Probability of diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from utils.EpochDots import EpochDots\n",
    "from utils.HistoryPlotter import HistoryPlotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2.2.0\n"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n0              6      148             72             35        0  33.6   \n1              1       85             66             29        0  26.6   \n2              8      183             64              0        0  23.3   \n3              1       89             66             23       94  28.1   \n4              0      137             40             35      168  43.1   \n..           ...      ...            ...            ...      ...   ...   \n763           10      101             76             48      180  32.9   \n764            2      122             70             27        0  36.8   \n765            5      121             72             23      112  26.2   \n766            1      126             60              0        0  30.1   \n767            1       93             70             31        0  30.4   \n\n     DiabetesPedigreeFunction  Age  Outcome  \n0                       0.627   50        1  \n1                       0.351   31        0  \n2                       0.672   32        1  \n3                       0.167   21        0  \n4                       2.288   33        1  \n..                        ...  ...      ...  \n763                     0.171   63        0  \n764                     0.340   27        0  \n765                     0.245   30        0  \n766                     0.349   47        1  \n767                     0.315   23        0  \n\n[768 rows x 9 columns]",
      "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>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</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    </tr>\n    <tr>\n      <th>763</th>\n      <td>10</td>\n      <td>101</td>\n      <td>76</td>\n      <td>48</td>\n      <td>180</td>\n      <td>32.9</td>\n      <td>0.171</td>\n      <td>63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>764</th>\n      <td>2</td>\n      <td>122</td>\n      <td>70</td>\n      <td>27</td>\n      <td>0</td>\n      <td>36.8</td>\n      <td>0.340</td>\n      <td>27</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>765</th>\n      <td>5</td>\n      <td>121</td>\n      <td>72</td>\n      <td>23</td>\n      <td>112</td>\n      <td>26.2</td>\n      <td>0.245</td>\n      <td>30</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>766</th>\n      <td>1</td>\n      <td>126</td>\n      <td>60</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.1</td>\n      <td>0.349</td>\n      <td>47</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>767</th>\n      <td>1</td>\n      <td>93</td>\n      <td>70</td>\n      <td>31</td>\n      <td>0</td>\n      <td>30.4</td>\n      <td>0.315</td>\n      <td>23</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>768 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "raw_dataset = pd.read_csv('/Users/theodoretan/.keras/datasets/diabetes-dataset.csv')\n",
    "dataset = raw_dataset.copy()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Pregnancies                 0\nGlucose                     0\nBloodPressure               0\nSkinThickness               0\nInsulin                     0\nBMI                         0\nDiabetesPedigreeFunction    0\nAge                         0\nOutcome                     0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "# training data points: 614 \n# test data points: 154\n"
    }
   ],
   "source": [
    "train_data = dataset.sample(frac=0.8, random_state=0)\n",
    "test_data = dataset.drop(train_data.index)\n",
    "print('# training data points:', len(train_data.index), '\\n# test data points:', len(test_data.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "_ = sns.pairplot(train_data[['Outcome', 'Glucose', 'Insulin', 'BloodPressure']], hue='Outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "9      1\n11     1\n19     1\n23     1\n28     0\n      ..\n746    1\n753    1\n754    1\n759    1\n763    0\nName: Outcome, Length: 154, dtype: int64"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "train_labels = train_data.pop('Outcome')\n",
    "test_labels = test_data.pop('Outcome')\n",
