4i_Exercise_1_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise Solution: Synthetic Data (Stroke Thromobolysis Dataset) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Import machine learning methods\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "# Import package for SMOTE\n",
    "import imblearn\n",
    "\n",
    "# import SMOTE from imblearn so we can use it\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Turn warnings off to keep notebook clean\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read CSV into Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/processed_stroke.csv')\n",
    "# Make all data 'float' type and drop ID\n",
    "data = data.astype(float)\n",
    "data.drop('id', axis=1, inplace=True) # Remove ID column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Record number in each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positives : 209\n",
      "Negatives : 4700\n"
     ]
    }
   ],
   "source": [
    "# Record number in each class\n",
    "number_positive_stroke = np.sum(data['stroke'] == 1)\n",
    "number_negative_stroke = np.sum(data['stroke'] == 0)\n",
    "\n",
    "print (f\"Positives : {number_positive_stroke}\")\n",
    "print (f\"Negatives : {number_negative_stroke}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divide into X (features) and y (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('stroke',axis=1) # X = all 'data' except the 'stroke' column\n",
    "y = data['stroke'] # y = 'stroke' column from 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Divide into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show examples from the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type_private</th>\n",
       "      <th>work_type_self_employed</th>\n",
       "      <th>work_type_govt_job</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>work_type_never_worked</th>\n",
       "      <th>residence_type_rural</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>smoking_status_formerly_smoked</th>\n",
       "      <th>smoking_status_never_smoked</th>\n",
       "      <th>smoking_status_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4502</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>55.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>97.68</td>\n",
       "      <td>47.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.61</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.76</td>\n",
       "      <td>18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.79</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.01</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_male  gender_female  gender_other   age  hypertension  \\\n",
       "4502          1.0            0.0           0.0  55.0           0.0   \n",
       "1752          1.0            0.0           0.0  18.0           0.0   \n",
       "4317          1.0            0.0           0.0   3.0           0.0   \n",
       "1538          0.0            1.0           0.0  53.0           0.0   \n",
       "1912          0.0            1.0           0.0  50.0           0.0   \n",
       "\n",
       "      heart_disease  ever_married  work_type_private  work_type_self_employed  \\\n",
       "4502            0.0           1.0                0.0                      1.0   \n",
       "1752            0.0           0.0                1.0                      0.0   \n",
       "4317            0.0           0.0                0.0                      0.0   \n",
       "1538            0.0           1.0                0.0                      0.0   \n",
       "1912            0.0           1.0                1.0                      0.0   \n",
       "\n",
       "      work_type_govt_job  work_type_children  work_type_never_worked  \\\n",
       "4502                 0.0                 0.0                     0.0   \n",
       "1752                 0.0                 0.0                     0.0   \n",
       "4317                 0.0                 1.0                     0.0   \n",
       "1538                 1.0                 0.0                     0.0   \n",
       "1912                 0.0                 0.0                     0.0   \n",
       "\n",
       "      residence_type_rural  avg_glucose_level   bmi  smoking_status_smokes  \\\n",
       "4502                   1.0              97.68  47.1                    0.0   \n",
       "1752                   1.0              89.61  22.0                    0.0   \n",
       "4317                   0.0              72.76  18.8                    0.0   \n",
       "1538                   0.0              83.79  44.0                    0.0   \n",
       "1912                   0.0              95.01  26.2                    0.0   \n",
       "\n",
       "      smoking_status_formerly_smoked  smoking_status_never_smoked  \\\n",
       "4502                             1.0                          0.0   \n",
       "1752                             0.0                          1.0   \n",
       "4317                             0.0                          0.0   \n",
       "1538                             0.0                          0.0   \n",
       "1912                             1.0                          0.0   \n",
       "\n",
       "      smoking_status_unknown  \n",
       "4502                     0.0  \n",
       "1752                     0.0  \n",
       "4317                     1.0  \n",
       "1538                     1.0  \n",
       "1912                     0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardise_data(X_train, X_test):\n",
    "\n",
    "    # Initialise a new scaling object for normalising input data\n",
    "    sc = StandardScaler()\n",
    "\n",
    "    # Set up the scaler just on the training set\n",
    "    sc.fit(X_train)\n",
    "\n",
    "    # Apply the scaler to the training and test sets\n",
    "    train_std=sc.transform(X_train)\n",
    "    test_std=sc.transform(X_test)\n",
    "\n",
    "    return train_std, test_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std, X_test_std = standardise_data(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_std,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use fitted model to make predictions on training and test set data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict training and test set labels\n",
    "y_pred_train = model.predict(X_train_std)\n",
    "y_pred_test = model.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of predicting training data = 0.958\n",
      "Accuracy of predicting test data = 0.957\n"
     ]
    }
   ],
   "source": [
    "accuracy_train = np.mean(y_pred_train == y_train)\n",
    "accuracy_test = np.mean(y_pred_test == y_test)\n",
    "\n",
    "print (f'Accuracy of predicting training data = {accuracy_train:0.3f}')\n",
    "print (f'Accuracy of predicting test data = {accuracy_test:0.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to create synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_synthetic_data_smote(X, y, number_of_samples=[1000,1000]):\n",
    "    \"\"\"\n",
    "    Synthetic data generation for two classes.\n",
    "\n",
    "    Inputs\n",
    "    ------\n",
    "    original_data: X, y numpy arrays (y should have label 0 and 1)\n",
    "    number_of_samples: number of samples to generate (list for y=0, y=1)\n",
    "    (Note - number_of_samples has default of 1000 samples for each class\n",
    "    if no numbers are specified at the point of calling the function)\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    X_synthetic: NumPy array\n",
    "    y_synthetic: NumPy array\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # Count instances in each class\n",
    "    count_label_0 = np.sum(y==0)\n",
    "    count_label_1 = np.sum(y==1)\n",
    "\n",
    "    # SMOTE requires final class counts; add current counts to required counts\n",
    "    # (which are passed into the function)\n",
