neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26525dd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-09 13:59:21.520483: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import sklearn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a760af4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(context='talk', style='white', rc={'figure.facecolor':'white'}, font_scale=1)\n",
    "sns.set_style('ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb161ed1",
   "metadata": {},
   "source": [
    "# read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "770e691f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>12</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>105</td>\n",
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       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>121</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>251</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>224</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>577</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1  2  3  4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
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       "3     6  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "5     1  1  0  0  6  0  0  0  0  0  ...      0     13      2      0      0   \n",
       "9     1  0  0  0  0  0  0  0  0  0  ...      0     47      0      0      0   \n",
       "20    0  0  0  0  0  0  0  0  0  0  ...      0     28      0      0      0   \n",
       "22    0  2  0  0  0  0  0  0  0  0  ...      0      4      0      0      0   \n",
       "25    0  0  0  0  3  0  0  0  0  0  ...      0     26      0      0      0   \n",
       "27    0  0  0  0  0  0  0  0  0  0  ...      0     11      0      0      0   \n",
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       "44   11  0  0  0  0  0  0  0  0  0  ...      0     36      0      0      0   \n",
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       "83    0  0  0  0  0  0  0  0  0  1  ...      0    224      0      0      0   \n",
       "89    0  0  0  0  0  0  0  0  0  0  ...      0      8      0      0      0   \n",
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       "108   0  1  0  0  1  0  0  0  0  1  ...      0     34      0      0      0   \n",
       "114  31  0  0  0  0  0  0  0  0  0  ...      0     79      0      0      0   \n",
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       "118   0  0  0  0  2  0  0  0  0  4  ...      0     33      7      0      0   \n",
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       "130   0  4  0  0  0  0  0  0  0  0  ...      0     75      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735               disease  \n",
       "2        7    132      1      0          Preeclampsia  \n",
       "3        0     47      0      0          Preeclampsia  \n",
       "5        2     76      0      0          Preeclampsia  \n",
       "9        0     30      0      0          Preeclampsia  \n",
       "20      10    111     11      0          Preeclampsia  \n",
       "22       0     65      0      0          Preeclampsia  \n",
       "25       0    160      0      0          Preeclampsia  \n",
       "27       0     83      0      0          Preeclampsia  \n",
       "43       0     65      0      0  Gestational-Diabetes  \n",
       "44       4     95      1      0  Gestational-Diabetes  \n",
       "49       2     82      0      0  Gestational-Diabetes  \n",
       "56       8    131      0      0  Gestational-Diabetes  \n",
       "66      19    105      3      0  Gestational-Diabetes  \n",
       "70       0     36      0      0  Gestational-Diabetes  \n",
       "71       0     70      0      0  Gestational-Diabetes  \n",
       "83      28    371      8      0      Normal-Pregnancy  \n",
       "89       0    146      0      0      Normal-Pregnancy  \n",
       "92       0     44      0      0      Normal-Pregnancy  \n",
       "99       3    121      8      0      Normal-Pregnancy  \n",
       "108      4    153      0      0      Normal-Pregnancy  \n",
       "114     34    251      8      0      Normal-Pregnancy  \n",
       "117     10    577     13      0      Normal-Pregnancy  \n",
       "118     18    116      0      0      Normal-Pregnancy  \n",
       "124      8     73      4      0  Chronic-Hypertension  \n",
       "130     30    134      0      0  Chronic-Hypertension  \n",
       "\n",
       "[25 rows x 57737 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_trimester_data = pd.read_csv(\"first_trimester_data.txt\", sep=\"\\t\").T\n",
    "first_trimester_diseases = pd.read_csv(\"first_trimester_diseases.txt\", sep=\"\\t\")\n",
    "first_trimester_data[\"disease\"] = first_trimester_diseases[\"disease\"].values\n",
    "first_trimester_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b481db02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>38</td>\n",
       "      <td>0</td>\n",
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       "      <th>34</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
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       "      <td>0</td>\n",
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       "      <td>39</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>155</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0   1  2  3   4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
       "0     3   9  0  0   0  0  0  0  0  0  ...      0     45      3      0      0   \n",
       "4    19   0  0  0   0  0  0  0  0  0  ...      0     41      0      0      0   \n",
       "10    0   0  0  0   0  0  0  0  0  0  ...      0     35      0      0      0   \n",
       "13    1   0  0  0   0  0  0  0  0  3  ...      0     56      0      0      0   \n",
       "23    9   0  0  0   0  0  0  0  0  2  ...      0     31      0      0      0   \n",
       "24    0   8  0  0   0  0  0  0  0  0  ...      0     38      0      0      0   \n",
       "31    0   0  0  0   3  0  0  0  0  0  ...      0     31      0      0      0   \n",
       "34    1   0  0  0   8  0  0  0  0  0  ...      0     38      1      0      0   \n",
       "42    0   5  0  0   0  0  0  0  0  0  ...      0     72      0      0      0   \n",
       "48    0   0  0  0   2  0  0  0  0  0  ...      0     16      0      0      0   \n",
       "57    2   0  0  0  15  0  0  0  0  1  ...      0     39      0      0      0   \n",
       "58    0   0  0  0   9  1  0  0  0  0  ...      0     14      0      0      0   \n",
       "60    0   0  0  0   0  0  0  0  0  0  ...      0     58      0      0      0   \n",
       "63    0   0  0  0   0  0  0  0  0  0  ...      0     61      0      0      0   \n",
       "65    0   0  0  0   0  0  0  0  0  1  ...      0     17      0      0      0   \n",
       "82    0  12  0  0   0  0  0  0  0  0  ...      0      8      0      0      0   \n",
       "87    0   0  0  0   3  0  0  0  0  0  ...      0     23      0      0      2   \n",
       "88    0   0  0  0   0  0  0  0  0  0  ...      1     28      0      0      0   \n",
       "97    0   0  0  0   0  0  0  0  0  0  ...      0      5      0      0      0   \n",
       "98    0   0  0  0   1  0  0  0  0  0  ...      0     28      0      0      0   \n",
       "104   2   1  0  0   0  0  0  0  0  1  ...      0    102      0      0      0   \n",
       "105   0   0  0  0   5  0  0  0  0  0  ...      0     33      0      0      0   \n",
       "121   0   0  0  0   2  0  0  0  0  0  ...      0      3      3      0      0   \n",
       "122   4   0  0  0   0  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "125   0   0  0  0   0  0  0  0  0  0  ...      0     88      0      0      0   \n",
       "129   0   0  0  0  11  2  0  0  0  0  ...      1     16      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735               disease  \n",
       "0        0    135      0      0          Preeclampsia  \n",
       "4        0     20      0      0          Preeclampsia  \n",
       "10       1    112      7      0          Preeclampsia  \n",
       "13      21    171      0      0          Preeclampsia  \n",
       "23       1    107      0      0          Preeclampsia  \n",
       "24       0     98      0      0          Preeclampsia  \n",
       "31      13    135      0      0          Preeclampsia  \n",
       "34       6     74      2      0          Preeclampsia  \n",
       "42       0    110      0      0  Gestational-Diabetes  \n",
       "48      10     76      0      0  Gestational-Diabetes  \n",
       "57       8    146      1      0  Gestational-Diabetes  \n",
       "58       4     49      3      0  Gestational-Diabetes  \n",
       "60       0      1      0      0  Gestational-Diabetes  \n",
       "63       0    102      0      0  Gestational-Diabetes  \n",
       "65       1    157      0      0  Gestational-Diabetes  \n",
       "82       0     60      0      0      Normal-Pregnancy  \n",
       "87       1     90      0      0      Normal-Pregnancy  \n",
       "88      12    109      0      0      Normal-Pregnancy  \n",
       "97       8     97      0      0      Normal-Pregnancy  \n",
       "98      18     97      0      0      Normal-Pregnancy  \n",
       "104     13    208      0      0      Normal-Pregnancy  \n",
       "105     25    173      0      0      Normal-Pregnancy  \n",
       "121      2     27      1      0      Normal-Pregnancy  \n",
       "122     59    274      0      0      Normal-Pregnancy  \n",
       "125     28    155      7      0  Chronic-Hypertension  \n",
       "129      2     90      0      0  Chronic-Hypertension  \n",
       "\n",
       "[26 rows x 57737 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_trimester_data = pd.read_csv(\"second_trimester_data.txt\", sep=\"\\t\").T\n",
    "second_trimester_diseases = pd.read_csv(\"second_trimester_diseases.txt\", sep=\"\\t\")\n",
    "second_trimester_data[\"disease\"] = second_trimester_diseases[\"disease\"].values\n",
    "second_trimester_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20fdc668",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>57731</th>\n",
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       "      <th>57733</th>\n",
       "      <th>57734</th>\n",
       "      <th>57735</th>\n",
       "      <th>disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Preeclampsia</td>\n",
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       "      <th>12</th>\n",
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       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>9</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
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       "      <td>42</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>74</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Preeclampsia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gestational-Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal-Pregnancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Chronic-Hypertension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1  2  3   4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
       "11    0  0  0  0   0  0  0  3  0  0  ...      0      5      0      0      0   \n",
       "12    0  0  1  0   5  0  0  2  0  2  ...      0     35      0      0      1   \n",
       "14    0  4  0  0   0  0  0  0  0  0  ...      0     42      0      0      0   \n",
       "15    1  0  0  0   2  0  0  0  0  2  ...      2     59      0      0      0   \n",
       "29    0  1  0  0   0  5  1  2  0  0  ...      4     83      0      0      4   \n",
       "33    0  0  0  0   0  0  0  0  0  0  ...      0     51      0      0      0   \n",
       "35   34  2  0  0   0  0  0  0  0  4  ...      0     11      0      0      0   \n",
       "36    0  0  0  0   0  0  0  0  0  2  ...      8     47      0      0      0   \n",
       "40    1  0  0  0   0  0  0  0  0  0  ...      0     20      0      0      0   \n",
       "46    2  8  0  0   0  0  0  0  0  0  ...      0     29      0      0      0   \n",
       "55    1  0  0  0   0  0  0  0  0  1  ...      0     18      0      0      0   \n",
       "68   43  0  0  0   9  0  0  0  0  1  ...      0      2      0      0      0   \n",
       "69    4  1  0  0   4  0  0  0  0  1  ...      3     22      0      0      0   \n",
       "72    0  5  0  0   1  0  0  0  0  0  ...      0     18      0      0      0   \n",
       "81    0  0  0  0   0  0  0  0  0  1  ...      0      0      0      0      0   \n",
       "86    6  0  0  0   0  0  0  0  0  0  ...      0      9      0      0      0   \n",
       "96    0  2  0  0   0  0  0  0  0  0  ...      0     53      0      0      0   \n",
       "101   0  2  0  0   0  0  0  0  0  2  ...      0    272      0      0      0   \n",
       "102  20  2  0  0   0  0  0  7  0  0  ...      0      8      0      0      0   \n",
       "106   0  0  0  0  10  0  0  0  0  0  ...      0     52      1      0      0   \n",
       "107   0  1  0  0   0  0  0  0  0  0  ...      0    130      0      0      0   \n",
       "112   1  0  0  0   0  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "115   0  0  0  0   0  0  0  4  0  0  ...      0     27      0      0      1   \n",
       "127   0  0  0  0   0  0  0  1  0  0  ...      0     59      0      0      0   \n",
       "131   0  2  0  0   3  1  0  2  0  0  ...      0     30      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735               disease  \n",
       "11       3     46      1      0          Preeclampsia  \n",
       "12       9    127      0      0          Preeclampsia  \n",
       "14       0    213      3      0          Preeclampsia  \n",
       "15      11    123      0      0          Preeclampsia  \n",
       "29      13     74     10      0          Preeclampsia  \n",
       "33       6    175      0      0          Preeclampsia  \n",
       "35       1    109      1      0          Preeclampsia  \n",
       "36       1    122      0      0          Preeclampsia  \n",
       "40       3     71      0      0  Gestational-Diabetes  \n",
       "46       0    116      0      0  Gestational-Diabetes  \n",
       "55       0     37      0      0  Gestational-Diabetes  \n",
       "68       5     77      0      0  Gestational-Diabetes  \n",
       "69       0     50      5      0  Gestational-Diabetes  \n",
       "72       1     69      0      0  Gestational-Diabetes  \n",
