================] - 0s 4ms/step\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.4943 - accuracy: 0.8152\n",
            "[0.49432772397994995, 0.8152173757553101]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "model = clf_comb_bl.export_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "HjD3gwbJ5Z4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "638eb8c7-9842-45ca-f1dc-f1191a8f6209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 9)]               0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 9)                0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " normalization (Normalizatio  (None, 9)                19        \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               2560      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 256)              1024      \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                8224      \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 32)                0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 11,988\n",
            "Trainable params: 11,393\n",
            "Non-trainable params: 595\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute F1 score\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(combined_strat_test_y, combined_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k5-YifuDHiPm",
        "outputId": "bdcd73ba-4bfd-4f5d-a0fe-b00428e4baef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8365384615384616"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CORRELATED"
      ],
      "metadata": {
        "id": "yncUFRSs5qlq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ADDITIONAL DATA PREPROCESSING\n",
        "(starting after combined dataset created and before stratified sampling above)"
      ],
      "metadata": {
        "id": "g-6hPPPK51yj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "combined.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg8t_OwH5yPF",
        "outputId": "d28e55d2-cbf4-419a-e52c-da681e376508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 920 entries, 0 to 919\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       920 non-null    float64\n",
            " 1   sex       920 non-null    float64\n",
            " 2   cp        920 non-null    float64\n",
            " 3   trestbps  920 non-null    float64\n",
            " 4   chol      920 non-null    float64\n",
            " 5   restecg   920 non-null    object \n",
            " 6   thalach   920 non-null    float64\n",
            " 7   exang     920 non-null    object \n",
            " 8   oldpeak   920 non-null    float64\n",
            " 9   num       920 non-null    float64\n",
            "dtypes: float64(8), object(2)\n",
            "memory usage: 72.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined[['restecg', 'exang']] = combined[['restecg', 'exang']].astype(float) # convert dtype"
      ],
      "metadata": {
        "id": "lJJsGH6ZTvV6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check correlation\n",
        "combined_correlation = combined.corr()\n",
        "combined_correlation['num'].sort_values()\n",
        "# only trestbps has abs(corr) < 0.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bf70661-dc22-4519-ebd9-618b4b514b73",
        "id": "VhM9dfAQ6YUx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thalach    -0.385972\n",
              "chol       -0.234679\n",
              "restecg     0.062304\n",
              "trestbps    0.103828\n",
              "age         0.282700\n",
              "sex         0.307284\n",
              "oldpeak     0.373382\n",
              "exang       0.443433\n",
              "cp          0.471712\n",
              "num         1.000000\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# eliminate columns with correlation less than abs(0.2)\n",
        "drop_columns = ['trestbps']\n",
        "combined_downsized = combined.drop(columns = drop_columns)\n",
        "combined_downsized.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5842dd4-aa03-4e32-aefc-551deca5d1a5",
        "id": "8gtM9beL6YUy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 920 entries, 0 to 919\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   age      920 non-null    float64\n",
            " 1   sex      920 non-null    float64\n",
            " 2   cp       920 non-null    float64\n",
            " 3   chol     920 non-null    float64\n",
            " 4   restecg  920 non-null    float64\n",
            " 5   thalach  920 non-null    float64\n",
            " 6   exang    920 non-null    float64\n",
            " 7   oldpeak  920 non-null    float64\n",
            " 8   num      920 non-null    float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 64.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_downsized_strat = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in combined_downsized_strat.split(combined_downsized, combined[\"num\"]):\n",
        "  combined_downsized_strat_train = combined_downsized.loc[train_index]\n",
        "  combined_downsized_strat_test = combined_downsized.loc[test_index]"
      ],
      "metadata": {
        "id": "tq4rfZjk63jw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_downsized_strat_test[\"num\"].value_counts()/len(combined_downsized_strat_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fff2b68-78be-4b54-cb35-5ed506fd6113",
        "id": "BEZgns0j63jw"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    0.554348\n",