    "    n_class_0 = number_of_samples[0] + count_label_0\n",
    "    n_class_1 = number_of_samples[1] + count_label_1\n",
    "\n",
    "    # Use SMOTE to sample data points.  The number of points that we pass over\n",
    "    # to SMOTE is calculated above (the number of synthetic data samples we\n",
    "    # want, which we passed into the function + the counts from the original\n",
    "    # data).  This tells SMOTE how many TOTAL data points are needed (original\n",
    "    # + synthetic) for each class.  It then uses the original data to generate\n",
    "    # new synthetic data points.\n",
    "    # For example, imagine our original data has 100 samples for class 0 and 50\n",
    "    # for class 1, and we tell SMOTE we want 100 synthetic data points for\n",
    "    # class 0 and 150 synthetic data points for class 1.  We tell SMOTE that we\n",
    "    # need a total of 200 data points for class 0 (100 original + 100 synthetic)\n",
    "    # and 200 data points for class 1 (50 original + 150 synthetic).  It will\n",
    "    # then fill those data points by taking the original data (which will fill\n",
    "    # up the first 100 \"slots\" for class 0, and the first 50 \"slots\" for class 1)\n",
    "    # and then use these original data points to sample new synthetic data points\n",
    "    # to fill the remaining \"slots\" in each class.\n",
    "    X_resampled, y_resampled = SMOTE(\n",
    "        sampling_strategy = {0:n_class_0, 1:n_class_1}).fit_resample(X, y)\n",
    "\n",
    "    # Get just the additional (synthetic) data points.  By using len(X) for the\n",
    "    # X (input feature) data, and len(y) for the y (output label) data, we skip\n",
    "    # the original data, and just start from the newly created synthetic data,\n",
    "    # generated by SMOTE (above)\n",
    "    X_synthetic = X_resampled[len(X):]\n",
    "    y_synthetic = y_resampled[len(y):]\n",
    "\n",
    "    return X_synthetic, y_synthetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate raw synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get counts of classes from y_train\n",
    "unique, original_frequency = np.unique(y_train, return_counts = True)\n",
    "required_smote_count = list(original_frequency * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the function we wrote above to generate and extract the synthetic data\n",
    "X_synthetic, y_synthetic = make_synthetic_data_smote(\n",
    "        X_train, y_train, number_of_samples=required_smote_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type_private</th>\n",
       "      <th>work_type_self_employed</th>\n",
       "      <th>work_type_govt_job</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>work_type_never_worked</th>\n",
       "      <th>residence_type_rural</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>smoking_status_formerly_smoked</th>\n",
       "      <th>smoking_status_never_smoked</th>\n",
       "      <th>smoking_status_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.568534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.000000</td>\n",
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       "      <td>0.568534</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>0.688153</td>\n",
       "      <td>0.311847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.311847</td>\n",
       "      <td>0.311847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.688153</td>\n",
       "      <td>0.311847</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>222.470035</td>\n",
       "      <td>34.268815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3684</th>\n",
       "      <td>0.375244</td>\n",
       "      <td>0.624756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.529825</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>133.224763</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_male  gender_female  gender_other        age  hypertension  \\\n",
       "3681     1.000000       0.000000           0.0   5.000000      0.000000   \n",
       "3682     0.431466       0.568534           0.0  13.000000      0.000000   \n",
       "3683     0.688153       0.311847           0.0  70.311847      0.311847   \n",
       "3684     0.375244       0.624756           0.0   1.529825      0.000000   \n",
       "3685     0.000000       1.000000           0.0  33.765655      0.000000   \n",
       "\n",
       "      heart_disease  ever_married  work_type_private  work_type_self_employed  \\\n",
       "3681            0.0           0.0                0.0                 0.000000   \n",
       "3682            0.0           0.0                0.0                 0.000000   \n",
       "3683            0.0           1.0                0.0                 0.688153   \n",
       "3684            0.0           0.0                0.0                 0.000000   \n",
       "3685            0.0           1.0                1.0                 0.000000   \n",
       "\n",
       "      work_type_govt_job  work_type_children  work_type_never_worked  \\\n",
       "3681            0.000000                 1.0                     0.0   \n",
       "3682            0.000000                 1.0                     0.0   \n",
       "3683            0.311847                 0.0                     0.0   \n",
       "3684            0.000000                 1.0                     0.0   \n",
       "3685            0.000000                 0.0                     0.0   \n",
       "\n",
       "      residence_type_rural  avg_glucose_level        bmi  \\\n",
       "3681              1.000000          99.779906  17.148605   \n",
       "3682              0.568534          76.816507  20.868534   \n",
       "3683              0.000000         222.470035  34.268815   \n",
       "3684              0.000000         164.704902  16.825731   \n",
       "3685              0.234345         133.224763  21.501422   \n",
       "\n",
       "      smoking_status_smokes  smoking_status_formerly_smoked  \\\n",
       "3681                    0.0                             0.0   \n",
       "3682                    0.0                             0.0   \n",
       "3683                    0.0                             1.0   \n",
       "3684                    0.0                             0.0   \n",
       "3685                    0.0                             0.0   \n",
       "\n",
       "      smoking_status_never_smoked  smoking_status_unknown  \n",
       "3681                          0.0                     1.0  \n",
       "3682                          0.0                     1.0  \n",
       "3683                          0.0                     0.0  \n",
       "3684                          0.0                     1.0  \n",
       "3685                          1.0                     0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_synthetic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare lists of categorical, integer and binary features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get full list of column names (the names of our features)\n",
    "X_col_names = list(X_train)\n",
    "\n",
    "# Set categorical one-hots cols using common prefix\n",
    "categorical = ['gender_', 'work_type_', 'smoking_status_']\n",
    "\n",
    "one_hot_cols = []\n",
    "for col in categorical:\n",
    "    one_hot_cols.append([x for x in X_col_names if x[0:len(col)] == col])\n",
    "\n",
    "# Set integer columns\n",
    "integer_cols = ['age']\n",
    "\n",
    "# Set binary columns\n",
    "binary_cols = ['hypertension',\n",
    "               'heart_disease',\n",
    "               'ever_married',\n",
    "               'residence_type_rural']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to process raw synthetic categorical data to one-hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_one_hot(x):\n",
    "    \"\"\"\n",
    "    Takes a list/array/series and turns it into a one-hot encoded\n",