       "81       0      0      0      0      Normal-Pregnancy  \n",
       "86       0     40      0      0      Normal-Pregnancy  \n",
       "96       3    102      1      0      Normal-Pregnancy  \n",
       "101     30    570      0      0      Normal-Pregnancy  \n",
       "102      5     96      0      0      Normal-Pregnancy  \n",
       "106     26     70      2      0      Normal-Pregnancy  \n",
       "107     26    422      0      0      Normal-Pregnancy  \n",
       "112      5    205      8      0      Normal-Pregnancy  \n",
       "115      5    116      1      0      Normal-Pregnancy  \n",
       "127      3    146      0      0  Chronic-Hypertension  \n",
       "131      5    139      3      0  Chronic-Hypertension  \n",
       "\n",
       "[25 rows x 57737 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_trimester_data = pd.read_csv(\"third_trimester_data.txt\", sep=\"\\t\").T\n",
    "third_trimester_diseases = pd.read_csv(\"third_trimester_diseases.txt\", sep=\"\\t\")\n",
    "third_trimester_data[\"disease\"] = third_trimester_diseases[\"disease\"].values\n",
    "third_trimester_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64bd0b3",
   "metadata": {},
   "source": [
    "# first trimester data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "577564ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>121</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>251</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>224</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>577</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1  2  3  4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
       "2     0  1  0  0  0  0  0  0  0  0  ...      0     17      0      0      0   \n",
       "3     6  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "5     1  1  0  0  6  0  0  0  0  0  ...      0     13      2      0      0   \n",
       "9     1  0  0  0  0  0  0  0  0  0  ...      0     47      0      0      0   \n",
       "20    0  0  0  0  0  0  0  0  0  0  ...      0     28      0      0      0   \n",
       "22    0  2  0  0  0  0  0  0  0  0  ...      0      4      0      0      0   \n",
       "25    0  0  0  0  3  0  0  0  0  0  ...      0     26      0      0      0   \n",
       "27    0  0  0  0  0  0  0  0  0  0  ...      0     11      0      0      0   \n",
       "43    0  5  0  0  0  0  0  0  0  1  ...      0     24      0      0      0   \n",
       "44   11  0  0  0  0  0  0  0  0  0  ...      0     36      0      0      0   \n",
       "49    0  1  0  0  2  0  0  1  0  2  ...      0     39      4      0      2   \n",
       "56    0  0  0  0  6  0  0  0  0  0  ...      3     19      0      0      1   \n",
       "66   13  0  0  0  4  0  0  0  0  1  ...      0     12      0      0      0   \n",
       "70    0  0  0  0  1  0  0  0  0  1  ...      0     16      0      0      0   \n",
       "71    8  3  0  0  0  0  0  0  0  0  ...      0     10      0      0      0   \n",
       "83    0  0  0  0  0  0  0  0  0  1  ...      0    224      0      0      0   \n",
       "89    0  0  0  0  0  0  0  0  0  0  ...      0      8      0      0      0   \n",
       "92    0  0  0  0  0  0  0  0  0  0  ...      0      8      0      0      0   \n",
       "99    0  0  0  0  5  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "108   0  1  0  0  1  0  0  0  0  1  ...      0     34      0      0      0   \n",
       "114  31  0  0  0  0  0  0  0  0  0  ...      0     79      0      0      0   \n",
       "117  31  2  0  0  0  0  0  0  0  5  ...      8    224      9      1      1   \n",
       "118   0  0  0  0  2  0  0  0  0  4  ...      0     33      7      0      0   \n",
       "124   0  0  0  0  0  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "130   0  4  0  0  0  0  0  0  0  0  ...      0     75      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735  adverse_pregnancy  \n",
       "2        7    132      1      0                  1  \n",
       "3        0     47      0      0                  1  \n",
       "5        2     76      0      0                  1  \n",
       "9        0     30      0      0                  1  \n",
       "20      10    111     11      0                  1  \n",
       "22       0     65      0      0                  1  \n",
       "25       0    160      0      0                  1  \n",
       "27       0     83      0      0                  1  \n",
       "43       0     65      0      0                  1  \n",
       "44       4     95      1      0                  1  \n",
       "49       2     82      0      0                  1  \n",
       "56       8    131      0      0                  1  \n",
       "66      19    105      3      0                  1  \n",
       "70       0     36      0      0                  1  \n",
       "71       0     70      0      0                  1  \n",
       "83      28    371      8      0                  0  \n",
       "89       0    146      0      0                  0  \n",
       "92       0     44      0      0                  0  \n",
       "99       3    121      8      0                  0  \n",
       "108      4    153      0      0                  0  \n",
       "114     34    251      8      0                  0  \n",
       "117     10    577     13      0                  0  \n",
       "118     18    116      0      0                  0  \n",
       "124      8     73      4      0                  1  \n",
       "130     30    134      0      0                  1  \n",
       "\n",
       "[25 rows x 57737 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_trimester_data[\"adverse_pregnancy\"] = np.where(first_trimester_data.disease.str.contains(\"Normal-Pregnancy\"), 0, 1)\n",
    "binary_data_firstT = first_trimester_data.drop([\"disease\"], axis=1)\n",
    "binary_data_firstT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75e40e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preeclampsia_dif_exp_firstT = pd.read_csv(\"preeclamspsia_diffe_firstT.txt\", sep=\" \")\n",
    "preeclampsia_dif_exp_firstT = preeclampsia_dif_exp_firstT[preeclampsia_dif_exp_firstT[\"PValue\"] <= 0.05]\n",
    "preeclampsia_dif_exp_firstT = list(preeclampsia_dif_exp_firstT.index)\n",
    "\n",
    "gestational_diabetes_dif_exp_firstT = pd.read_csv(\"gestational_diabetes_diffe_firstT.txt\", sep=\" \")\n",
    "gestational_diabetes_dif_exp_firstT = gestational_diabetes_dif_exp_firstT[gestational_diabetes_dif_exp_firstT[\"PValue\"] <= 0.05]\n",
    "gestational_diabetes_dif_exp_firstT = list(gestational_diabetes_dif_exp_firstT.index)\n",
    "\n",
    "chronic_hypertension_dif_exp_firstT = pd.read_csv(\"chronic_hypertension_diffe_firstT.txt\", sep=\" \")\n",
    "chronic_hypertension_dif_exp_firstT = chronic_hypertension_dif_exp_firstT[chronic_hypertension_dif_exp_firstT[\"PValue\"] <= 0.05]\n",
    "chronic_hypertension_dif_exp_firstT = list(chronic_hypertension_dif_exp_firstT.index)\n",
    "\n",
    "firstT_diffe_list = list(set(preeclampsia_dif_exp_firstT + gestational_diabetes_dif_exp_firstT + chronic_hypertension_dif_exp_firstT))\n",
    "firstT_diffe_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "faf310ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>802</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 4612 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     40960  32771  40963  49160  10  57354  40971  24589  15  16401  ...  \\\n",
       "2        1      1      0      0   7     15      0      0   0     13  ...   \n",
       "3        1      0      0      0   9      3      0      0   0      9  ...   \n",
       "5        3      0      0      0   2      5      0      7   0     25  ...   \n",
       "9        0      0      0      0   1      0      0      0   0      5  ...   \n",
       "20       0      0      0      0   0      0      0      0   0      5  ...   \n",
       "22       0      0      0      0   0      0      0      0   0      3  ...   \n",
       "25       1      0      0      0   0      9      0      0   0     20  ...   \n",
       "27       2      0      0      0   0      0      0      0   0     11  ...   \n",
       "43       2      0      0      0   1      1      0      0   0     18  ...   \n",
       "44       2      1      0      0   2      6      1      0   0     13  ...   \n",
       "49       3      0      0      0   0      3      0      0   0     25  ...   \n",
       "56       0      2      0      0   0     11      0      1   0      6  ...   \n",
       "66       0      0      0      0   5      1      0      0   0     17  ...   \n",
       "70       0      0      0      0   0     16      0      0   0      0  ...   \n",
       "71       0      0      0      0   0      0      0      0   0      0  ...   \n",
       "83       1      0      0      0  20      1      0      0   0      3  ...   \n",
       "89       0      0      0      0   0      0      0      0   0      6  ...   \n",
       "92       0      0      0      0   0      0      0      0   0      0  ...   \n",
       "99       0      4      0      0   0      0      0      0   0      8  ...   \n",
       "108      0      0      0      0   0      0      0      0   0     13  ...   \n",
       "114      0      0      0      0   6      9      0      0   0     16  ...   \n",
       "117      0      0      0      0  31      1      0      0   0     11  ...   \n",
       "118     12      0      0      0   1     23      0      0   0     29  ...   \n",
       "124      0      2      0      0   0      1      0      0   0     19  ...   \n",
       "130      0      0      0      0   0      0      0      0   0      6  ...   \n",
       "\n",
       "     57325  49133  57333  49142  8186  8187  40956  16381  8190  \\\n",
       "2        3      0     27      0    61     0      0      0     0   \n",
       "3        0      0      0      0    22     2      0      0     0   \n",
       "5        4      1     20      0    41     1      0      0     0   \n",
       "9        0      0      2      0    25     6      0      0     0   \n",
       "20       0      0      1      0    58     1      0      0     0   \n",
       "22       0      0      0      0    20     0      0      0     0   \n",
       "25       0      0     10      0    55     4      0      0     0   \n",
       "27       0      0     19      0    59     0      0      0     0   \n",
       "43       0      0     21      0    72     0      0      0     0   \n",
       "44       3      0      6      0    65     1      0      0     0   \n",
       "49       0      0     10      0    39     3      0      0     0   \n",
       "56       1      0     22      0    91     0      1      0     0   \n",
       "66       0      0      2      0   134     3      0      0     0   \n",
       "70       0      0      0      0    75     2      0      0     0   \n",
       "71       0      0      9      0   109     1      0      0     0   \n",
       "83       0      0     10      0   125     1      0      0     0   \n",
       "89      10      0      1      0     1     0      0      0     0   \n",
       "92       0      0      7      0    44     3      0      0     0   \n",
       "99       5      0      1      0    44     2      0      0     0   \n",
       "108      0      0      0      0    78     1      0      0     0   \n",
       "114      0      0     13      0   633     1      0      0     0   \n",
       "117      1      0     20      0   802     2      0      0     0   \n",
       "118      0      0     11      0    49     1      0      0     0   \n",
       "124      1      1      7      0   117     0      0      0     0   \n",
       "130      0      0      3      0    74     1      0      0     0   \n",
       "\n",
       "     adverse_pregnancy  \n",
       "2                    1  \n",
       "3                    1  \n",
       "5                    1  \n",
       "9                    1  \n",
       "20                   1  \n",
       "22                   1  \n",
       "25                   1  \n",
       "27                   1  \n",
       "43                   1  \n",
       "44                   1  \n",
       "49                   1  \n",
       "56                   1  \n",
       "66                   1  \n",
       "70                   1  \n",
       "71                   1  \n",
       "83                   0  \n",
       "89                   0  \n",
       "92                   0  \n",
       "99                   0  \n",
       "108                  0  \n",
       "114                  0  \n",
       "117                  0  \n",
       "118                  0  \n",
       "124                  1  \n",
       "130                  1  \n",
       "\n",
       "[25 rows x 4612 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firstT_diffe = binary_data_firstT[firstT_diffe_list]\n",
    "firstT_diffe = firstT_diffe.join(binary_data_firstT[\"adverse_pregnancy\"])\n",
    "firstT_diffe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27c6602",
   "metadata": {},
   "source": [
    "# first trimester model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03830513",
   "metadata": {},
   "source": [
    "## case: pregnancy complications (all diseases) control: healthy pregnancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d58badfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4611)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "## set x and y\n",
    "X = firstT_diffe.loc[:, firstT_diffe.columns != \"adverse_pregnancy\"].values\n",
    "Y = firstT_diffe[\"adverse_pregnancy\"].values \n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fab8d8d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25, 15)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(X, Y)\n",
    "model = SelectFromModel(lsvc, prefit=True)\n",
    "X = model.transform(X) ## using L-1 based feature selection \n",
    "X.shape ## dimentionality of the data is significantly reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "69addfb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## test/train split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fd33dbf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "firstT_model = Sequential()\n",
    "firstT_model.add(Dense(8, input_dim=x_train.shape[1], activation='relu'))\n",
    "firstT_model.add(Dense(8, activation='relu'))\n",