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                          count        mean         std     min        25%  \\\nPregnancies               614.0    3.863192    3.382738   0.000    1.00000   \nGlucose                   614.0  121.636808   32.016768   0.000  100.00000   \nBloodPressure             614.0   68.576547   19.911136   0.000   62.00000   \nSkinThickness             614.0   20.314332   16.082796   0.000    0.00000   \nInsulin                   614.0   78.301303  115.537423   0.000    0.00000   \nBMI                       614.0   32.106678    7.712639   0.000   27.40000   \nDiabetesPedigreeFunction  614.0    0.475573    0.329461   0.078    0.24525   \nAge                       614.0   33.364821   11.593037  21.000   24.00000   \n\n                              50%        75%     max  \nPregnancies                 3.000    6.00000   17.00  \nGlucose                   118.500  140.75000  199.00  \nBloodPressure              72.000   80.00000  122.00  \nSkinThickness              23.000   32.00000   99.00  \nInsulin                     7.000  127.75000  846.00  \nBMI                        32.150   36.57500   59.40  \nDiabetesPedigreeFunction    0.377    0.63925    2.42  \nAge                        29.000   41.00000   72.00  ",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Pregnancies</th>\n      <td>614.0</td>\n      <td>3.863192</td>\n      <td>3.382738</td>\n      <td>0.000</td>\n      <td>1.00000</td>\n      <td>3.000</td>\n      <td>6.00000</td>\n      <td>17.00</td>\n    </tr>\n    <tr>\n      <th>Glucose</th>\n      <td>614.0</td>\n      <td>121.636808</td>\n      <td>32.016768</td>\n      <td>0.000</td>\n      <td>100.00000</td>\n      <td>118.500</td>\n      <td>140.75000</td>\n      <td>199.00</td>\n    </tr>\n    <tr>\n      <th>BloodPressure</th>\n      <td>614.0</td>\n      <td>68.576547</td>\n      <td>19.911136</td>\n      <td>0.000</td>\n      <td>62.00000</td>\n      <td>72.000</td>\n      <td>80.00000</td>\n      <td>122.00</td>\n    </tr>\n    <tr>\n      <th>SkinThickness</th>\n      <td>614.0</td>\n      <td>20.314332</td>\n      <td>16.082796</td>\n      <td>0.000</td>\n      <td>0.00000</td>\n      <td>23.000</td>\n      <td>32.00000</td>\n      <td>99.00</td>\n    </tr>\n    <tr>\n      <th>Insulin</th>\n      <td>614.0</td>\n      <td>78.301303</td>\n      <td>115.537423</td>\n      <td>0.000</td>\n      <td>0.00000</td>\n      <td>7.000</td>\n      <td>127.75000</td>\n      <td>846.00</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>614.0</td>\n      <td>32.106678</td>\n      <td>7.712639</td>\n      <td>0.000</td>\n      <td>27.40000</td>\n      <td>32.150</td>\n      <td>36.57500</td>\n      <td>59.40</td>\n    </tr>\n    <tr>\n      <th>DiabetesPedigreeFunction</th>\n      <td>614.0</td>\n      <td>0.475573</td>\n      <td>0.329461</td>\n      <td>0.078</td>\n      <td>0.24525</td>\n      <td>0.377</td>\n      <td>0.63925</td>\n      <td>2.42</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>614.0</td>\n      <td>33.364821</td>\n      <td>11.593037</td>\n      <td>21.000</td>\n      <td>24.00000</td>\n      <td>29.000</td>\n      <td>41.00000</td>\n      <td>72.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "train_stats = train_data.describe().transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                          count          mean  std       min       25%  \\\nPregnancies               614.0  3.688689e-17  1.0 -1.142031 -0.846413   \nGlucose                   614.0  1.909439e-16  1.0 -3.799160 -0.675796   \nBloodPressure             614.0  2.256610e-16  1.0 -3.444130 -0.330295   \nSkinThickness             614.0 -4.050325e-17  1.0 -1.263109 -1.263109   \nInsulin                   614.0 -6.943414e-17  1.0 -0.677714 -0.677714   \nBMI                       614.0 -2.430195e-16  1.0 -4.162865 -0.610255   \nDiabetesPedigreeFunction  614.0 -6.943414e-17  1.0 -1.206738 -0.699091   \nAge                       614.0 -1.157236e-17  1.0 -1.066573 -0.807797   \n\n                               50%       75%       max  \nPregnancies              -0.255176  0.631680  3.883484  \nGlucose                  -0.097974  0.596974  2.416334  \nBloodPressure             0.171937  0.573722  2.683094  \nSkinThickness             0.166990  0.726594  4.892536  \nInsulin                  -0.617127  0.427989  6.644589  \nBMI                       0.005617  0.579351  3.538779  \nDiabetesPedigreeFunction -0.299196  0.496801  5.901840  \nAge                      -0.376504  0.658600  3.332619  ",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Pregnancies</th>\n      <td>614.0</td>\n      <td>3.688689e-17</td>\n      <td>1.0</td>\n      <td>-1.142031</td>\n      <td>-0.846413</td>\n      <td>-0.255176</td>\n      <td>0.631680</td>\n      <td>3.883484</td>\n    </tr>\n    <tr>\n      <th>Glucose</th>\n      <td>614.0</td>\n      <td>1.909439e-16</td>\n      <td>1.0</td>\n      <td>-3.799160</td>\n      <td>-0.675796</td>\n      <td>-0.097974</td>\n      <td>0.596974</td>\n      <td>2.416334</td>\n    </tr>\n    <tr>\n      <th>BloodPressure</th>\n      <td>614.0</td>\n      <td>2.256610e-16</td>\n      <td>1.0</td>\n      <td>-3.444130</td>\n      <td>-0.330295</td>\n      <td>0.171937</td>\n      <td>0.573722</td>\n      <td>2.683094</td>\n    </tr>\n    <tr>\n      <th>SkinThickness</th>\n      <td>614.0</td>\n      <td>-4.050325e-17</td>\n      <td>1.0</td>\n      <td>-1.263109</td>\n      <td>-1.263109</td>\n      <td>0.166990</td>\n      <td>0.726594</td>\n      <td>4.892536</td>\n    </tr>\n    <tr>\n      <th>Insulin</th>\n      <td>614.0</td>\n      <td>-6.943414e-17</td>\n      <td>1.0</td>\n      <td>-0.677714</td>\n      <td>-0.677714</td>\n      <td>-0.617127</td>\n      <td>0.427989</td>\n      <td>6.644589</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>614.0</td>\n      <td>-2.430195e-16</td>\n      <td>1.0</td>\n      <td>-4.162865</td>\n      <td>-0.610255</td>\n      <td>0.005617</td>\n      <td>0.579351</td>\n      <td>3.538779</td>\n    </tr>\n    <tr>\n      <th>DiabetesPedigreeFunction</th>\n      <td>614.0</td>\n      <td>-6.943414e-17</td>\n      <td>1.0</td>\n      <td>-1.206738</td>\n      <td>-0.699091</td>\n      <td>-0.299196</td>\n      <td>0.496801</td>\n      <td>5.901840</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>614.0</td>\n      <td>-1.157236e-17</td>\n      <td>1.0</td>\n      <td>-1.066573</td>\n      <td>-0.807797</td>\n      <td>-0.376504</td>\n      <td>0.658600</td>\n      <td>3.332619</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "def normalize(data):\n",
    "    return (data - train_stats['mean']) / train_stats['std']\n",
    "\n",
    "norm_train_data = normalize(train_data)\n",
    "norm_test_data = normalize(test_data)\n",
    "norm_train_data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the tensorflow graph\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    # logistic regression is a single layer neural network with one output\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(1, activation='sigmoid', input_dim=train_data.shape[1]),\n",
    "    ])\n",
    "\n",
    "    # binary because 0/1, if multiple then categorical crossentropy is appropriate\n",
    "    model.compile(\n",
    "        optimizer='rmsprop', \n",
    "        loss='binary_crossentropy', \n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Model: \"sequential_8\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_9 (Dense)              (None, 1)                 9         \n=================================================================\nTotal params: 9\nTrainable params: 9\nNon-trainable params: 0\n_________________________________________________________________\n"
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[0.7042456 ],\n       [0.54654557],\n       [0.341304  ],\n       [0.35318643],\n       [0.09696546],\n       [0.33620912],\n       [0.7444562 ],\n       [0.3795278 ],\n       [0.0159909 ],\n       [0.697039  ]], dtype=float32)"
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "model.predict(norm_train_data[:10])"
   ]
  },
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   "execution_count": 54,
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nEpoch: 0, accuracy:0.5804,  loss:0.8204,  val_accuracy:0.5772,  val_loss:0.7872,  \n....................................................................................................\nEpoch: 100, accuracy:0.7963,  loss:0.4549,  val_accuracy:0.7073,  val_loss:0.5523,  \n....................................................................................................\nEpoch: 200, accuracy:0.7984,  loss:0.4514,  val_accuracy:0.7073,  val_loss:0.5629,  \n....................................................................................................\nEpoch: 300, accuracy:0.8004,  loss:0.4513,  val_accuracy:0.6992,  val_loss:0.5649,  \n....................................................................................................\nEpoch: 400, accuracy:0.7963,  loss:0.4514,  val_accuracy:0.7073,  val_loss:0.5660,  \n...................................................................................................."