    "    list/array series, by setting 1 for highest value and 0 for all\n",
    "    others\n",
    "\n",
    "    \"\"\"\n",
    "    # Get argmax (this returns the index of the highest values in\n",
    "    # the list / array / series passed in to the function)\n",
    "    highest = np.argmax(x)\n",
    "    # Set all values to zero (just multiply all values by 0)\n",
    "    x *= 0.0\n",
    "    # Set the value that was found to be the highest to 1, by\n",
    "    # using the index we found using argmax above\n",
    "    x[highest] = 1.0\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process raw synthetic data and show a sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set y_label\n",
    "y_label = 'stroke'\n",
    "\n",
    "# Create a data frame with id to store the synthetic data\n",
    "synth_df = pd.DataFrame()\n",
    "\n",
    "# Transfer X values to the new DataFrame\n",
    "synth_df=pd.concat([synth_df,\n",
    "                    pd.DataFrame(X_synthetic, columns=X_col_names)],\n",
    "                    axis=1)\n",
    "\n",
    "# Make columns (that need to be) one hot encoded using the\n",
    "# function we wrote above, using the raw synthetic data\n",
    "for one_hot_list in one_hot_cols:\n",
    "    for index, row in synth_df.iterrows():\n",
    "        x = row[one_hot_list]\n",
    "        x_one_hot = make_one_hot(x)\n",
    "        row[x_one_hot.index]= x_one_hot.values\n",
    "\n",
    "# Make integer as necessary by rounding the raw synthetic data\n",
    "for col in integer_cols:\n",
    "    synth_df[col] = synth_df[col].round(0)\n",
    "\n",
    "# Round binary cols and clip so values under 0 or above 1\n",
    "# are set to 0 and 1 respectively (this won't happen with\n",
    "# SMOTE, as it will only sample between the two points (so\n",
    "# points sampled between binary points will always be\n",
    "# between 0 and 1) but it can happen with other methods)\n",
    "for col in binary_cols:\n",
    "    synth_df[col] = np.clip(synth_df[col],0,1).round(0)\n",
    "\n",
    "# Add y data with a label\n",
    "y_list = list(y_synthetic)\n",
    "synth_df[y_label] = y_list\n",
    "\n",
    "# Shuffle data\n",
    "synth_df = synth_df.sample(frac=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type_private</th>\n",
       "      <th>work_type_self_employed</th>\n",
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       "      <th>work_type_children</th>\n",
       "      <th>work_type_never_worked</th>\n",
       "      <th>residence_type_rural</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>smoking_status_formerly_smoked</th>\n",
       "      <th>smoking_status_never_smoked</th>\n",
       "      <th>smoking_status_unknown</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3956</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>57.0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "       gender_male  gender_female  gender_other   age  hypertension  \\\n",
       "11029          1.0            0.0           0.0  66.0           0.0   \n",
       "8917           0.0            1.0           0.0   1.0           0.0   \n",
       "7515           1.0            0.0           0.0  13.0           0.0   \n",
       "10261          0.0            1.0           0.0  68.0           0.0   \n",
       "3956           1.0            0.0           0.0  57.0           0.0   \n",
       "\n",
       "       heart_disease  ever_married  work_type_private  \\\n",
       "11029            0.0           1.0                1.0   \n",
       "8917             0.0           0.0                0.0   \n",
       "7515             0.0           0.0                0.0   \n",
       "10261            0.0           1.0                0.0   \n",
       "3956             0.0           1.0                1.0   \n",
       "\n",
       "       work_type_self_employed  work_type_govt_job  work_type_children  \\\n",
       "11029                      0.0                 0.0                 0.0   \n",
       "8917                       0.0                 0.0                 1.0   \n",
       "7515                       0.0                 0.0                 1.0   \n",
       "10261                      1.0                 0.0                 0.0   \n",
       "3956                       0.0                 0.0                 0.0   \n",
       "\n",
       "       work_type_never_worked  residence_type_rural  avg_glucose_level  \\\n",
       "11029                     0.0                   1.0          76.595595   \n",
       "8917                      0.0                   1.0          86.177242   \n",
       "7515                      0.0                   1.0          98.342804   \n",
       "10261                     0.0                   1.0         111.693441   \n",
       "3956                      0.0                   1.0          71.070423   \n",
       "\n",
       "             bmi  smoking_status_smokes  smoking_status_formerly_smoked  \\\n",
       "11029  21.341845                    0.0                             1.0   \n",
       "8917   16.819550                    0.0                             0.0   \n",
       "7515   19.927970                    0.0                             0.0   \n",
       "10261  26.023267                    0.0                             0.0   \n",
       "3956   25.928229                    0.0                             1.0   \n",
       "\n",
       "       smoking_status_never_smoked  smoking_status_unknown  stroke  \n",
       "11029                          0.0                     0.0     1.0  \n",
       "8917                           0.0                     1.0     0.0  \n",
       "7515                           0.0                     1.0     0.0  \n",
       "10261                          1.0                     0.0     0.0  \n",
       "3956                           0.0                     0.0     0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find nearest original data point to each synthetic data point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardise synthetic data (based on real training data)\n",
    "X_train_std, X_synth_std = standardise_data(X_train, X_synthetic)\n",
    "\n",
    "# Get ALL real X data (combine standardised training + test data)\n",
    "# We do this because we need to check for duplicates / very close\n",
    "# values in all of the real data we've got\n",
    "X_real_std = np.concatenate([X_train_std, X_test_std], axis=0)\n",
    "\n",
    "# Use SciKitLearn neighbors.NearestNeighbors to find nearest neighbour\n",
    "# to each data point. First, we fit to the real standardised data\n",
    "# (all of it, train + test set).  Then we can give it the synthetic data\n",
    "# and ask it to give us the cartesian distance and ID of its nearest\n",
    "# real world data point neighbour for each synthetic data point.\n",
    "nn = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(X_real_std)\n",
    "dists, idxs = nn.kneighbors(X_synth_std)\n",
    "\n",
    "# Store the index and ids (indices) in the synthetic data DataFrame\n",
    "# Flatten just reduces something in more than 1 dimension down to\n",
    "# 1 dimension (eg a list of lists becomes a single list)\n",
    "synth_df['distance_to_closest_real'] = list(dists.flatten())\n",
    "synth_df['closest_X_real_row_index'] = list(idxs.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
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       "      <th>distance_to_closest_real</th>\n",
       "      <th>closest_X_real_row_index</th>\n",
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       "      <td>19.927970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.901454</td>\n",
       "      <td>2592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10261</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>111.693441</td>\n",
       "      <td>26.023267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.088711</td>\n",