    "firstT_model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d2d2473c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_27 (Dense)            (None, 8)                 128       \n",
      "                                                                 \n",
      " dense_28 (Dense)            (None, 8)                 72        \n",
      "                                                                 \n",
      " dense_29 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 209\n",
      "Trainable params: 209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "firstT_model.summary() ## architecture of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aa7731b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "firstT_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "dbf60bb8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "2/2 [==============================] - 1s 160ms/step - loss: 2047.9963 - accuracy: 0.4615 - val_loss: 9.0085 - val_accuracy: 0.8000\n",
      "Epoch 2/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1937.6163 - accuracy: 0.4615 - val_loss: 10.1414 - val_accuracy: 0.6000\n",
      "Epoch 3/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 1788.9534 - accuracy: 0.4615 - val_loss: 13.1913 - val_accuracy: 0.6000\n",
      "Epoch 4/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 1679.7815 - accuracy: 0.3077 - val_loss: 15.6525 - val_accuracy: 0.8000\n",
      "Epoch 5/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1555.3182 - accuracy: 0.4615 - val_loss: 18.2752 - val_accuracy: 0.8000\n",
      "Epoch 6/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 1428.9922 - accuracy: 0.4615 - val_loss: 20.0358 - val_accuracy: 0.8000\n",
      "Epoch 7/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 1298.8073 - accuracy: 0.3846 - val_loss: 20.3751 - val_accuracy: 0.8000\n",
      "Epoch 8/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 1236.7629 - accuracy: 0.4615 - val_loss: 19.6508 - val_accuracy: 0.8000\n",
      "Epoch 9/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 1128.9523 - accuracy: 0.4615 - val_loss: 18.9225 - val_accuracy: 0.8000\n",
      "Epoch 10/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1016.2462 - accuracy: 0.4615 - val_loss: 17.9849 - val_accuracy: 0.8000\n",
      "Epoch 11/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 920.2276 - accuracy: 0.3846 - val_loss: 18.0800 - val_accuracy: 0.8000\n",
      "Epoch 12/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 840.6692 - accuracy: 0.4615 - val_loss: 19.0658 - val_accuracy: 0.8000\n",
      "Epoch 13/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 728.6199 - accuracy: 0.4615 - val_loss: 20.2111 - val_accuracy: 0.8000\n",
      "Epoch 14/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 685.8347 - accuracy: 0.5385 - val_loss: 22.0338 - val_accuracy: 0.8000\n",
      "Epoch 15/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 637.0355 - accuracy: 0.5385 - val_loss: 24.7249 - val_accuracy: 0.8000\n",
      "Epoch 16/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 604.3573 - accuracy: 0.4615 - val_loss: 27.1464 - val_accuracy: 0.8000\n",
      "Epoch 17/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 579.3505 - accuracy: 0.4615 - val_loss: 29.2455 - val_accuracy: 0.8000\n",
      "Epoch 18/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 544.7361 - accuracy: 0.4615 - val_loss: 31.2817 - val_accuracy: 0.8000\n",
      "Epoch 19/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 493.2378 - accuracy: 0.4615 - val_loss: 32.0043 - val_accuracy: 0.8000\n",
      "Epoch 20/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 471.6485 - accuracy: 0.4615 - val_loss: 31.8888 - val_accuracy: 0.8000\n",
      "Epoch 21/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 421.9821 - accuracy: 0.4615 - val_loss: 32.2647 - val_accuracy: 0.6000\n",
      "Epoch 22/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 400.5041 - accuracy: 0.4615 - val_loss: 32.4009 - val_accuracy: 0.6000\n",
      "Epoch 23/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 348.4420 - accuracy: 0.4615 - val_loss: 34.8673 - val_accuracy: 0.6000\n",
      "Epoch 24/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 327.0822 - accuracy: 0.5385 - val_loss: 38.2535 - val_accuracy: 0.6000\n",
      "Epoch 25/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 271.7906 - accuracy: 0.5385 - val_loss: 41.4661 - val_accuracy: 0.8000\n",
      "Epoch 26/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 231.9219 - accuracy: 0.5385 - val_loss: 44.0336 - val_accuracy: 0.8000\n",
      "Epoch 27/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 209.7158 - accuracy: 0.5385 - val_loss: 46.2769 - val_accuracy: 0.8000\n",
      "Epoch 28/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 149.4558 - accuracy: 0.5385 - val_loss: 48.4397 - val_accuracy: 0.8000\n",
      "Epoch 29/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 131.5752 - accuracy: 0.5385 - val_loss: 50.5420 - val_accuracy: 0.8000\n",
      "Epoch 30/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 132.9013 - accuracy: 0.5385 - val_loss: 54.0818 - val_accuracy: 0.8000\n",
      "Epoch 31/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 132.7561 - accuracy: 0.5385 - val_loss: 57.8531 - val_accuracy: 0.8000\n",
      "Epoch 32/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 130.3816 - accuracy: 0.5385 - val_loss: 61.9214 - val_accuracy: 0.8000\n",
      "Epoch 33/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 128.3757 - accuracy: 0.5385 - val_loss: 66.3114 - val_accuracy: 0.8000\n",
      "Epoch 34/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 125.3989 - accuracy: 0.5385 - val_loss: 70.9666 - val_accuracy: 0.8000\n",
      "Epoch 35/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 122.3820 - accuracy: 0.5385 - val_loss: 75.6886 - val_accuracy: 0.8000\n",
      "Epoch 36/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 118.8672 - accuracy: 0.6154 - val_loss: 78.7162 - val_accuracy: 0.8000\n",
      "Epoch 37/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 115.2671 - accuracy: 0.6154 - val_loss: 79.9176 - val_accuracy: 0.8000\n",
      "Epoch 38/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 111.5341 - accuracy: 0.6154 - val_loss: 80.6391 - val_accuracy: 0.8000\n",
      "Epoch 39/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 107.6846 - accuracy: 0.6154 - val_loss: 81.1606 - val_accuracy: 0.8000\n",
      "Epoch 40/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 103.7735 - accuracy: 0.6154 - val_loss: 81.5553 - val_accuracy: 0.6000\n",
      "Epoch 41/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 99.6726 - accuracy: 0.6154 - val_loss: 81.9177 - val_accuracy: 0.6000\n",
      "Epoch 42/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 95.7383 - accuracy: 0.6154 - val_loss: 82.2020 - val_accuracy: 0.6000\n",
      "Epoch 43/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 92.3913 - accuracy: 0.6154 - val_loss: 82.3446 - val_accuracy: 0.6000\n",
      "Epoch 44/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 88.5713 - accuracy: 0.5385 - val_loss: 82.1613 - val_accuracy: 0.6000\n",
      "Epoch 45/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 83.4011 - accuracy: 0.5385 - val_loss: 81.8150 - val_accuracy: 0.6000\n",
      "Epoch 46/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 79.9524 - accuracy: 0.6154 - val_loss: 81.6633 - val_accuracy: 0.6000\n",
      "Epoch 47/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 75.1906 - accuracy: 0.7692 - val_loss: 81.4111 - val_accuracy: 0.6000\n",
      "Epoch 48/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 71.5022 - accuracy: 0.6923 - val_loss: 81.4820 - val_accuracy: 0.6000\n",
      "Epoch 49/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 66.7175 - accuracy: 0.6923 - val_loss: 82.0290 - val_accuracy: 0.6000\n",
      "Epoch 50/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 63.2427 - accuracy: 0.7692 - val_loss: 82.9841 - val_accuracy: 0.6000\n",
      "Epoch 51/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 59.4311 - accuracy: 0.7692 - val_loss: 83.8385 - val_accuracy: 0.6000\n",
      "Epoch 52/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 55.1436 - accuracy: 0.7692 - val_loss: 84.4134 - val_accuracy: 0.6000\n",
      "Epoch 53/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 50.5279 - accuracy: 0.7692 - val_loss: 85.0164 - val_accuracy: 0.6000\n",
      "Epoch 54/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 47.9566 - accuracy: 0.7692 - val_loss: 85.8588 - val_accuracy: 0.6000\n",
      "Epoch 55/200\n",
      "2/2 [==============================] - 0s 41ms/step - loss: 46.0332 - accuracy: 0.8462 - val_loss: 86.4369 - val_accuracy: 0.6000\n",
      "Epoch 56/200\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 45.4314 - accuracy: 0.8462 - val_loss: 86.8103 - val_accuracy: 0.6000\n",
      "Epoch 57/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 44.8496 - accuracy: 0.8462 - val_loss: 86.9343 - val_accuracy: 0.6000\n",
      "Epoch 58/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 27ms/step - loss: 44.1885 - accuracy: 0.8462 - val_loss: 86.7074 - val_accuracy: 0.6000\n",
      "Epoch 59/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 43.9918 - accuracy: 0.7692 - val_loss: 86.3452 - val_accuracy: 0.6000\n",
      "Epoch 60/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 43.3343 - accuracy: 0.7692 - val_loss: 86.0723 - val_accuracy: 0.6000\n",
      "Epoch 61/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 42.6935 - accuracy: 0.7692 - val_loss: 85.7569 - val_accuracy: 0.6000\n",
      "Epoch 62/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 42.1787 - accuracy: 0.7692 - val_loss: 85.4074 - val_accuracy: 0.6000\n",
      "Epoch 63/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 41.5122 - accuracy: 0.7692 - val_loss: 85.0780 - val_accuracy: 0.6000\n",
      "Epoch 64/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 41.0855 - accuracy: 0.7692 - val_loss: 84.6003 - val_accuracy: 0.6000\n",
      "Epoch 65/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 40.5095 - accuracy: 0.8462 - val_loss: 83.9753 - val_accuracy: 0.6000\n",
      "Epoch 66/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 39.9631 - accuracy: 0.8462 - val_loss: 83.3584 - val_accuracy: 0.6000\n",
      "Epoch 67/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 39.3244 - accuracy: 0.8462 - val_loss: 82.7528 - val_accuracy: 0.6000\n",
      "Epoch 68/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 38.9676 - accuracy: 0.7692 - val_loss: 82.4251 - val_accuracy: 0.6000\n",
      "Epoch 69/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 38.6016 - accuracy: 0.7692 - val_loss: 82.2367 - val_accuracy: 0.6000\n",
      "Epoch 70/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 38.2338 - accuracy: 0.6923 - val_loss: 81.9707 - val_accuracy: 0.6000\n",
      "Epoch 71/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 37.5907 - accuracy: 0.7692 - val_loss: 81.5952 - val_accuracy: 0.6000\n",
      "Epoch 72/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 37.1031 - accuracy: 0.7692 - val_loss: 81.2123 - val_accuracy: 0.6000\n",
      "Epoch 73/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 36.7227 - accuracy: 0.6923 - val_loss: 81.0308 - val_accuracy: 0.6000\n",
      "Epoch 74/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 36.6575 - accuracy: 0.6923 - val_loss: 80.9420 - val_accuracy: 0.6000\n",
      "Epoch 75/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 35.6348 - accuracy: 0.7692 - val_loss: 80.8736 - val_accuracy: 0.6000\n",
      "Epoch 76/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 35.0866 - accuracy: 0.7692 - val_loss: 80.6678 - val_accuracy: 0.6000\n",
      "Epoch 77/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 34.7157 - accuracy: 0.7692 - val_loss: 80.3712 - val_accuracy: 0.6000\n",
      "Epoch 78/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 34.0331 - accuracy: 0.7692 - val_loss: 80.0116 - val_accuracy: 0.6000\n",
      "Epoch 79/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 33.5192 - accuracy: 0.6923 - val_loss: 79.4916 - val_accuracy: 0.6000\n",
      "Epoch 80/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.9164 - accuracy: 0.7692 - val_loss: 78.8999 - val_accuracy: 0.6000\n",
      "Epoch 81/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 32.4174 - accuracy: 0.7692 - val_loss: 78.3318 - val_accuracy: 0.6000\n",
      "Epoch 82/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 31.9582 - accuracy: 0.7692 - val_loss: 77.7854 - val_accuracy: 0.6000\n",
      "Epoch 83/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 31.5099 - accuracy: 0.7692 - val_loss: 77.2570 - val_accuracy: 0.6000\n",
      "Epoch 84/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 31.0817 - accuracy: 0.7692 - val_loss: 76.6776 - val_accuracy: 0.6000\n",
      "Epoch 85/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 30.5460 - accuracy: 0.6923 - val_loss: 76.3536 - val_accuracy: 0.6000\n",
      "Epoch 86/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.1782 - accuracy: 0.7692 - val_loss: 76.5079 - val_accuracy: 0.4000\n",
      "Epoch 87/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.1109 - accuracy: 0.8462 - val_loss: 76.7206 - val_accuracy: 0.4000\n",
      "Epoch 88/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.1933 - accuracy: 0.8462 - val_loss: 76.8757 - val_accuracy: 0.4000\n",
      "Epoch 89/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.4414 - accuracy: 0.7692 - val_loss: 77.1685 - val_accuracy: 0.4000\n",
      "Epoch 90/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 30.6850 - accuracy: 0.8462 - val_loss: 77.5548 - val_accuracy: 0.4000\n",
      "Epoch 91/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 31.5961 - accuracy: 0.8462 - val_loss: 77.8559 - val_accuracy: 0.4000\n",
      "Epoch 92/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.0158 - accuracy: 0.7692 - val_loss: 78.2642 - val_accuracy: 0.6000\n",
      "Epoch 93/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 32.2912 - accuracy: 0.7692 - val_loss: 78.7721 - val_accuracy: 0.6000\n",
      "Epoch 94/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.8039 - accuracy: 0.6923 - val_loss: 78.9300 - val_accuracy: 0.6000\n",