              "0.0    0.445652\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_downsized_strat_train[\"num\"].value_counts()/len(combined_downsized_strat_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f76f04d9-f4a8-4f5c-dff2-4846d8a05278",
        "id": "iFEAx_HY63jx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    0.552989\n",
              "0.0    0.447011\n",
              "Name: num, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split stratified data\n",
        "combined_downsized_strat_train_X = combined_downsized_strat_train.drop([heart_label], axis=1)\n",
        "combined_downsized_strat_train_y = combined_downsized_strat_train[heart_label]\n",
        "combined_downsized_strat_test_X = combined_downsized_strat_test.drop([heart_label], axis=1)\n",
        "combined_downsized_strat_test_y = combined_downsized_strat_test[heart_label]\n",
        "combined_downsized_strat_train_X.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8524881f-0fff-4a4b-9f59-48578df65518",
        "id": "HBw6ZduI63jx"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age  sex   cp        chol  restecg  thalach  exang  oldpeak\n",
              "299  68.0  1.0  4.0  193.000000      0.0    141.0    0.0      3.4\n",
              "683  61.0  1.0  4.0    0.000000      0.0    105.0    1.0      0.0\n",
              "393  49.0  0.0  2.0  250.848708      0.0    160.0    0.0      0.0\n",
              "381  52.0  1.0  2.0  100.000000      0.0    138.0    1.0      0.0\n",
              "885  48.0  1.0  4.0  178.746114      0.0    159.0    1.0      1.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75668db3-c2c1-48c3-8267-55c18661fd80\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>chol</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>61.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>250.848708</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <td>52.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>48.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>178.746114</td>\n",
              "      <td>0.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75668db3-c2c1-48c3-8267-55c18661fd80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-75668db3-c2c1-48c3-8267-55c18661fd80 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-75668db3-c2c1-48c3-8267-55c18661fd80');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BINARY MODEL - correlated"
      ],
      "metadata": {
        "id": "uKcwIQ2X7MfP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_combds_bl = ak.StructuredDataClassifier(overwrite=True, seed = 42) # instantiate classifier\n",
        "clf_combds_bl.fit(combined_downsized_strat_train_X, combined_downsized_strat_train_y) # fit model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkbYs3yG7UJW",
        "outputId": "effd3e63-1341-4c98-bef3-9fe90c27d136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 100 Complete [00h 00m 08s]\n",
            "val_accuracy: 0.84375\n",
            "\n",
            "Best val_accuracy So Far: 0.8515625\n",
            "Total elapsed time: 00h 19m 28s\n",
            "Epoch 1/2\n",
            "23/23 [==============================] - 1s 5ms/step - loss: 0.5499 - accuracy: 0.7554\n",
            "Epoch 2/2\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 0.4446 - accuracy: 0.8057\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbb1ba54940>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_downsized_strat_pred_y = clf_combds_bl.predict(combined_downsized_strat_test_X) # predict with best model\n",
        "print(clf_combds_bl.evaluate(combined_downsized_strat_test_X, combined_downsized_strat_test_y)) # evaluate best model with testing data\n",
        "# default settings 04/03 run1 - accuracy 0.7989 and run time 35m 51s\n",
        "# default settings 04/03 run2 - accuracy 0.8152 and run time 10m 41s\n",
        "# default settings 04/03 run3 - accuracy 0.7880 and run time 24m 02s\n",
        "# default settings 04/05 run1 - accuracy 0.8098 and run time 12m 35s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttZ-6bk27UJX",
        "outputId": "906a61de-439f-4672-e08c-abc14a7d37e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 3ms/step\n",
            "6/6 [==============================] - 0s 4ms/step\n",
            "6/6 [==============================] - 0s 6ms/step - loss: 0.4018 - accuracy: 0.8315\n",
            "[0.4018210172653198, 0.83152174949646]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view model parameters\n",
        "model = clf_combds_bl.export_model()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f7d22ea-efea-473c-a8cb-4fca66fa1317",
        "id": "gtQMnvST7UJY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 8)]               0         \n",
            "                                                                 \n",
            " multi_category_encoding (Mu  (None, 8)                0         \n",
            " ltiCategoryEncoding)                                            \n",
            "                                                                 \n",
            " normalization (Normalizatio  (None, 8)                17        \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               2304      \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                8224      \n",
            "                                                                 \n",