       "      <td>2726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3956</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71.070423</td>\n",
       "      <td>25.928229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.489179</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6383</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>131.403461</td>\n",
       "      <td>31.684226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.295965</td>\n",
       "      <td>3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9031</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.107014</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.378650</td>\n",
       "      <td>2951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5755</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.518722</td>\n",
       "      <td>33.282594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.831434</td>\n",
       "      <td>3521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9023</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>210.940018</td>\n",
       "      <td>23.934760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.758766</td>\n",
       "      <td>3469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4264</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.439642</td>\n",
       "      <td>29.097616</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.721686</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7362 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender_male  gender_female  gender_other   age  hypertension  \\\n",
       "11029          1.0            0.0           0.0  66.0           0.0   \n",
       "8917           0.0            1.0           0.0   1.0           0.0   \n",
       "7515           1.0            0.0           0.0  13.0           0.0   \n",
       "10261          0.0            1.0           0.0  68.0           0.0   \n",
       "3956           1.0            0.0           0.0  57.0           0.0   \n",
       "...            ...            ...           ...   ...           ...   \n",
       "6383           0.0            1.0           0.0  27.0           0.0   \n",
       "9031           0.0            1.0           0.0  36.0           0.0   \n",
       "5755           0.0            1.0           0.0   8.0           0.0   \n",
       "9023           0.0            1.0           0.0  66.0           0.0   \n",
       "4264           0.0            1.0           0.0  54.0           0.0   \n",
       "\n",
       "       heart_disease  ever_married  work_type_private  \\\n",
       "11029            0.0           1.0                1.0   \n",
       "8917             0.0           0.0                0.0   \n",
       "7515             0.0           0.0                0.0   \n",
       "10261            0.0           1.0                0.0   \n",
       "3956             0.0           1.0                1.0   \n",
       "...              ...           ...                ...   \n",
       "6383             0.0           0.0                1.0   \n",
       "9031             0.0           1.0                1.0   \n",
       "5755             0.0           0.0                0.0   \n",
       "9023             0.0           1.0                0.0   \n",
       "4264             0.0           1.0                0.0   \n",
       "\n",
       "       work_type_self_employed  work_type_govt_job  ...  residence_type_rural  \\\n",
       "11029                      0.0                 0.0  ...                   1.0   \n",
       "8917                       0.0                 0.0  ...                   1.0   \n",
       "7515                       0.0                 0.0  ...                   1.0   \n",
       "10261                      1.0                 0.0  ...                   1.0   \n",
       "3956                       0.0                 0.0  ...                   1.0   \n",
       "...                        ...                 ...  ...                   ...   \n",
       "6383                       0.0                 0.0  ...                   1.0   \n",
       "9031                       0.0                 0.0  ...                   0.0   \n",
       "5755                       0.0                 0.0  ...                   0.0   \n",
       "9023                       1.0                 0.0  ...                   1.0   \n",
       "4264                       1.0                 0.0  ...                   0.0   \n",
       "\n",
       "       avg_glucose_level        bmi  smoking_status_smokes  \\\n",
       "11029          76.595595  21.341845                    0.0   \n",
       "8917           86.177242  16.819550                    0.0   \n",
       "7515           98.342804  19.927970                    0.0   \n",
       "10261         111.693441  26.023267                    0.0   \n",
       "3956           71.070423  25.928229                    0.0   \n",
       "...                  ...        ...                    ...   \n",
       "6383          131.403461  31.684226                    0.0   \n",
       "9031           60.107014  24.100000                    0.0   \n",
       "5755           96.518722  33.282594                    0.0   \n",
       "9023          210.940018  23.934760                    0.0   \n",
       "4264          107.439642  29.097616                    0.0   \n",
       "\n",
       "       smoking_status_formerly_smoked  smoking_status_never_smoked  \\\n",
       "11029                             1.0                          0.0   \n",
       "8917                              0.0                          0.0   \n",
       "7515                              0.0                          0.0   \n",
       "10261                             0.0                          1.0   \n",
       "3956                              1.0                          0.0   \n",
       "...                               ...                          ...   \n",
       "6383                              0.0                          1.0   \n",
       "9031                              0.0                          0.0   \n",
       "5755                              0.0                          0.0   \n",
       "9023                              0.0                          1.0   \n",
       "4264                              1.0                          0.0   \n",
       "\n",
       "       smoking_status_unknown  stroke  distance_to_closest_real  \\\n",
       "11029                     0.0     1.0                  0.017891   \n",
       "8917                      1.0     0.0                  1.512034   \n",
       "7515                      1.0     0.0                  1.901454   \n",
       "10261                     0.0     0.0                  1.088711   \n",
       "3956                      0.0     0.0                  0.489179   \n",
       "...                       ...     ...                       ...   \n",
       "6383                      0.0     0.0                  0.295965   \n",
       "9031                      1.0     0.0                  0.378650   \n",
       "5755                      1.0     0.0                  1.831434   \n",
       "9023                      0.0     0.0                  0.758766   \n",
       "4264                      0.0     0.0                  0.721686   \n",
       "\n",
       "       closest_X_real_row_index  \n",
       "11029                      3246  \n",
       "8917                        527  \n",
       "7515                       2592  \n",
       "10261                      2726  \n",
       "3956                       3500  \n",
       "...                         ...  \n",
       "6383                       3612  \n",
       "9031                       2951  \n",
       "5755                       3521  \n",
       "9023                       3469  \n",
       "4264                        217  \n",
       "\n",