      "Epoch 95/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.8254 - accuracy: 0.6923 - val_loss: 78.6362 - val_accuracy: 0.6000\n",
      "Epoch 96/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.7751 - accuracy: 0.7692 - val_loss: 78.2214 - val_accuracy: 0.6000\n",
      "Epoch 97/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.6779 - accuracy: 0.7692 - val_loss: 77.7628 - val_accuracy: 0.6000\n",
      "Epoch 98/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.2863 - accuracy: 0.7692 - val_loss: 77.4146 - val_accuracy: 0.6000\n",
      "Epoch 99/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 31.9438 - accuracy: 0.6923 - val_loss: 77.1166 - val_accuracy: 0.6000\n",
      "Epoch 100/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.0684 - accuracy: 0.7692 - val_loss: 76.7118 - val_accuracy: 0.6000\n",
      "Epoch 101/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.2196 - accuracy: 0.7692 - val_loss: 76.1210 - val_accuracy: 0.6000\n",
      "Epoch 102/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 31.2672 - accuracy: 0.7692 - val_loss: 75.6340 - val_accuracy: 0.4000\n",
      "Epoch 103/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.3671 - accuracy: 0.6923 - val_loss: 75.5865 - val_accuracy: 0.4000\n",
      "Epoch 104/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 29.5232 - accuracy: 0.8462 - val_loss: 75.5037 - val_accuracy: 0.4000\n",
      "Epoch 105/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 29.3722 - accuracy: 0.8462 - val_loss: 75.0307 - val_accuracy: 0.4000\n",
      "Epoch 106/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 29.0327 - accuracy: 0.8462 - val_loss: 74.4727 - val_accuracy: 0.4000\n",
      "Epoch 107/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 28.1799 - accuracy: 0.8462 - val_loss: 74.0026 - val_accuracy: 0.4000\n",
      "Epoch 108/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 27.2345 - accuracy: 0.8462 - val_loss: 73.5460 - val_accuracy: 0.4000\n",
      "Epoch 109/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 26.8930 - accuracy: 0.7692 - val_loss: 72.9878 - val_accuracy: 0.4000\n",
      "Epoch 110/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 26.6280 - accuracy: 0.7692 - val_loss: 72.3156 - val_accuracy: 0.4000\n",
      "Epoch 111/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 26.0013 - accuracy: 0.8462 - val_loss: 71.5724 - val_accuracy: 0.4000\n",
      "Epoch 112/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 25.1187 - accuracy: 0.7692 - val_loss: 71.6110 - val_accuracy: 0.4000\n",
      "Epoch 113/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 24.3833 - accuracy: 0.8462 - val_loss: 71.8385 - val_accuracy: 0.4000\n",
      "Epoch 114/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 23.6301 - accuracy: 0.8462 - val_loss: 71.9770 - val_accuracy: 0.4000\n",
      "Epoch 115/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 25.5752 - accuracy: 0.7692 - val_loss: 72.3801 - val_accuracy: 0.4000\n",
      "Epoch 116/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 24.1508 - accuracy: 0.7692 - val_loss: 73.1178 - val_accuracy: 0.4000\n",
      "Epoch 117/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.0202 - accuracy: 0.8462 - val_loss: 73.7699 - val_accuracy: 0.4000\n",
      "Epoch 118/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 22.9049 - accuracy: 0.8462 - val_loss: 73.9489 - val_accuracy: 0.4000\n",
      "Epoch 119/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.0050 - accuracy: 0.8462 - val_loss: 73.8440 - val_accuracy: 0.4000\n",
      "Epoch 120/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.2644 - accuracy: 0.8462 - val_loss: 73.7642 - val_accuracy: 0.4000\n",
      "Epoch 121/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.3435 - accuracy: 0.8462 - val_loss: 73.6990 - val_accuracy: 0.4000\n",
      "Epoch 122/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.2720 - accuracy: 0.8462 - val_loss: 73.6281 - val_accuracy: 0.4000\n",
      "Epoch 123/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.0521 - accuracy: 0.8462 - val_loss: 73.5535 - val_accuracy: 0.4000\n",
      "Epoch 124/200\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 22.5942 - accuracy: 0.8462 - val_loss: 73.4983 - val_accuracy: 0.4000\n",
      "Epoch 125/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 22.2008 - accuracy: 0.9231 - val_loss: 73.1753 - val_accuracy: 0.4000\n",
      "Epoch 126/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 22.0413 - accuracy: 0.8462 - val_loss: 72.6221 - val_accuracy: 0.4000\n",
      "Epoch 127/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 21.7893 - accuracy: 0.8462 - val_loss: 72.1503 - val_accuracy: 0.4000\n",
      "Epoch 128/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 21.4642 - accuracy: 0.8462 - val_loss: 71.7363 - val_accuracy: 0.4000\n",
      "Epoch 129/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 21.1610 - accuracy: 0.9231 - val_loss: 71.3674 - val_accuracy: 0.4000\n",
      "Epoch 130/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 20.8867 - accuracy: 0.9231 - val_loss: 71.0352 - val_accuracy: 0.4000\n",
      "Epoch 131/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 20.6119 - accuracy: 0.9231 - val_loss: 70.7329 - val_accuracy: 0.4000\n",
      "Epoch 132/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 20.3321 - accuracy: 0.9231 - val_loss: 70.4549 - val_accuracy: 0.4000\n",
      "Epoch 133/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 20.0473 - accuracy: 0.9231 - val_loss: 70.1968 - val_accuracy: 0.4000\n",
      "Epoch 134/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 19.9941 - accuracy: 0.8462 - val_loss: 70.5867 - val_accuracy: 0.4000\n",
      "Epoch 135/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 20.2834 - accuracy: 0.9231 - val_loss: 71.4424 - val_accuracy: 0.4000\n",
      "Epoch 136/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 21.3934 - accuracy: 0.9231 - val_loss: 72.1032 - val_accuracy: 0.4000\n",
      "Epoch 137/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 22.6052 - accuracy: 0.9231 - val_loss: 72.5653 - val_accuracy: 0.4000\n",
      "Epoch 138/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 22.8308 - accuracy: 0.9231 - val_loss: 72.8320 - val_accuracy: 0.4000\n",
      "Epoch 139/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.1578 - accuracy: 0.9231 - val_loss: 73.0006 - val_accuracy: 0.4000\n",
      "Epoch 140/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.3610 - accuracy: 0.9231 - val_loss: 73.0770 - val_accuracy: 0.4000\n",
      "Epoch 141/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 23.4591 - accuracy: 0.9231 - val_loss: 73.0650 - val_accuracy: 0.4000\n",
      "Epoch 142/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.4780 - accuracy: 0.9231 - val_loss: 73.0017 - val_accuracy: 0.4000\n",
      "Epoch 143/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.4347 - accuracy: 0.9231 - val_loss: 72.9095 - val_accuracy: 0.4000\n",
      "Epoch 144/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.3408 - accuracy: 0.9231 - val_loss: 72.7991 - val_accuracy: 0.4000\n",
      "Epoch 145/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 23.2048 - accuracy: 0.9231 - val_loss: 72.6771 - val_accuracy: 0.4000\n",
      "Epoch 146/200\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 23.0349 - accuracy: 0.9231 - val_loss: 72.5456 - val_accuracy: 0.4000\n",
      "Epoch 147/200\n",
      "2/2 [==============================] - 0s 78ms/step - loss: 22.8373 - accuracy: 0.9231 - val_loss: 72.4065 - val_accuracy: 0.4000\n",
      "Epoch 148/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 22.6172 - accuracy: 0.9231 - val_loss: 72.2612 - val_accuracy: 0.4000\n",
      "Epoch 149/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 22.3785 - accuracy: 0.9231 - val_loss: 72.1109 - val_accuracy: 0.4000\n",
      "Epoch 150/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 22.1250 - accuracy: 0.9231 - val_loss: 71.9564 - val_accuracy: 0.4000\n",
      "Epoch 151/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 21.8599 - accuracy: 0.9231 - val_loss: 71.7984 - val_accuracy: 0.4000\n",
      "Epoch 152/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 21.5840 - accuracy: 0.9231 - val_loss: 71.6377 - val_accuracy: 0.4000\n",
      "Epoch 153/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 21.3004 - accuracy: 0.9231 - val_loss: 71.4748 - val_accuracy: 0.4000\n",
      "Epoch 154/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 20.8860 - accuracy: 0.9231 - val_loss: 71.2854 - val_accuracy: 0.4000\n",
      "Epoch 155/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 20.6697 - accuracy: 0.9231 - val_loss: 71.0479 - val_accuracy: 0.4000\n",
      "Epoch 156/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 20.2368 - accuracy: 0.9231 - val_loss: 70.8224 - val_accuracy: 0.4000\n",
      "Epoch 157/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 19.8253 - accuracy: 0.9231 - val_loss: 70.6067 - val_accuracy: 0.4000\n",
      "Epoch 158/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 19.4314 - accuracy: 0.9231 - val_loss: 70.3988 - val_accuracy: 0.4000\n",
      "Epoch 159/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 19.0515 - accuracy: 0.9231 - val_loss: 70.1973 - val_accuracy: 0.4000\n",
      "Epoch 160/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 18.6828 - accuracy: 0.9231 - val_loss: 70.0009 - val_accuracy: 0.4000\n",
      "Epoch 161/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 18.3236 - accuracy: 0.9231 - val_loss: 69.8087 - val_accuracy: 0.4000\n",
      "Epoch 162/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 17.9715 - accuracy: 0.9231 - val_loss: 69.6198 - val_accuracy: 0.4000\n",
      "Epoch 163/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 17.6252 - accuracy: 0.9231 - val_loss: 69.4336 - val_accuracy: 0.4000\n",
      "Epoch 164/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 17.2835 - accuracy: 0.9231 - val_loss: 69.2496 - val_accuracy: 0.4000\n",
      "Epoch 165/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 16.9455 - accuracy: 0.9231 - val_loss: 69.0673 - val_accuracy: 0.4000\n",
      "Epoch 166/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 16.4674 - accuracy: 0.9231 - val_loss: 68.8618 - val_accuracy: 0.4000\n",
      "Epoch 167/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 16.0202 - accuracy: 0.9231 - val_loss: 68.5858 - val_accuracy: 0.4000\n",
      "Epoch 168/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 15.4557 - accuracy: 0.9231 - val_loss: 68.2529 - val_accuracy: 0.4000\n",
      "Epoch 169/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.7971 - accuracy: 0.9231 - val_loss: 67.8741 - val_accuracy: 0.4000\n",
      "Epoch 170/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.4117 - accuracy: 0.9231 - val_loss: 67.4840 - val_accuracy: 0.4000\n",
      "Epoch 171/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 19.5923 - accuracy: 0.8462 - val_loss: 67.5276 - val_accuracy: 0.4000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 172/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 17.8560 - accuracy: 0.8462 - val_loss: 67.8961 - val_accuracy: 0.4000\n",
      "Epoch 173/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 13.9067 - accuracy: 0.9231 - val_loss: 68.1397 - val_accuracy: 0.4000\n",
      "Epoch 174/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 13.9000 - accuracy: 0.9231 - val_loss: 68.2549 - val_accuracy: 0.4000\n",
      "Epoch 175/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 13.7994 - accuracy: 0.9231 - val_loss: 68.2924 - val_accuracy: 0.4000\n",
      "Epoch 176/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 13.6664 - accuracy: 0.9231 - val_loss: 68.2402 - val_accuracy: 0.4000\n",
      "Epoch 177/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 13.3786 - accuracy: 0.9231 - val_loss: 68.1659 - val_accuracy: 0.4000\n",
      "Epoch 178/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 13.0846 - accuracy: 0.9231 - val_loss: 68.0737 - val_accuracy: 0.4000\n",
      "Epoch 179/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.7856 - accuracy: 0.9231 - val_loss: 67.9671 - val_accuracy: 0.4000\n",
      "Epoch 180/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.3531 - accuracy: 0.9231 - val_loss: 67.8252 - val_accuracy: 0.4000\n",
      "Epoch 181/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.1305 - accuracy: 0.9231 - val_loss: 67.6293 - val_accuracy: 0.4000\n",
      "Epoch 182/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 11.6891 - accuracy: 0.9231 - val_loss: 67.4403 - val_accuracy: 0.4000\n",
      "Epoch 183/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.1047 - accuracy: 0.8462 - val_loss: 67.9783 - val_accuracy: 0.4000\n",
      "Epoch 184/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.4897 - accuracy: 0.9231 - val_loss: 69.0200 - val_accuracy: 0.4000\n",
      "Epoch 185/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 13.4533 - accuracy: 0.9231 - val_loss: 69.7653 - val_accuracy: 0.4000\n",
      "Epoch 186/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 13.7597 - accuracy: 0.9231 - val_loss: 70.2907 - val_accuracy: 0.4000\n",
      "Epoch 187/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 14.2245 - accuracy: 0.9231 - val_loss: 70.6866 - val_accuracy: 0.4000\n",
      "Epoch 188/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 14.6767 - accuracy: 0.9231 - val_loss: 70.9533 - val_accuracy: 0.4000\n",
      "Epoch 189/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 14.7056 - accuracy: 0.9231 - val_loss: 71.0656 - val_accuracy: 0.4000\n",
      "Epoch 190/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.6111 - accuracy: 0.9231 - val_loss: 71.0762 - val_accuracy: 0.4000\n",
      "Epoch 191/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 14.3882 - accuracy: 0.9231 - val_loss: 71.0533 - val_accuracy: 0.4000\n",
      "Epoch 192/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.0429 - accuracy: 0.9231 - val_loss: 70.9798 - val_accuracy: 0.4000\n",
      "Epoch 193/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 13.8429 - accuracy: 0.9231 - val_loss: 70.8409 - val_accuracy: 0.4000\n",
      "Epoch 194/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 13.4395 - accuracy: 0.9231 - val_loss: 70.6985 - val_accuracy: 0.4000\n",