            " re_lu_1 (ReLU)              (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            " classification_head_1 (Acti  (None, 1)                0         \n",
            " vation)                                                         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,578\n",
            "Trainable params: 10,561\n",
            "Non-trainable params: 17\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute F1 score\n",
        "import sklearn\n",
        "from sklearn.metrics import f1_score\n",
        "sklearn.metrics.f1_score(combined_downsized_strat_test_y, combined_downsized_strat_pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Au5010E-XQou",
        "outputId": "b8892435-43a7-4466-bf1a-ebc0b6eb9dc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8544600938967136"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PLOTTING"
      ],
      "metadata": {
        "id": "yeSsRv08cnaL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA PREPROCESSING\n",
        "(in above created datasets _for_plot after proper accounting of unknowns)"
      ],
      "metadata": {
        "id": "MsLE9wMPfosI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check _for_plot datasets\n",
        "cleveland_for_plot.info()\n",
        "hungarian_for_plot.info()\n",
        "switzerland_for_plot.info()\n",
        "va_for_plot.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DogpCAZcm3B",
        "outputId": "f5ef61ec-8ecb-4664-ea17-085cc2a15781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   age       200 non-null    int64 \n",
            " 1   sex       200 non-null    int64 \n",
            " 2   cp        200 non-null    int64 \n",
            " 3   trestbps  144 non-null    object\n",
            " 4   chol      193 non-null    object\n",
            " 5   fbs       193 non-null    object\n",
            " 6   restecg   200 non-null    int64 \n",
            " 7   thalach   147 non-null    object\n",
            " 8   exang     147 non-null    object\n",
            " 9   oldpeak   144 non-null    object\n",
            " 10  slope     98 non-null     object\n",
            " 11  ca        2 non-null      object\n",
            " 12  thal      34 non-null     object\n",
            " 13  num       200 non-null    int64 \n",
            "dtypes: int64(5), object(9)\n",
            "memory usage: 22.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create combined _for_plot datasets\n",
        "combined_for_plot = pd.concat((cleveland_for_plot, hungarian_for_plot, switzerland_for_plot, va_for_plot), axis=0)\n",
        "combined_with_for_plot = combined_for_plot[combined_for_plot['num'] > 0]\n",
        "combined_wo_for_plot = combined_for_plot[combined_for_plot['num'] == 0]"
      ],
      "metadata": {
        "id": "fRWdPpkYfPdM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to verify\n",
        "combined_for_plot.sample(10)\n",
        "combined_for_plot.value_counts('num')\n",
        "combined_with_for_plot.value_counts('num')\n",
        "combined_wo_for_plot.value_counts('num')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qbC1LSufbSG",
        "outputId": "e0f6395b-2de3-49c9-bd6c-234d697d5bbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num\n",
              "0.0    411\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HISTOGRAMS"
      ],
      "metadata": {
        "id": "z-vZ73OJgmLZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# main histograms - Combined - HD Positive\n",
        "combined_with_for_plot[['age', 'trestbps', 'chol', 'thalach', 'oldpeak']].astype(float).hist(bins = 50, figsize = (10,7.5))\n",
        "plt.suptitle(\"Combined - HD Positive - main histograms\", fontsize = 16)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "t-gjUOz8gnYU",
        "outputId": "97ea6074-c032-493c-8c63-dc39f882ef01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x750 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# main histograms - Combined - HD Negative\n",
        "combined_wo_for_plot[['age', 'trestbps', 'chol', 'thalach', 'oldpeak']].astype(float).hist(bins = 50, figsize = (10,7.5))\n",
        "plt.suptitle(\"Combined - HD Negative - main histograms\", fontsize = 16)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "uYNF84xRg94X",
        "outputId": "65a9fabc-7a0d-4520-c1dc-7d74d84fd5b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x750 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# look at other categories - Combined - HD Positive\n",
        "combined_with_for_plot[['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'num']].astype(float).hist(figsize = (10,7.5))\n",
        "plt.suptitle(\"Combined - HD Positive - other categories\", fontsize = 16)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "PqLvw-UuhhVo",
        "outputId": "cc387b60-e828-4f34-e715-0705bec469f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x750 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# look at other categories - Combined - HD Negative\n",
        "combined_wo_for_plot[['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'num']].astype(float).hist(figsize = (10,7.5))\n",
        "plt.suptitle(\"Combined - HD Negative - other categories\", fontsize = 16)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "ZMtQH0DLhvfQ",
        "outputId": "168f7d88-7098-4a2c-9aad-4b8fb73dcefb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x750 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}