       "[7362 rows x 22 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synth_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove identical points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of data points identical to real data points = 0.001\n"
     ]
    }
   ],
   "source": [
    "# Get points with zero distance to real (use distance of <0.001 as effectively identical)\n",
    "identical = synth_df['distance_to_closest_real'] < 0.001\n",
    "\n",
    "print (f'Proportion of data points identical to real data points = {identical.mean():0.3f}')\n",
    "# Remove points with zero (or effectively zero) distance to a real data point.  We\n",
    "# do this by setting up a mask that says we only want to see data points where the \"identical\"\n",
    "# criterion we specified above is false (ie they're not identical).  Then we apply that\n",
    "# mask and overwrite our existing synthetic data DataFrame so we've now only got data points\n",
    "# that are not identical to real world data points.\n",
    "mask = identical == False\n",
    "synth_df = synth_df[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove points closest to original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Proportion of points to remove\n",
    "proportion_to_remove = 0.1\n",
    "\n",
    "# Sort by distance, with highest distances (those we want to keep) at\n",
    "# the top\n",
    "synth_by_distance = synth_df.sort_values(\n",
    "    'distance_to_closest_real', ascending=False)\n",
    "\n",
    "# Limit data.  Calculate the number of entries to keep as being the\n",
    "# total number of synthetic data points we've now got (after having\n",
    "# removed ones identical to real world data points) multiplied by\n",
    "# the proportion we want to keep (the inverse of the proportion to remove).\n",
    "# As we've sorted in descending order by distance, we can then just\n",
    "# use .head to identify how much of the top of list we want to keep\n",
    "# (90% in this case, where we're removing the 10% that are closest - at\n",
    "# the bottom)\n",
    "number_to_keep = int(len(synth_by_distance) * (1 - proportion_to_remove))\n",
    "synth_by_distance = synth_by_distance.head(number_to_keep)\n",
    "\n",
    "# Shuffle and store back in synth_df (frac=1 gives us a sample size of 100%\n",
    "# (ie - all of the ones we said above we wanted to keep))\n",
    "synth_df = synth_by_distance.sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show five examples with their closest data points in the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Synthetic_1</th>\n",
       "      <th>Synthetic_1_closest</th>\n",
       "      <th>Synthetic_2</th>\n",
       "      <th>Synthetic_2_closest</th>\n",
       "      <th>Synthetic_3</th>\n",
       "      <th>Synthetic_3_closest</th>\n",
       "      <th>Synthetic_4</th>\n",
       "      <th>Synthetic_4_closest</th>\n",
       "      <th>Synthetic_5</th>\n",
       "      <th>Synthetic_5_closest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender_male</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_female</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_other</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>19.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type_private</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type_self_employed</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type_govt_job</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type_children</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type_never_worked</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence_type_rural</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>67.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_formerly_smoked</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_never_smoked</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status_unknown</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Synthetic_1  Synthetic_1_closest  Synthetic_2  \\\n",
       "gender_male                             0.0                  1.0          0.0   \n",
       "gender_female                           1.0                  0.0          1.0   \n",
       "gender_other                            0.0                  0.0          0.0   \n",
       "age                                    19.0                 80.0         58.0   \n",
       "hypertension                            1.0                  0.0          0.0   \n",
       "heart_disease                           0.0                  0.0          0.0   \n",
       "ever_married                            0.0                  1.0          1.0   \n",
       "work_type_private                       1.0                  0.0          0.0   \n",
       "work_type_self_employed                 0.0                  1.0          0.0   \n",
       "work_type_govt_job                      0.0                  0.0          1.0   \n",
       "work_type_children                      0.0                  0.0          0.0   \n",
       "work_type_never_worked                  0.0                  0.0          0.0   \n",
       "residence_type_rural                    1.0                  1.0          1.0   \n",
       "avg_glucose_level                      67.0                104.0         69.0   \n",
       "bmi                                    30.0                 24.0         28.0   \n",
       "smoking_status_smokes                   0.0                  0.0          0.0   \n",
       "smoking_status_formerly_smoked          0.0                  0.0          0.0   \n",
       "smoking_status_never_smoked             1.0                  1.0          0.0   \n",
       "smoking_status_unknown                  0.0                  0.0          1.0   \n",
       "\n",
       "                                Synthetic_2_closest  Synthetic_3  \\\n",
       "gender_male                                     0.0          0.0   \n",
       "gender_female                                   1.0          1.0   \n",
       "gender_other                                    0.0          0.0   \n",
       "age                                            79.0         43.0   \n",
       "hypertension                                    0.0          0.0   \n",
       "heart_disease                                   0.0          0.0   \n",
       "ever_married                                    1.0          0.0   \n",
       "work_type_private                               0.0          0.0   \n",
       "work_type_self_employed                         0.0          1.0   \n",
       "work_type_govt_job                              1.0          0.0   \n",
       "work_type_children                              0.0          0.0   \n",
       "work_type_never_worked                          0.0          0.0   \n",
       "residence_type_rural                            0.0          1.0   \n",
       "avg_glucose_level                              74.0         93.0   \n",
       "bmi                                            30.0         23.0   \n",
       "smoking_status_smokes                           0.0          0.0   \n",
       "smoking_status_formerly_smoked                  0.0          0.0   \n",
       "smoking_status_never_smoked                     0.0          0.0   \n",
       "smoking_status_unknown                          1.0          1.0   \n",
       "\n",
       "                                Synthetic_3_closest  Synthetic_4  \\\n",
       "gender_male                                     0.0          1.0   \n",
       "gender_female                                   1.0          0.0   \n",
       "gender_other                                    0.0          0.0   \n",
       "age                                            67.0         70.0   \n",
       "hypertension                                    0.0          1.0   \n",
       "heart_disease                                   0.0          0.0   \n",
       "ever_married                                    1.0          1.0   \n",