      "Epoch 195/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 13.0512 - accuracy: 0.9231 - val_loss: 70.5544 - val_accuracy: 0.4000\n",
      "Epoch 196/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 12.5148 - accuracy: 0.9231 - val_loss: 70.3881 - val_accuracy: 0.4000\n",
      "Epoch 197/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 12.2634 - accuracy: 0.9231 - val_loss: 70.1846 - val_accuracy: 0.4000\n",
      "Epoch 198/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 11.7723 - accuracy: 0.9231 - val_loss: 70.0021 - val_accuracy: 0.4000\n",
      "Epoch 199/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 11.3132 - accuracy: 0.9231 - val_loss: 69.8170 - val_accuracy: 0.4000\n",
      "Epoch 200/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 10.8799 - accuracy: 0.9231 - val_loss: 69.5169 - val_accuracy: 0.2000\n"
     ]
    }
   ],
   "source": [
    "history = firstT_model.fit(x_train, y_train, epochs=200,  validation_split=0.25, batch_size=10, verbose=1)\n",
    "## fitting the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548f7d51",
   "metadata": {},
   "source": [
    "### model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0374d656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.034645080566406, 0.7142857313156128]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = firstT_model.evaluate(x_test, y_test, verbose=0)\n",
    "score ## accuracy score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb885696",
   "metadata": {},
   "source": [
    "### plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c9a8d499",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"], label=\"train\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"validation\")\n",
    "plt.title(\"model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\"], loc='upper left')\n",
    "plt.show() ## model loss plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c5f1662b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 57ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = firstT_model.predict(x_test)\n",
    "predictions.round(1)\n",
    "np.rint(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5867ae6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix = sklearn.metrics.confusion_matrix(y_test, np.rint(predictions))\n",
    "sns.heatmap(confusion_matrix, annot=True, cmap=\"Reds\")\n",
    "plt.xlabel(\"model prediction\")\n",
    "plt.ylabel(\"truth\")\n",
    "plt.show() ## confusion matrix to visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0585b944",
   "metadata": {},
   "source": [
    "# second trimester data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a116cf51",
   "metadata": {
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>57727</th>\n",
       "      <th>57728</th>\n",
       "      <th>57729</th>\n",
       "      <th>57730</th>\n",
       "      <th>57731</th>\n",
       "      <th>57732</th>\n",
       "      <th>57733</th>\n",
       "      <th>57734</th>\n",
       "      <th>57735</th>\n",
       "      <th>adverse_pregnancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>155</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0   1  2  3   4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
       "0     3   9  0  0   0  0  0  0  0  0  ...      0     45      3      0      0   \n",
       "4    19   0  0  0   0  0  0  0  0  0  ...      0     41      0      0      0   \n",
       "10    0   0  0  0   0  0  0  0  0  0  ...      0     35      0      0      0   \n",
       "13    1   0  0  0   0  0  0  0  0  3  ...      0     56      0      0      0   \n",
       "23    9   0  0  0   0  0  0  0  0  2  ...      0     31      0      0      0   \n",
       "24    0   8  0  0   0  0  0  0  0  0  ...      0     38      0      0      0   \n",
       "31    0   0  0  0   3  0  0  0  0  0  ...      0     31      0      0      0   \n",
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       "57    2   0  0  0  15  0  0  0  0  1  ...      0     39      0      0      0   \n",
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       "60    0   0  0  0   0  0  0  0  0  0  ...      0     58      0      0      0   \n",
       "63    0   0  0  0   0  0  0  0  0  0  ...      0     61      0      0      0   \n",
       "65    0   0  0  0   0  0  0  0  0  1  ...      0     17      0      0      0   \n",
       "82    0  12  0  0   0  0  0  0  0  0  ...      0      8      0      0      0   \n",
       "87    0   0  0  0   3  0  0  0  0  0  ...      0     23      0      0      2   \n",
       "88    0   0  0  0   0  0  0  0  0  0  ...      1     28      0      0      0   \n",
       "97    0   0  0  0   0  0  0  0  0  0  ...      0      5      0      0      0   \n",
       "98    0   0  0  0   1  0  0  0  0  0  ...      0     28      0      0      0   \n",
       "104   2   1  0  0   0  0  0  0  0  1  ...      0    102      0      0      0   \n",
       "105   0   0  0  0   5  0  0  0  0  0  ...      0     33      0      0      0   \n",
       "121   0   0  0  0   2  0  0  0  0  0  ...      0      3      3      0      0   \n",
       "122   4   0  0  0   0  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "125   0   0  0  0   0  0  0  0  0  0  ...      0     88      0      0      0   \n",
       "129   0   0  0  0  11  2  0  0  0  0  ...      1     16      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735  adverse_pregnancy  \n",
       "0        0    135      0      0                  1  \n",
       "4        0     20      0      0                  1  \n",
       "10       1    112      7      0                  1  \n",
       "13      21    171      0      0                  1  \n",
       "23       1    107      0      0                  1  \n",
       "24       0     98      0      0                  1  \n",
       "31      13    135      0      0                  1  \n",
       "34       6     74      2      0                  1  \n",
       "42       0    110      0      0                  1  \n",
       "48      10     76      0      0                  1  \n",
       "57       8    146      1      0                  1  \n",
       "58       4     49      3      0                  1  \n",
       "60       0      1      0      0                  1  \n",
       "63       0    102      0      0                  1  \n",
       "65       1    157      0      0                  1  \n",
       "82       0     60      0      0                  0  \n",
       "87       1     90      0      0                  0  \n",
       "88      12    109      0      0                  0  \n",
       "97       8     97      0      0                  0  \n",
       "98      18     97      0      0                  0  \n",
       "104     13    208      0      0                  0  \n",
       "105     25    173      0      0                  0  \n",
       "121      2     27      1      0                  0  \n",
       "122     59    274      0      0                  0  \n",
       "125     28    155      7      0                  1  \n",
       "129      2     90      0      0                  1  \n",
       "\n",
       "[26 rows x 57737 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_trimester_data[\"adverse_pregnancy\"] = np.where(second_trimester_data.disease.str.contains(\"Normal-Pregnancy\"), 0, 1)\n",
    "binary_data_secondT = second_trimester_data.drop([\"disease\"], axis=1)\n",
    "binary_data_secondT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1f07d5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 28253,\n",
       " ...]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preeclampsia_dif_exp_secondT = pd.read_csv(\"preeclamspsia_diffe_secondT.txt\", sep=\" \")\n",
    "preeclampsia_dif_exp_secondT = preeclampsia_dif_exp_secondT[preeclampsia_dif_exp_secondT[\"PValue\"] <= 0.05]\n",
    "preeclampsia_dif_exp_secondT = list(preeclampsia_dif_exp_secondT.index)\n",
    "\n",
    "gestational_diabetes_dif_exp_secondT = pd.read_csv(\"gestational_diabetes_diffe_secondT.txt\", sep=\" \")\n",
    "gestational_diabetes_dif_exp_secondT = gestational_diabetes_dif_exp_secondT[gestational_diabetes_dif_exp_secondT[\"PValue\"] <= 0.05]\n",
    "gestational_diabetes_dif_exp_secondT = list(gestational_diabetes_dif_exp_secondT.index)\n",
    "\n",
    "chronic_hypertension_dif_exp_secondT = pd.read_csv(\"chronic_hypertension_diffe_secondT.txt\", sep=\" \")\n",
    "chronic_hypertension_dif_exp_secondT = chronic_hypertension_dif_exp_secondT[chronic_hypertension_dif_exp_secondT[\"PValue\"] <= 0.05]\n",
    "chronic_hypertension_dif_exp_secondT = list(chronic_hypertension_dif_exp_secondT.index)\n",
    "\n",
    "secondT_diffe_list = list(set(preeclampsia_dif_exp_secondT + gestational_diabetes_dif_exp_secondT + chronic_hypertension_dif_exp_secondT))\n",
    "secondT_diffe_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "232a206e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>467</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>225</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>356</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>2205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>633</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>587</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 2201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1  8199  11  24587  24588  15  16403  32790  16408  24610  ...  8165  \\\n",
       "0     9     0   0     29    718   0      0      0     18      2  ...     0   \n",
       "4     0     0   0      0    578   0      0      0      1     11  ...     0   \n",
       "10    0     0   0      9    743   0      0      0     26     21  ...     0   \n",
       "13    0     0   0     18    501   0      0      0     11     24  ...     0   \n",
       "23    0     0   0      1    399   0      0      0      1      4  ...     0   \n",
       "24    8     0   0     67    288   0      0      0      0     55  ...     0   \n",
       "31    0     0   0     16    765   0      0      0     12     11  ...     0   \n",
       "34    0     4   0     15    182   0      0      0     21      8  ...     0   \n",
       "42    5     1   0      7    902   0      0      0      2     34  ...     0   \n",
       "48    0     0   0     11    230   0      0      0      0      4  ...     0   \n",
       "57    0     0   0     18    648   0      0      0     21     16  ...     0   \n",
       "58    0     3   0      8    157   0      0      0     16      8  ...     0   \n",
       "60    0     0   0      0    890   0      0      0     45      0  ...     0   \n",
       "63    0     0   0      0    467   0      0      0      2      0  ...     0   \n",
       "65    0     0   0      9    980   0      0      0      2      5  ...     0   \n",
       "82   12     0   0     23    556   0      0      0      0      4  ...     0   \n",
       "87    0     0   0     34    202   0      0      0      3      0  ...     0   \n",
       "88    0     0   0     25    225   0      0      0      8      8  ...     0   \n",
       "97    0     0   0     29    356   0      0      0      0     26  ...     0   \n",
       "98    0     3   0      5    662   0      0      0      7      0  ...     0   \n",
       "104   1     0   0     57   2205   0      0      0      0      1  ...     0   \n",
       "105   0     0   0     38    852   0      0      0     10     14  ...     0   \n",
       "121   0     0   0      0     14   0      0      0      9      0  ...     0   \n",
       "122   0     0   0     79    633   0      0      0      0     14  ...     0   \n",
       "125   0     0   0     31    587   0      0      0     14     10  ...     0   \n",
       "129   0     1   0     11    684   0      0      0     30     26  ...     0   \n",
       "\n",
       "     40933  49127  40936  49129  24553  57328  49138  32758  adverse_pregnancy  \n",
       "0        0      0      0      0      0      3      0     12                  1  \n",
       "4        0      0      0      0      0      1      0      0                  1  \n",
       "10       0      0      0      0      0      2      0     36                  1  \n",
       "13       0      0      0      0      0     16      0     27                  1  \n",
       "23       0      0      0      0      0      0      0     34                  1  \n",
       "24       0      0      0      0      0      5      0      4                  1  \n",
       "31       0      0      0      0      0      2      0     31                  1  \n",
       "34       1      0      2      0      0      8      0      5                  1  \n",
       "42       0      0      0      0      0      2      0     31                  1  \n",
       "48       0      0      0      0      0      2      0      9                  1  \n",
       "57       0      1      0      0      0      3      0     24                  1  \n",
       "58       1      2      1      0      0     10      0     14                  1  \n",
       "60       0      0      0      0      0      0      0      4                  1  \n",
       "63       0      0      0      0      0      0      0      1                  1  \n",
       "65       0      0      0      0      0      0      0     15                  1  \n",
       "82       0      0      0      0      0      0      0      4                  0  \n",
       "87       0      0      0      0      0      2      0      1                  0  \n",
       "88       0      0      0      0      0      0      0     13                  0  \n",
       "97       0      0      0      0      0      0      0      5                  0  \n",
       "98       0      0      0      0      0      3      0      2                  0  \n",
       "104      0      0      0      0      0      1      0     65                  0  \n",
       "105      0      1      0      0      0      4      0     31                  0  \n",
       "121      0      0      0      0      0      0      0      2                  0  \n",
       "122      0      0      0      0      0      4      0     43                  0  \n",
       "125      0      0      0      0      0      0      0     48                  1  \n",
       "129      0      1      2      0      0      2      0     22                  1  \n",
       "\n",