    }
   ],
   "source": [
    "EPOCHS = 500\n",
    "history = model.fit(norm_train_data, train_labels, epochs=EPOCHS, validation_split=0.2, verbose=0, callbacks=[EpochDots()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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      "text/plain": "         loss  accuracy  val_loss  val_accuracy  epoch\n495  0.451352  0.796334  0.567497      0.707317    495\n496  0.451353  0.796334  0.567624      0.707317    496\n497  0.451366  0.796334  0.567579      0.707317    497\n498  0.451345  0.796334  0.567523      0.707317    498\n499  0.451318  0.798371  0.567525      0.707317    499",
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     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df['epoch'] = history.epoch\n",
    "history_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0, 0.5, 'BCE,Accuracy to Diabetes Outcome')"
     },
     "metadata": {},
     "execution_count": 59
    },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plotter = HistoryPlotter(smoothing_std=2)\n",
    "plotter.plot({'Basic BCE': history}, metric='loss')\n",
    "plotter.plot({'Basic Accuracy': history}, metric='accuracy')\n",
    "\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('BCE,Accuracy to Diabetes Outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nEpoch: 0, accuracy:0.4786,  loss:0.9730,  val_accuracy:0.5122,  val_loss:0.8444,  \n............................................................................................"
    }
   ],
   "source": [
    "# retry with early stopping again\n",
    "model = build_model()\n",
    "\n",
    "# patience is num epochs to check for improvement\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "# try to fit model again\n",
    "early_history = model.fit(norm_train_data, train_labels, epochs=EPOCHS, validation_split=0.2, verbose=0, callbacks=[early_stop, EpochDots()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0, 0.5, 'BCE,Accuracy to Diabetes Outcome')"
     },
     "metadata": {},
     "execution_count": 70
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plotter = HistoryPlotter(smoothing_std=2)\n",
    "plotter.plot({'Early Stop BCE': early_history}, metric='loss')\n",
    "plotter.plot({'Early Stop Accuracy': early_history}, metric='accuracy')\n",
    "\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('BCE,Accuracy to Diabetes Outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "154/154 [==============================] - 0s 624us/step - loss: 0.4960 - accuracy: 0.7727\nTesting set accuracy:  0.77%\n"
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(norm_test_data, test_labels, batch_size=1, verbose=1)\n",
    "print(\"Testing set accuracy: {:5.2f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(norm_test_data).flatten()\n",
    "test_labels.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_df = pd.concat([pd.Series(test_predictions, name='Probability'), test_labels], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mark_correct(row):\n",
    "    return (row['Probability'] >= 0.5 and row['Outcome'] == 1) or (row['Probability'] < 0.5 and row['Outcome'] == 0) \n",
    "compare_df['Correct'] = compare_df.apply(lambda row: mark_correct(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "correct, incorrect = compare_df['Correct'].value_counts()\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie([correct, incorrect], labels=['Correct', 'Incorrect'], autopct='%1.1f%%', shadow=True, colors=['green', 'red'])\n",
    "ax1.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "tf-keras-test",
   "display_name": "tf-keras-test"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}