       "work_type_private                               1.0          0.0   \n",
       "work_type_self_employed                         0.0          1.0   \n",
       "work_type_govt_job                              0.0          0.0   \n",
       "work_type_children                              0.0          0.0   \n",
       "work_type_never_worked                          0.0          0.0   \n",
       "residence_type_rural                            0.0          0.0   \n",
       "avg_glucose_level                              82.0        251.0   \n",
       "bmi                                            14.0         27.0   \n",
       "smoking_status_smokes                           0.0          0.0   \n",
       "smoking_status_formerly_smoked                  0.0          0.0   \n",
       "smoking_status_never_smoked                     1.0          1.0   \n",
       "smoking_status_unknown                          0.0          0.0   \n",
       "\n",
       "                                Synthetic_4_closest  Synthetic_5  \\\n",
       "gender_male                                     1.0          0.0   \n",
       "gender_female                                   0.0          1.0   \n",
       "gender_other                                    0.0          0.0   \n",
       "age                                            59.0         23.0   \n",
       "hypertension                                    1.0          0.0   \n",
       "heart_disease                                   1.0          0.0   \n",
       "ever_married                                    1.0          0.0   \n",
       "work_type_private                               0.0          1.0   \n",
       "work_type_self_employed                         0.0          0.0   \n",
       "work_type_govt_job                              1.0          0.0   \n",
       "work_type_children                              0.0          0.0   \n",
       "work_type_never_worked                          0.0          0.0   \n",
       "residence_type_rural                            1.0          1.0   \n",
       "avg_glucose_level                              82.0         63.0   \n",
       "bmi                                            33.0         25.0   \n",
       "smoking_status_smokes                           0.0          1.0   \n",
       "smoking_status_formerly_smoked                  0.0          0.0   \n",
       "smoking_status_never_smoked                     1.0          0.0   \n",
       "smoking_status_unknown                          0.0          0.0   \n",
       "\n",
       "                                Synthetic_5_closest  \n",
       "gender_male                                     1.0  \n",
       "gender_female                                   0.0  \n",
       "gender_other                                    0.0  \n",
       "age                                            48.0  \n",
       "hypertension                                    0.0  \n",
       "heart_disease                                   0.0  \n",
       "ever_married                                    1.0  \n",
       "work_type_private                               1.0  \n",
       "work_type_self_employed                         0.0  \n",
       "work_type_govt_job                              0.0  \n",
       "work_type_children                              0.0  \n",
       "work_type_never_worked                          0.0  \n",
       "residence_type_rural                            1.0  \n",
       "avg_glucose_level                             147.0  \n",
       "bmi                                            22.0  \n",
       "smoking_status_smokes                           0.0  \n",
       "smoking_status_formerly_smoked                  0.0  \n",
       "smoking_status_never_smoked                     0.0  \n",
       "smoking_status_unknown                          1.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reproduce X_real but with non-standardised (ie the raw original) values for\n",
    "# comparison\n",
    "X_real = np.concatenate([X_train, X_test], axis=0)\n",
    "\n",
    "# Set up Data Frame for comparison\n",
    "comparison = pd.DataFrame(index=X_col_names)\n",
    "\n",
    "# Generate five examples\n",
    "for i in range(5):\n",
    "    # Get synthetic data sample (sample size of 1 - one data point)\n",
    "    sample = synth_df.sample(1)\n",
    "    comparison[f'Synthetic_{i+1}'] = sample[X_col_names].values[0]\n",
    "    # Get closest point from the real data (remember we stored earlier\n",
    "    # the index of the closest real world point, so we can grab it out\n",
    "    # easily here)We remove\n",
    "    closest_id = sample['closest_X_real_row_index']\n",
    "    comparison[f'Synthetic_{i+1}_closest'] = X_real[closest_id, :][0]\n",
    "\n",
    "# Display the comparisons\n",
    "comparison.round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample from synthetic data to get same size / balance as the original data, and compare counts to ensure identical to original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly sample from the synthetic data those who had a stroke,\n",
    "# and sample this the same number of times as we had number\n",
    "# who had a stroke in the real data\n",
    "mask = synth_df['stroke'] == 1\n",
    "synth_positive_stroke = synth_df[mask].sample(number_positive_stroke)\n",
    "\n",
    "# The same as above, but for those who didn't have a stroke\n",
    "mask = synth_df['stroke'] == 0\n",
    "synth_negative_stroke = synth_df[mask].sample(number_negative_stroke)\n",
    "\n",
    "# Reconstruct into synth_df and shuffle\n",
    "synth_df = pd.concat([synth_positive_stroke, synth_negative_stroke], axis=0)\n",
    "synth_df = synth_df.sample(frac=1.0, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of real data stroke :  209\n",
      "Number of synthetic data stroke :  209\n",
      "Number of real data non-stroke :  4700\n",
      "Number of synthetic data non-stroke :  4700\n"
     ]
    }
   ],
   "source": [
    "print ('Number of real data stroke : ', np.sum(data['stroke'] == 1))\n",
    "print ('Number of synthetic data stroke : ', np.sum(synth_df['stroke'] == 1))\n",
    "print ('Number of real data non-stroke : ', np.sum(data['stroke'] == 0))\n",
    "print ('Number of synthetic data non-stroke : ', np.sum(synth_df['stroke'] == 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Logistic Regression model using synthetic data and compare accuracy with model trained on original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of predicting test data from model trained on real data = 0.957\n",
      "Accuracy of predicting test data from model trained on synthetic data = 0.957\n"
     ]
    }
   ],
   "source": [
    "# Get X data and standardised\n",
    "X_synth = synth_df[X_col_names]\n",
    "y_synth = synth_df['stroke'].values\n",
    "X_synth_std, X_test_std = standardise_data(X_synth, X_test)\n",
    "\n",
    "# Fit model\n",
    "model_synth = LogisticRegression()\n",
    "model_synth.fit(X_synth_std,y_synth)\n",
    "\n",
    "# Get predictions of test set\n",
    "y_pred_test_synth = model_synth.predict(X_test_std)\n",
    "\n",
    "# Report accuracy\n",
    "accuracy_test_synth = np.mean(y_pred_test_synth == y_test)\n",
    "\n",
    "print (f'Accuracy of predicting test data from model trained on real data = {accuracy_test:0.3f}')\n",
    "print (f'Accuracy of predicting test data from model trained on synthetic data = {accuracy_test_synth:0.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_probs = model.predict_proba(X_test_std)[:,1]\n",
    "y_probs_synthetic = model_synth.predict_proba(X_test_std)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC real training data: 0.83\n",
      "ROC AUC synthetic training data: 0.83\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n",
    "fpr_synth, tpr_synth, thresholds_synth = roc_curve(y_test, y_probs_synthetic)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "roc_auc_snth = auc(fpr_synth, tpr_synth)\n",