       "[26 rows x 2201 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secondT_diffe = binary_data_secondT[secondT_diffe_list]\n",
    "secondT_diffe = secondT_diffe.join(binary_data_secondT[\"adverse_pregnancy\"])\n",
    "secondT_diffe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67be611",
   "metadata": {},
   "source": [
    "# second trimester model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2506ed",
   "metadata": {},
   "source": [
    "## case: pregnancy complications (all diseases) control: healthy pregnancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6dc96549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 2200)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## case: pregnancy complications (all diseases) control: healthy pregnancyfrom sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "X = secondT_diffe.loc[:, secondT_diffe.columns != \"adverse_pregnancy\"].values\n",
    "Y = secondT_diffe[\"adverse_pregnancy\"].values \n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4fd09710",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(26, 29)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(X, Y)\n",
    "model = SelectFromModel(lsvc, prefit=True)\n",
    "X = model.transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "904f4275",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7a236a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "secondT_model = Sequential()\n",
    "secondT_model.add(Dense(8, input_dim=x_train.shape[1], activation='relu'))\n",
    "secondT_model.add(Dense(8, activation='relu'))\n",
    "secondT_model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c4b144e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_15 (Dense)            (None, 8)                 240       \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 8)                 72        \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 321\n",
      "Trainable params: 321\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "secondT_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "44d0dc42",
   "metadata": {},
   "outputs": [],
   "source": [
    "secondT_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eeefed29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "2/2 [==============================] - 1s 162ms/step - loss: 1480.1099 - accuracy: 0.4667 - val_loss: 1404.6547 - val_accuracy: 0.2500\n",
      "Epoch 2/300\n",
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      "Epoch 50/300\n",
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      "Epoch 56/300\n",
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      "Epoch 57/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 25ms/step - loss: 192.4382 - accuracy: 0.5333 - val_loss: 5.6696e-16 - val_accuracy: 1.0000\n",
      "Epoch 58/300\n",
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      "Epoch 114/300\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 25ms/step - loss: 12.3040 - accuracy: 0.6667 - val_loss: 10.5878 - val_accuracy: 0.7500\n",
      "Epoch 115/300\n",
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      "Epoch 125/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 9.0369 - accuracy: 0.7333 - val_loss: 10.3209 - val_accuracy: 0.7500\n",
      "Epoch 126/300\n",
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      "Epoch 128/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 8.4802 - accuracy: 0.6667 - val_loss: 10.1536 - val_accuracy: 0.7500\n",
      "Epoch 129/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 8.1533 - accuracy: 0.6667 - val_loss: 10.0953 - val_accuracy: 0.7500\n",
      "Epoch 130/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 7.9331 - accuracy: 0.6667 - val_loss: 9.8280 - val_accuracy: 0.7500\n",
      "Epoch 131/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 7.6682 - accuracy: 0.6667 - val_loss: 9.5981 - val_accuracy: 0.7500\n",
      "Epoch 132/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 7.5202 - accuracy: 0.6667 - val_loss: 9.4436 - val_accuracy: 0.7500\n",
      "Epoch 133/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 7.2369 - accuracy: 0.6667 - val_loss: 9.3120 - val_accuracy: 0.7500\n",
      "Epoch 134/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 7.0680 - accuracy: 0.6667 - val_loss: 9.3172 - val_accuracy: 0.7500\n",
      "Epoch 135/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 6.9728 - accuracy: 0.6667 - val_loss: 8.7573 - val_accuracy: 0.7500\n",
      "Epoch 136/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 6.7872 - accuracy: 0.7333 - val_loss: 8.2929 - val_accuracy: 0.7500\n",
      "Epoch 137/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 6.6691 - accuracy: 0.7333 - val_loss: 8.0675 - val_accuracy: 0.7500\n",
      "Epoch 138/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 6.5163 - accuracy: 0.7333 - val_loss: 7.9983 - val_accuracy: 0.7500\n",
      "Epoch 139/300\n",
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      "Epoch 150/300\n",
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      "Epoch 154/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.7791 - accuracy: 0.8667 - val_loss: 6.8817 - val_accuracy: 0.7500\n",
      "Epoch 155/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.7348 - accuracy: 0.8667 - val_loss: 6.6598 - val_accuracy: 0.7500\n",
      "Epoch 156/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.6062 - accuracy: 0.8667 - val_loss: 6.2974 - val_accuracy: 0.7500\n",
      "Epoch 157/300\n",
      "2/2 [==============================] - 0s 64ms/step - loss: 4.4125 - accuracy: 0.8667 - val_loss: 5.8676 - val_accuracy: 0.7500\n",
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      "Epoch 168/300\n",
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      "Epoch 171/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.6900 - accuracy: 0.8000 - val_loss: 3.1746 - val_accuracy: 0.7500\n",
      "Epoch 172/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.6321 - accuracy: 0.8667 - val_loss: 2.9123 - val_accuracy: 0.7500\n",
      "Epoch 173/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.6225 - accuracy: 0.8000 - val_loss: 2.7260 - val_accuracy: 0.7500\n",
      "Epoch 174/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 2.5639 - accuracy: 0.8667 - val_loss: 2.7872 - val_accuracy: 0.7500\n",
      "Epoch 175/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 2.5044 - accuracy: 0.8667 - val_loss: 3.0859 - val_accuracy: 0.7500\n",
      "Epoch 176/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.4380 - accuracy: 0.9333 - val_loss: 3.2946 - val_accuracy: 0.7500\n",
      "Epoch 177/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.4407 - accuracy: 0.9333 - val_loss: 3.3634 - val_accuracy: 0.7500\n",
      "Epoch 178/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.4441 - accuracy: 0.9333 - val_loss: 3.3251 - val_accuracy: 0.7500\n",
      "Epoch 179/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.4293 - accuracy: 0.9333 - val_loss: 3.1944 - val_accuracy: 0.7500\n",
      "Epoch 180/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.3989 - accuracy: 0.9333 - val_loss: 2.9856 - val_accuracy: 0.7500\n",
      "Epoch 181/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.3399 - accuracy: 0.9333 - val_loss: 2.7177 - val_accuracy: 0.7500\n",
      "Epoch 182/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 2.3371 - accuracy: 0.9333 - val_loss: 2.3911 - val_accuracy: 0.7500\n",
      "Epoch 183/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.2771 - accuracy: 0.8667 - val_loss: 2.2527 - val_accuracy: 0.7500\n",
      "Epoch 184/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.2661 - accuracy: 0.8667 - val_loss: 2.2040 - val_accuracy: 0.7500\n",
      "Epoch 185/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.2407 - accuracy: 0.8667 - val_loss: 2.1946 - val_accuracy: 0.7500\n",
      "Epoch 186/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.2205 - accuracy: 0.9333 - val_loss: 2.1959 - val_accuracy: 0.7500\n",
      "Epoch 187/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.1972 - accuracy: 0.9333 - val_loss: 2.2695 - val_accuracy: 0.7500\n",
      "Epoch 188/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.1879 - accuracy: 0.9333 - val_loss: 2.2607 - val_accuracy: 0.7500\n",
      "Epoch 189/300\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 2.1760 - accuracy: 0.9333 - val_loss: 2.1759 - val_accuracy: 0.7500\n",
      "Epoch 190/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.1495 - accuracy: 0.9333 - val_loss: 2.0240 - val_accuracy: 0.7500\n",
      "Epoch 191/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.1286 - accuracy: 0.9333 - val_loss: 1.7369 - val_accuracy: 0.7500\n",
      "Epoch 192/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.1485 - accuracy: 0.8667 - val_loss: 1.6141 - val_accuracy: 0.7500\n",
      "Epoch 193/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 2.0843 - accuracy: 0.8667 - val_loss: 1.7482 - val_accuracy: 0.7500\n",
      "Epoch 194/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.0813 - accuracy: 0.9333 - val_loss: 1.8288 - val_accuracy: 0.7500\n",
      "Epoch 195/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.0592 - accuracy: 0.9333 - val_loss: 1.7121 - val_accuracy: 0.7500\n",
      "Epoch 196/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.0408 - accuracy: 0.9333 - val_loss: 1.5730 - val_accuracy: 0.7500\n",
      "Epoch 197/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.0083 - accuracy: 0.9333 - val_loss: 1.4727 - val_accuracy: 0.7500\n",
      "Epoch 198/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.9839 - accuracy: 0.9333 - val_loss: 1.3481 - val_accuracy: 0.7500\n",
      "Epoch 199/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.9921 - accuracy: 0.8667 - val_loss: 1.1719 - val_accuracy: 0.7500\n",
      "Epoch 200/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.9475 - accuracy: 0.8667 - val_loss: 1.1764 - val_accuracy: 0.7500\n",
      "Epoch 201/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.9270 - accuracy: 0.9333 - val_loss: 1.1922 - val_accuracy: 0.7500\n",
      "Epoch 202/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.9028 - accuracy: 0.9333 - val_loss: 1.2570 - val_accuracy: 0.7500\n",
      "Epoch 203/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 1.9104 - accuracy: 0.9333 - val_loss: 1.2880 - val_accuracy: 0.7500\n",
      "Epoch 204/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.8831 - accuracy: 0.9333 - val_loss: 1.1079 - val_accuracy: 0.7500\n",
      "Epoch 205/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.8747 - accuracy: 0.9333 - val_loss: 0.8342 - val_accuracy: 0.7500\n",
      "Epoch 206/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 1.8251 - accuracy: 0.9333 - val_loss: 0.6975 - val_accuracy: 0.7500\n",
      "Epoch 207/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 1.8038 - accuracy: 0.9333 - val_loss: 0.6236 - val_accuracy: 0.7500\n",
      "Epoch 208/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.7821 - accuracy: 0.8667 - val_loss: 0.4875 - val_accuracy: 0.7500\n",
      "Epoch 209/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.7931 - accuracy: 0.8667 - val_loss: 0.4756 - val_accuracy: 0.7500\n",
      "Epoch 210/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 1.7605 - accuracy: 0.8667 - val_loss: 0.6525 - val_accuracy: 0.7500\n",
      "Epoch 211/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.7241 - accuracy: 0.9333 - val_loss: 0.6371 - val_accuracy: 0.7500\n",
      "Epoch 212/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.7106 - accuracy: 0.9333 - val_loss: 0.5793 - val_accuracy: 0.7500\n",
      "Epoch 213/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.6718 - accuracy: 0.9333 - val_loss: 0.3646 - val_accuracy: 0.7500\n",
      "Epoch 214/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.6491 - accuracy: 0.9333 - val_loss: 0.1448 - val_accuracy: 1.0000\n",
      "Epoch 215/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.6302 - accuracy: 0.8667 - val_loss: 0.0763 - val_accuracy: 1.0000\n",
      "Epoch 216/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.6289 - accuracy: 0.8667 - val_loss: 0.0758 - val_accuracy: 1.0000\n",
      "Epoch 217/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.5813 - accuracy: 0.8667 - val_loss: 0.0639 - val_accuracy: 1.0000\n",
      "Epoch 218/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.5545 - accuracy: 0.8667 - val_loss: 0.0733 - val_accuracy: 1.0000\n",
      "Epoch 219/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.5165 - accuracy: 0.9333 - val_loss: 0.1324 - val_accuracy: 1.0000\n",
      "Epoch 220/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 1.5089 - accuracy: 0.9333 - val_loss: 0.1596 - val_accuracy: 1.0000\n",
      "Epoch 221/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.5089 - accuracy: 0.9333 - val_loss: 0.1331 - val_accuracy: 1.0000\n",
      "Epoch 222/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.4869 - accuracy: 0.9333 - val_loss: 0.0539 - val_accuracy: 1.0000\n",
      "Epoch 223/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.4385 - accuracy: 0.9333 - val_loss: 0.0179 - val_accuracy: 1.0000\n",
      "Epoch 224/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.4017 - accuracy: 0.9333 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
      "Epoch 225/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.3857 - accuracy: 0.8667 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
      "Epoch 226/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.3844 - accuracy: 0.8667 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
      "Epoch 227/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.3766 - accuracy: 0.8667 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
      "Epoch 228/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.4129 - accuracy: 0.8000 - val_loss: 0.0015 - val_accuracy: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 229/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.4512 - accuracy: 0.8000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
      "Epoch 230/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.3521 - accuracy: 0.8000 - val_loss: 0.0125 - val_accuracy: 1.0000\n",
      "Epoch 231/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.3053 - accuracy: 0.9333 - val_loss: 0.0818 - val_accuracy: 1.0000\n",
      "Epoch 232/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.3746 - accuracy: 0.9333 - val_loss: 0.2000 - val_accuracy: 0.7500\n",
      "Epoch 233/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.4177 - accuracy: 0.9333 - val_loss: 0.2665 - val_accuracy: 0.7500\n",
      "Epoch 234/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.4342 - accuracy: 0.9333 - val_loss: 0.2403 - val_accuracy: 0.7500\n",
      "Epoch 235/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.4137 - accuracy: 0.9333 - val_loss: 0.1074 - val_accuracy: 1.0000\n",
      "Epoch 236/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.3301 - accuracy: 0.9333 - val_loss: 0.0306 - val_accuracy: 1.0000\n",
      "Epoch 237/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.2409 - accuracy: 0.9333 - val_loss: 0.0038 - val_accuracy: 1.0000\n",
      "Epoch 238/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.2097 - accuracy: 0.9333 - val_loss: 3.9546e-04 - val_accuracy: 1.0000\n",
      "Epoch 239/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.2019 - accuracy: 0.8667 - val_loss: 1.3760e-04 - val_accuracy: 1.0000\n",
      "Epoch 240/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.1375 - accuracy: 0.8667 - val_loss: 2.0070e-04 - val_accuracy: 1.0000\n",
      "Epoch 241/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.0863 - accuracy: 0.8667 - val_loss: 3.3298e-04 - val_accuracy: 1.0000\n",
      "Epoch 242/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 1.0623 - accuracy: 0.9333 - val_loss: 8.0628e-04 - val_accuracy: 1.0000\n",
      "Epoch 243/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.0863 - accuracy: 0.9333 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 244/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 1.0720 - accuracy: 0.9333 - val_loss: 5.2566e-04 - val_accuracy: 1.0000\n",
      "Epoch 245/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 1.0371 - accuracy: 0.9333 - val_loss: 2.0183e-04 - val_accuracy: 1.0000\n",
      "Epoch 246/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.9894 - accuracy: 0.9333 - val_loss: 7.3732e-05 - val_accuracy: 1.0000\n",
      "Epoch 247/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.9544 - accuracy: 0.9333 - val_loss: 2.8652e-05 - val_accuracy: 1.0000\n",
      "Epoch 248/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.9383 - accuracy: 0.8667 - val_loss: 1.6020e-05 - val_accuracy: 1.0000\n",
      "Epoch 249/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.9343 - accuracy: 0.8667 - val_loss: 1.3984e-05 - val_accuracy: 1.0000\n",
      "Epoch 250/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.8983 - accuracy: 0.8667 - val_loss: 9.6646e-06 - val_accuracy: 1.0000\n",
      "Epoch 251/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.8736 - accuracy: 0.8667 - val_loss: 7.0085e-06 - val_accuracy: 1.0000\n",
      "Epoch 252/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.8589 - accuracy: 0.8667 - val_loss: 8.8910e-06 - val_accuracy: 1.0000\n",
      "Epoch 253/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.8183 - accuracy: 0.9333 - val_loss: 1.9891e-05 - val_accuracy: 1.0000\n",
      "Epoch 254/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.8142 - accuracy: 0.9333 - val_loss: 2.5867e-05 - val_accuracy: 1.0000\n",
      "Epoch 255/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.8123 - accuracy: 0.9333 - val_loss: 2.0234e-05 - val_accuracy: 1.0000\n",
      "Epoch 256/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.7936 - accuracy: 0.9333 - val_loss: 1.0603e-05 - val_accuracy: 1.0000\n",
      "Epoch 257/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.7439 - accuracy: 0.9333 - val_loss: 4.2578e-06 - val_accuracy: 1.0000\n",
      "Epoch 258/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.7156 - accuracy: 0.9333 - val_loss: 9.6058e-07 - val_accuracy: 1.0000\n",
      "Epoch 259/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.6619 - accuracy: 0.8667 - val_loss: 3.0298e-07 - val_accuracy: 1.0000\n",
      "Epoch 260/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.6702 - accuracy: 0.8667 - val_loss: 1.1802e-07 - val_accuracy: 1.0000\n",
      "Epoch 261/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.7082 - accuracy: 0.8667 - val_loss: 8.9086e-08 - val_accuracy: 1.0000\n",
      "Epoch 262/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.6446 - accuracy: 0.8667 - val_loss: 2.3277e-07 - val_accuracy: 1.0000\n",
      "Epoch 263/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.5825 - accuracy: 0.8667 - val_loss: 1.3115e-06 - val_accuracy: 1.0000\n",
      "Epoch 264/300\n",
      "2/2 [==============================] - 0s 36ms/step - loss: 0.5936 - accuracy: 0.9333 - val_loss: 3.2560e-06 - val_accuracy: 1.0000\n",
      "Epoch 265/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.6243 - accuracy: 0.9333 - val_loss: 4.1877e-06 - val_accuracy: 1.0000\n",
      "Epoch 266/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.6278 - accuracy: 0.9333 - val_loss: 2.9544e-06 - val_accuracy: 1.0000\n",
      "Epoch 267/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.5796 - accuracy: 0.9333 - val_loss: 7.7293e-07 - val_accuracy: 1.0000\n",
      "Epoch 268/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.5340 - accuracy: 0.9333 - val_loss: 9.5257e-08 - val_accuracy: 1.0000\n",
      "Epoch 269/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.4978 - accuracy: 0.8667 - val_loss: 2.1092e-08 - val_accuracy: 1.0000\n",
      "Epoch 270/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.4318 - accuracy: 0.8667 - val_loss: 1.4782e-08 - val_accuracy: 1.0000\n",
      "Epoch 271/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.4151 - accuracy: 0.8667 - val_loss: 1.3406e-08 - val_accuracy: 1.0000\n",
      "Epoch 272/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.3699 - accuracy: 0.8667 - val_loss: 9.1084e-09 - val_accuracy: 1.0000\n",
      "Epoch 273/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.3607 - accuracy: 0.8667 - val_loss: 8.8506e-09 - val_accuracy: 1.0000\n",
      "Epoch 274/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.3201 - accuracy: 0.9333 - val_loss: 5.1659e-09 - val_accuracy: 1.0000\n",
      "Epoch 275/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.3065 - accuracy: 0.8667 - val_loss: 3.9629e-09 - val_accuracy: 1.0000\n",
      "Epoch 276/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.2634 - accuracy: 0.9333 - val_loss: 5.7730e-09 - val_accuracy: 1.0000\n",
      "Epoch 277/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2467 - accuracy: 0.9333 - val_loss: 5.8410e-09 - val_accuracy: 1.0000\n",
      "Epoch 278/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.2342 - accuracy: 0.9333 - val_loss: 4.1388e-09 - val_accuracy: 1.0000\n",
      "Epoch 279/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.2115 - accuracy: 0.9333 - val_loss: 2.4998e-09 - val_accuracy: 1.0000\n",
      "Epoch 280/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1770 - accuracy: 0.9333 - val_loss: 1.2369e-09 - val_accuracy: 1.0000\n",
      "Epoch 281/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1765 - accuracy: 0.9333 - val_loss: 5.4444e-10 - val_accuracy: 1.0000\n",
      "Epoch 282/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1448 - accuracy: 0.9333 - val_loss: 4.5043e-10 - val_accuracy: 1.0000\n",
      "Epoch 283/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1316 - accuracy: 0.9333 - val_loss: 4.0766e-10 - val_accuracy: 1.0000\n",
      "Epoch 284/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1182 - accuracy: 0.9333 - val_loss: 2.8440e-10 - val_accuracy: 1.0000\n",
      "Epoch 285/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1088 - accuracy: 1.0000 - val_loss: 2.3339e-10 - val_accuracy: 1.0000\n",
      "Epoch 286/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.1013 - accuracy: 1.0000 - val_loss: 2.1800e-10 - val_accuracy: 1.0000\n",
      "Epoch 287/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0930 - accuracy: 1.0000 - val_loss: 1.7833e-10 - val_accuracy: 1.0000\n",
      "Epoch 288/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0875 - accuracy: 1.0000 - val_loss: 1.6468e-10 - val_accuracy: 1.0000\n",
      "Epoch 289/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.0822 - accuracy: 1.0000 - val_loss: 1.4043e-10 - val_accuracy: 1.0000\n",
      "Epoch 290/300\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 0.0784 - accuracy: 1.0000 - val_loss: 1.3803e-10 - val_accuracy: 1.0000\n",
      "Epoch 291/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0734 - accuracy: 1.0000 - val_loss: 1.2388e-10 - val_accuracy: 1.0000\n",
      "Epoch 292/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0710 - accuracy: 1.0000 - val_loss: 1.0795e-10 - val_accuracy: 1.0000\n",
      "Epoch 293/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.0688 - accuracy: 1.0000 - val_loss: 1.0684e-10 - val_accuracy: 1.0000\n",
      "Epoch 294/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0650 - accuracy: 1.0000 - val_loss: 1.2697e-10 - val_accuracy: 1.0000\n",
      "Epoch 295/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0628 - accuracy: 1.0000 - val_loss: 1.5917e-10 - val_accuracy: 1.0000\n",
      "Epoch 296/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0623 - accuracy: 1.0000 - val_loss: 1.9167e-10 - val_accuracy: 1.0000\n",
      "Epoch 297/300\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 0.0629 - accuracy: 1.0000 - val_loss: 2.1271e-10 - val_accuracy: 1.0000\n",
      "Epoch 298/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.0628 - accuracy: 1.0000 - val_loss: 2.0924e-10 - val_accuracy: 1.0000\n",
      "Epoch 299/300\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 0.0620 - accuracy: 1.0000 - val_loss: 1.8717e-10 - val_accuracy: 1.0000\n",
      "Epoch 300/300\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 0.0604 - accuracy: 1.0000 - val_loss: 1.6004e-10 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "history = secondT_model.fit(x_train, y_train, epochs=300,  validation_split=0.2, batch_size=10, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9dfc5b",
   "metadata": {},
   "source": [
    "### model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "90d53e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24.408876419067383, 0.7142857313156128]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = secondT_model.evaluate(x_test, y_test, verbose=0)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56968f1",
   "metadata": {},
   "source": [
    "### plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6dbc415d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"], label=\"train\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"validation\")\n",
    "plt.title(\"model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\"], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "23a3977a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 58ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = secondT_model.predict(x_test)\n",
    "predictions.round(1)\n",
    "confusion_matrix = sklearn.metrics.confusion_matrix(y_test, np.rint(predictions))\n",
    "sns.heatmap(confusion_matrix, annot=True, cmap=\"Reds\")\n",
    "plt.xlabel(\"model prediction\")\n",
    "plt.ylabel(\"truth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50dc5ec",
   "metadata": {},
   "source": [
    "# third trimester data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a38ae550",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>37</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>205</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
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       "      <td>...</td>\n",
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       "      <td>59</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>139</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 57737 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1  2  3   4  5  6  7  8  9  ...  57727  57728  57729  57730  57731  \\\n",
       "11    0  0  0  0   0  0  0  3  0  0  ...      0      5      0      0      0   \n",
       "12    0  0  1  0   5  0  0  2  0  2  ...      0     35      0      0      1   \n",
       "14    0  4  0  0   0  0  0  0  0  0  ...      0     42      0      0      0   \n",
       "15    1  0  0  0   2  0  0  0  0  2  ...      2     59      0      0      0   \n",
       "29    0  1  0  0   0  5  1  2  0  0  ...      4     83      0      0      4   \n",
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       "35   34  2  0  0   0  0  0  0  0  4  ...      0     11      0      0      0   \n",
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       "40    1  0  0  0   0  0  0  0  0  0  ...      0     20      0      0      0   \n",
       "46    2  8  0  0   0  0  0  0  0  0  ...      0     29      0      0      0   \n",
       "55    1  0  0  0   0  0  0  0  0  1  ...      0     18      0      0      0   \n",
       "68   43  0  0  0   9  0  0  0  0  1  ...      0      2      0      0      0   \n",
       "69    4  1  0  0   4  0  0  0  0  1  ...      3     22      0      0      0   \n",
       "72    0  5  0  0   1  0  0  0  0  0  ...      0     18      0      0      0   \n",
       "81    0  0  0  0   0  0  0  0  0  1  ...      0      0      0      0      0   \n",
       "86    6  0  0  0   0  0  0  0  0  0  ...      0      9      0      0      0   \n",
       "96    0  2  0  0   0  0  0  0  0  0  ...      0     53      0      0      0   \n",
       "101   0  2  0  0   0  0  0  0  0  2  ...      0    272      0      0      0   \n",
       "102  20  2  0  0   0  0  0  7  0  0  ...      0      8      0      0      0   \n",
       "106   0  0  0  0  10  0  0  0  0  0  ...      0     52      1      0      0   \n",
       "107   0  1  0  0   0  0  0  0  0  0  ...      0    130      0      0      0   \n",
       "112   1  0  0  0   0  0  0  0  0  0  ...      0     44      0      0      0   \n",
       "115   0  0  0  0   0  0  0  4  0  0  ...      0     27      0      0      1   \n",
       "127   0  0  0  0   0  0  0  1  0  0  ...      0     59      0      0      0   \n",
       "131   0  2  0  0   3  1  0  2  0  0  ...      0     30      0      0      0   \n",
       "\n",
       "     57732  57733  57734  57735  adverse_pregnancy  \n",