    "print (f'ROC AUC real training data: {roc_auc:0.2f}')\n",
    "print (f'ROC AUC synthetic training data: {roc_auc_snth:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot ROC\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "\n",
    "ax.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
    "ax.plot(fpr,tpr, color='green', label = 'Real training data')\n",
    "ax.plot(fpr_synth,tpr_synth, color='red', label = 'Synthetic training data')\n",
    "\n",
    "ax.set_title('Stroke Data Receiver Operator Characteristic curve')\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "fig.savefig('synthetic_roc.png')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use synthetic data for positive class to generate another 2000 for this class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate synthetic data again, but this time with 4000 extra synthetic data\n",
    "# points for the positive class (double what we need), and 0 for the negative\n",
    "# class\n",
    "X_synthetic, y_synthetic = make_synthetic_data_smote(\n",
    "    X_train, y_train, number_of_samples=[0, 4000]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set y_label\n",
    "y_label = 'stroke'\n",
    "\n",
    "# Create a data frame with id to store the synthetic data\n",
    "synth_df = pd.DataFrame()\n",
    "\n",
    "# Transfer X values to the new DataFrame\n",
    "synth_df=pd.concat([synth_df,\n",
    "                    pd.DataFrame(X_synthetic, columns=X_col_names)],\n",
    "                    axis=1)\n",
    "\n",
    "# Make columns (that need to be) one hot encoded using the\n",
    "# function we wrote above, using the raw synthetic data\n",
    "for one_hot_list in one_hot_cols:\n",
    "    for index, row in synth_df.iterrows():\n",
    "        x = row[one_hot_list]\n",
    "        x_one_hot = make_one_hot(x)\n",
    "        row[x_one_hot.index]= x_one_hot.values\n",
    "\n",
    "# Make integer as necessary by rounding the raw synthetic data\n",
    "for col in integer_cols:\n",
    "    synth_df[col] = synth_df[col].round(0)\n",
    "\n",
    "# Round binary cols and clip so values under 0 or above 1\n",
    "# are set to 0 and 1 respectively (this won't happen with\n",
    "# SMOTE, as it will only sample between the two points (so\n",
    "# points sampled between binary points will always be\n",
    "# between 0 and 1) but it can happen with other methods)\n",
    "for col in binary_cols:\n",
    "    synth_df[col] = np.clip(synth_df[col],0,1).round(0)\n",
    "\n",
    "# Add y data with a label\n",
    "y_list = list(y_synthetic)\n",
    "synth_df[y_label] = y_list\n",
    "\n",
    "# Shuffle data\n",
    "synth_df = synth_df.sample(frac=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardise synthetic data (based on real training data)\n",
    "X_train_std, X_synth_std = standardise_data(X_train, X_synthetic)\n",
    "\n",
    "# Get ALL real X data (combine standardised training + test data)\n",
    "# We do this because we need to check for duplicates / very close\n",
    "# values in all of the real data we've got\n",
    "X_real_std = np.concatenate([X_train_std, X_test_std], axis=0)\n",
    "\n",
    "# Use SciKitLearn neighbors.NearestNeighbors to find nearest neighbour\n",
    "# to each data point. First, we fit to the real standardised data\n",
    "# (all of it, train + test set).  Then we can give it the synthetic data\n",
    "# and ask it to give us the cartesian distance and ID of its nearest\n",
    "# real world data point neighbour for each synthetic data point.\n",
    "nn = NearestNeighbors(n_neighbors=1, algorithm='auto').fit(X_real_std)\n",
    "dists, idxs = nn.kneighbors(X_synth_std)\n",
    "\n",
    "# Store the index and ids (indices) in the synthetic data DataFrame\n",
    "# Flatten just reduces something in more than 1 dimension down to\n",
    "# 1 dimension (eg a list of lists becomes a single list)\n",
    "synth_df['distance_to_closest_real'] = list(dists.flatten())\n",
    "synth_df['closest_X_real_row_index'] = list(idxs.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of data points identical to real data points = 0.001\n"
     ]
    }
   ],
   "source": [
    "# Get points with zero distance to real (use distance of <0.001 as effectively identical)\n",
    "identical = synth_df['distance_to_closest_real'] < 0.001\n",
    "\n",
    "print (f'Proportion of data points identical to real data points = {identical.mean():0.3f}')\n",
    "# Remove points with zero (or effectively zero) distance to a real data point.  We\n",
    "# do this by setting up a mask that says we only want to see data points where the \"identical\"\n",
    "# criterion we specified above is false (ie they're not identical).  Then we apply that\n",
    "# mask and overwrite our existing synthetic data DataFrame so we've now only got data points\n",
    "# that are not identical to real world data points.\n",
    "mask = identical == False\n",
    "synth_df = synth_df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Proportion of points to remove\n",
    "proportion_to_remove = 0.1\n",
    "\n",
    "# Sort by distance, with highest distances (those we want to keep) at\n",
    "# the top\n",
    "synth_by_distance = synth_df.sort_values(\n",
    "    'distance_to_closest_real', ascending=False)\n",
    "\n",
    "# Limit data.  Calculate the number of entries to keep as being the\n",
    "# total number of synthetic data points we've now got (after having\n",
    "# removed ones identical to real world data points) multiplied by\n",
    "# the proportion we want to keep (the inverse of the proportion to remove).\n",
    "# As we've sorted in descending order by distance, we can then just\n",
    "# use .head to identify how much of the top of list we want to keep\n",
    "# (90% in this case, where we're removing the 10% that are closest - at\n",
    "# the bottom)\n",
    "number_to_keep = int(len(synth_by_distance) * (1 - proportion_to_remove))\n",
    "synth_by_distance = synth_by_distance.head(number_to_keep)\n",
    "\n",
    "# Shuffle and store back in synth_df (frac=1 gives us a sample size of 100%\n",
    "# (ie - all of the ones we said above we wanted to keep))\n",
    "synth_df = synth_by_distance.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only a random sample of 2000 of the remaining synthetic datapoints\n",
    "# We don't need a mask here as ALL our synthetic datapoints are for class 1\n",
    "# (positive).\n",
    "synth_df = synth_df.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add synthetic data for positive class (class 1) to real data\n",
    "# We'll make a separate copy of the original dataframe with the new synthetic\n",
    "# data points added, keeping our original data intact.\n",
    "augmented_data = pd.concat([data, synth_df])\n",
    "\n",
    "# We'll also get rid of the two columns we added -\n",
    "# distance_to_closest_real and closest_X_real_row_index as we do not want these\n",
    "# to be used in a Logistic Regression model.\n",
    "augmented_data.drop('distance_to_closest_real', axis=1, inplace=True)\n",
    "augmented_data.drop('closest_X_real_row_index', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_male</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_other</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type_private</th>\n",
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       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
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       "      <th>smoking_status_formerly_smoked</th>\n",
       "      <th>smoking_status_never_smoked</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6909 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_male  gender_female  gender_other   age  hypertension  \\\n",
       "0             1.0            0.0           0.0  67.0           0.0   \n",
       "1             1.0            0.0           0.0  80.0           0.0   \n",
       "2             0.0            1.0           0.0  49.0           0.0   \n",
       "3             0.0            1.0           0.0  79.0           1.0   \n",
       "4             1.0            0.0           0.0  81.0           0.0   \n",
       "...           ...            ...           ...   ...           ...   \n",
       "5376          1.0            0.0           0.0  76.0           0.0   \n",
       "4560          1.0            0.0           0.0  81.0           0.0   \n",
       "4276          1.0            0.0           0.0  50.0           0.0   \n",
       "7584          0.0            1.0           0.0  78.0           0.0   \n",
       "3688          0.0            1.0           0.0  69.0           1.0   \n",
       "\n",
       "      heart_disease  ever_married  work_type_private  work_type_self_employed  \\\n",
       "0               1.0           1.0                1.0                      0.0   \n",
       "1               1.0           1.0                1.0                      0.0   \n",
       "2               0.0           1.0                1.0                      0.0   \n",
       "3               0.0           1.0                0.0                      1.0   \n",
       "4               0.0           1.0                1.0                      0.0   \n",
       "...             ...           ...                ...                      ...   \n",
       "5376            1.0           1.0                1.0                      0.0   \n",
       "4560            0.0           1.0                0.0                      1.0   \n",
       "4276            0.0           0.0                1.0                      0.0   \n",
       "7584            0.0           1.0                0.0                      1.0   \n",
       "3688            1.0           0.0                0.0                      1.0   \n",
       "\n",
       "      work_type_govt_job  work_type_children  work_type_never_worked  \\\n",
       "0                    0.0                 0.0                     0.0   \n",
       "1                    0.0                 0.0                     0.0   \n",
       "2                    0.0                 0.0                     0.0   \n",
       "3                    0.0                 0.0                     0.0   \n",
       "4                    0.0                 0.0                     0.0   \n",
       "...                  ...                 ...                     ...   \n",
       "5376                 0.0                 0.0                     0.0   \n",
       "4560                 0.0                 0.0                     0.0   \n",
       "4276                 0.0                 0.0                     0.0   \n",
       "7584                 0.0                 0.0                     0.0   \n",
       "3688                 0.0                 0.0                     0.0   \n",
       "\n",
       "      residence_type_rural  avg_glucose_level        bmi  \\\n",
       "0                      0.0         228.690000  36.600000   \n",
       "1                      1.0         105.920000  32.500000   \n",
       "2                      0.0         171.230000  34.400000   \n",
       "3                      1.0         174.120000  24.000000   \n",
       "4                      0.0         186.210000  29.000000   \n",
       "...                    ...                ...        ...   \n",
       "5376                   1.0         130.633158  25.249173   \n",
       "4560                   0.0          97.955702  33.567697   \n",
       "4276                   1.0         101.679233  29.784617   \n",
       "7584                   1.0         107.656949  30.885320   \n",
       "3688                   0.0          73.619327  35.449577   \n",
       "\n",
       "      smoking_status_smokes  smoking_status_formerly_smoked  \\\n",
       "0                       0.0                             1.0   \n",
       "1                       0.0                             0.0   \n",
       "2                       1.0                             0.0   \n",
       "3                       0.0                             0.0   \n",
       "4                       0.0                             1.0   \n",
       "...                     ...                             ...   \n",
       "5376                    0.0                             1.0   \n",
       "4560                    0.0                             0.0   \n",
       "4276                    1.0                             0.0   \n",
       "7584                    0.0                             0.0   \n",
       "3688                    0.0                             0.0   \n",
       "\n",
       "      smoking_status_never_smoked  smoking_status_unknown  stroke  \n",
       "0                             0.0                     0.0     1.0  \n",
       "1                             1.0                     0.0     1.0  \n",
       "2                             0.0                     0.0     1.0  \n",
       "3                             1.0                     0.0     1.0  \n",
       "4                             0.0                     0.0     1.0  \n",
       "...                           ...                     ...     ...  \n",
       "5376                          0.0                     0.0     1.0  \n",
       "4560                          1.0                     0.0     1.0  \n",
       "4276                          0.0                     0.0     1.0  \n",
       "7584                          1.0                     0.0     1.0  \n",
       "3688                          1.0                     0.0     1.0  \n",
       "\n",
       "[6909 rows x 20 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at our new dataframe.  We should have 6,909 records (the\n",
    "# original 4,909 + 2,000 additional ones for class 1 (positive)) and 20 columns\n",
    "augmented_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positives : 2209\n",
      "Negatives : 4700\n"
     ]
    }
   ],
   "source": [
    "# Let's also check that the class splits are as expected (we should have 2,209\n",
    "# positives, and 4,700 negatives)\n",
    "number_positive_stroke = np.sum(augmented_data['stroke'] == 1)\n",
    "number_negative_stroke = np.sum(augmented_data['stroke'] == 0)\n",
    "\n",
    "print (f\"Positives : {number_positive_stroke}\")\n",
    "print (f\"Negatives : {number_negative_stroke}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = augmented_data.drop('stroke',axis=1) # X = all 'data' except the 'stroke' column\n",
    "y = augmented_data['stroke'] # y = 'stroke' column from 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std, X_test_std = standardise_data(X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  /* Definition of color scheme for fitted estimators */\n",
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       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train_std,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict training and test set labels\n",
    "y_pred_train = model.predict(X_train_std)\n",
    "y_pred_test = model.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of predicting training data = 0.794\n",
      "Accuracy of predicting test data = 0.789\n"
     ]
    }
   ],
   "source": [
    "accuracy_train = np.mean(y_pred_train == y_train)\n",
    "accuracy_test = np.mean(y_pred_test == y_test)\n",
    "\n",
    "print (f'Accuracy of predicting training data = {accuracy_train:0.3f}')\n",
    "print (f'Accuracy of predicting test data = {accuracy_test:0.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like adding 2,000 extra synthetic datapoints to our positive class has worsened model performance in this instance.  We probably didn't need to do in this example - we already had very good performance.  And 2,000 may have been too many to generate from a sample of around 200."
   ]
  }
 ],
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