       "11       3     46      1      0                  1  \n",
       "12       9    127      0      0                  1  \n",
       "14       0    213      3      0                  1  \n",
       "15      11    123      0      0                  1  \n",
       "29      13     74     10      0                  1  \n",
       "33       6    175      0      0                  1  \n",
       "35       1    109      1      0                  1  \n",
       "36       1    122      0      0                  1  \n",
       "40       3     71      0      0                  1  \n",
       "46       0    116      0      0                  1  \n",
       "55       0     37      0      0                  1  \n",
       "68       5     77      0      0                  1  \n",
       "69       0     50      5      0                  1  \n",
       "72       1     69      0      0                  1  \n",
       "81       0      0      0      0                  0  \n",
       "86       0     40      0      0                  0  \n",
       "96       3    102      1      0                  0  \n",
       "101     30    570      0      0                  0  \n",
       "102      5     96      0      0                  0  \n",
       "106     26     70      2      0                  0  \n",
       "107     26    422      0      0                  0  \n",
       "112      5    205      8      0                  0  \n",
       "115      5    116      1      0                  0  \n",
       "127      3    146      0      0                  1  \n",
       "131      5    139      3      0                  1  \n",
       "\n",
       "[25 rows x 57737 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_trimester_data[\"adverse_pregnancy\"] = np.where(third_trimester_data.disease.str.contains(\"Normal-Pregnancy\"), 0, 1)\n",
    "binary_data_thirdT = third_trimester_data.drop([\"disease\"], axis=1)\n",
    "binary_data_thirdT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f8b1798",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 35394,\n",
       " 35398,\n",
       " 2633,\n",
       " 2634,\n",
       " 2636,\n",
       " 2641,\n",
       " 2651,\n",
       " 2654,\n",
       " 2655,\n",
       " 35429,\n",
       " 2665,\n",
       " 2669,\n",
       " 35445,\n",
       " 2677,\n",
       " 35449,\n",
       " 35452,\n",
       " 2686,\n",
       " 35460,\n",
       " 2696,\n",
       " 35470,\n",
       " 2710,\n",
       " 2715,\n",
       " 2717,\n",
       " 2720,\n",
       " 2725,\n",
       " 35497,\n",
       " 2736,\n",
       " 35507,\n",
       " 35513,\n",
       " 35515,\n",
       " 35518,\n",
       " 2751,\n",
       " 2754,\n",
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       " 2759,\n",
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       " 2781,\n",
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       " 2785,\n",
       " 35552,\n",
       " 35555,\n",
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       " 35565,\n",
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       " 2802,\n",
       " 35572,\n",
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       " 35578,\n",
       " 2812,\n",
       " 2813,\n",
       " 35581,\n",
       " 35590,\n",
       " 35591,\n",
       " 35592,\n",
       " 35594,\n",
       " 35596,\n",
       " 35598,\n",
       " 35603,\n",
       " 2840,\n",
       " 35610,\n",
       " 35614,\n",
       " 35627,\n",
       " 2869,\n",
       " 2875,\n",
       " 35646,\n",
       " 2881,\n",
       " 2882,\n",
       " 35650,\n",
       " 35652,\n",
       " 35658,\n",
       " 35667,\n",
       " 35670,\n",
       " 2905,\n",
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       " 35678,\n",
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       " 35694,\n",
       " 35695,\n",
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       " 35708,\n",
       " 35709,\n",
       " 35714,\n",
       " 35734,\n",
       " 35737,\n",
       " 35757,\n",
       " 2991,\n",
       " 2992,\n",
       " 35761,\n",
       " 2996,\n",
       " 2997,\n",
       " 2998,\n",
       " 2999,\n",
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       " 35775,\n",
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       " 35796,\n",
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       " 3142,\n",
       " 3148,\n",
       " 35917,\n",
       " 3151,\n",
       " 35927,\n",
       " 35928,\n",
       " 35930,\n",
       " 3166,\n",
       " 35935,\n",
       " 3173,\n",
       " 35948,\n",
       " 3183,\n",
       " 3184,\n",
       " 35951,\n",
       " 3186,\n",
       " 3187,\n",
       " 3191,\n",
       " 3192,\n",
       " 35965,\n",
       " 35967,\n",
       " 3202,\n",
       " 35970,\n",
       " 3205,\n",
       " 35980,\n",
       " 3232,\n",
       " 36007,\n",
       " 3239,\n",
       " 3241,\n",
       " 36009,\n",
       " 3244,\n",
       " 3246,\n",
       " 36020,\n",
       " 3253,\n",
       " 3255,\n",
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       " 3266,\n",
       " 3268,\n",
       " 36040,\n",
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       " 3284,\n",
       " 36066,\n",
       " 36070,\n",
       " 3308,\n",
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       " 36080,\n",
       " 36085,\n",
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       " 3323,\n",
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       " 3365,\n",
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       " 3382,\n",
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       " 3394,\n",
       " 3396,\n",
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       " 3403,\n",
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       " 3406,\n",
       " 3418,\n",
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       " 36191,\n",
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       " 36195,\n",
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       " 36198,\n",
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       " 3449,\n",
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       " 3555,\n",
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       " 36335,\n",
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       " 3570,\n",
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       " 36344,\n",
       " 36348,\n",
       " 3581,\n",
       " 36350,\n",
       " 3583,\n",
       " 3589,\n",
       " 36361,\n",
       " 3594,\n",
       " 36366,\n",
       " 36370,\n",
       " 3604,\n",
       " 3605,\n",
       " 36383,\n",
       " 3616,\n",
       " 36386,\n",
       " 3630,\n",
       " 36398,\n",
       " 36399,\n",
       " 36400,\n",
       " 3634,\n",
       " 3638,\n",
       " 3639,\n",
       " 36406,\n",
       " 36409,\n",
       " 36410,\n",
       " 3643,\n",
       " 3642,\n",
       " 3649,\n",
       " 3655,\n",
       " 36431,\n",
       " ...]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preeclampsia_dif_exp_thirdT = pd.read_csv(\"preeclamspsia_diffe_thirdT.txt\", sep=\" \")\n",
    "preeclampsia_dif_exp_thirdT = preeclampsia_dif_exp_thirdT[preeclampsia_dif_exp_thirdT[\"PValue\"] <= 0.05]\n",
    "preeclampsia_dif_exp_thirdT = list(preeclampsia_dif_exp_thirdT.index)\n",
    "\n",
    "gestational_diabetes_dif_exp_thirdT = pd.read_csv(\"gestational_diabetes_diffe_thirdT.txt\", sep=\" \")\n",
    "gestational_diabetes_dif_exp_thirdT = gestational_diabetes_dif_exp_thirdT[gestational_diabetes_dif_exp_thirdT[\"PValue\"] <= 0.05]\n",
    "gestational_diabetes_dif_exp_thirdT = list(gestational_diabetes_dif_exp_thirdT.index)\n",
    "\n",
    "chronic_hypertension_dif_exp_thirdT = pd.read_csv(\"chronic_hypertension_diffe_thirdT.txt\", sep=\" \")\n",
    "chronic_hypertension_dif_exp_thirdT = chronic_hypertension_dif_exp_thirdT[chronic_hypertension_dif_exp_thirdT[\"PValue\"] <= 0.05]\n",
    "chronic_hypertension_dif_exp_thirdT = list(chronic_hypertension_dif_exp_thirdT.index)\n",
    "\n",
    "thirdT_diffe_list = list(set(preeclampsia_dif_exp_thirdT + gestational_diabetes_dif_exp_thirdT + chronic_hypertension_dif_exp_thirdT))\n",
    "thirdT_diffe_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3c109e42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>299</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8905</td>\n",
       "      <td>3343</td>\n",
       "      <td>...</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>546</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>757</td>\n",
       "      <td>...</td>\n",
       "      <td>310</td>\n",
       "      <td>0</td>\n",
       "      <td>2487</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>2932</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>406</td>\n",
       "      <td>...</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>1059</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>254</td>\n",
       "      <td>...</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>378</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1038</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>496</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>654</td>\n",
       "      <td>903</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>245</td>\n",
       "      <td>4842</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>424</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 7361 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     32772  32788  20  32790  32792  32795  32796  32801    35    36  ...  \\\n",
       "11       0      9   0      0      0      1      0      2    22    21  ...   \n",
       "12       3     15   0      0     12      4      7      2   146   172  ...   \n",
       "14       0      0   0      0      0      0      0      0    12    23  ...   \n",
       "15       2     18   0      0      8      4      4      0    93    91  ...   \n",
       "29       1     42   0      3      1      4     24     11    28    34  ...   \n",
       "33       0      5   0      0      0      0      0      0   213  3504  ...   \n",
       "35       0      1   0      0      0      0      0      0   128   245  ...   \n",
       "36       0      0   0      0      0      0      0      0    77  1133  ...   \n",
       "40       0      0   0      0      1      0      0      0    11    21  ...   \n",
       "46       0      0   0      0      0      0      0      0   176   398  ...   \n",
       "55       0      3   0      0      0      0      0      0     7    37  ...   \n",
       "68       0     20   0      0      1      5      7      1    48    47  ...   \n",
       "69       0     15   0      0     17      4     11      2    40    17  ...   \n",
       "72       0      3   0      0      0      0      1      0    29    35  ...   \n",
       "81       0      0   0      0      0      0      0      0    15    37  ...   \n",
       "86       0      0   0      0      0      0      0      0    26    34  ...   \n",
       "96       0      2   0      0      1      0      0      0  8905  3343  ...   \n",
       "101      0      0   0      0      0      0      0      0   277   757  ...   \n",
       "102      0      1   0      0      0      0      0      0   142  2932  ...   \n",
       "106      0     22   0      0      3      0      1      0    80    56  ...   \n",
       "107      0      1   0      0      0      0      0      1   191   406  ...   \n",
       "112      0      9   0      0      0      0      0      0   104   254  ...   \n",
       "115      0      6   0      0      0      0     10      0    45  1038  ...   \n",
       "127      0      0   0      0      0      0      0      0   654   903  ...   \n",
       "131      1     13   0      0     12      0     19      5   245  4842  ...   \n",
       "\n",
       "     32701  32702  32704  32708  32722  32725  32740  32741  32743  \\\n",
       "11       8      0    111      3      0      0      0      9      0   \n",
       "12      59      0    336      1      0      0      4     11      0   \n",
       "14      41      0    433      0      0      3      0     20      0   \n",
       "15      83      0    354      4      0      4      3     28      2   \n",
       "29      56      3    134      0      0      2     29     32      6   \n",
       "33      53      0    611      1      0     16      0     47      0   \n",
       "35      61      0    479      2      0      4      0     24      0   \n",
       "36      32      0    533      0      0      3      0     26      0   \n",
       "40      96      0    214      0      0      1      0     10      0   \n",
       "46     129      0    521      0      0     22      0     19      0   \n",
       "55      21      0    185      0      0      0      0     22      0   \n",
       "68      64      0    175      1      0      0      2      5      0   \n",
       "69      43      0    125      0      0      2      4     13      0   \n",
       "72      34      0    299      2      0      9      0     22      0   \n",
       "81       0      0      5      0      0      0      0     24      0   \n",
       "86      85      0    188      0      0      0      0     16      0   \n",
       "96     105      0    546      1      0      5      1     28      0   \n",
       "101    310      0   2487      0      0      3      0     44      0   \n",
       "102     56      0    278      0      0      2      0     22      0   \n",
       "106     60      0    324      1      0     19      0     11      0   \n",
       "107    257      0   1059      2      0     26      0     58      0   \n",
       "112    159      0    378      0      0      2      0     61      0   \n",
       "115     44      0    496      0      0      2      0     26      0   \n",
       "127     45      0    264      0      0      0      0     17      0   \n",
       "131     84      1    424      2      0      2     11     28      0   \n",
       "\n",
       "     adverse_pregnancy  \n",
       "11                   1  \n",
       "12                   1  \n",
       "14                   1  \n",
       "15                   1  \n",
       "29                   1  \n",
       "33                   1  \n",
       "35                   1  \n",
       "36                   1  \n",
       "40                   1  \n",
       "46                   1  \n",
       "55                   1  \n",
       "68                   1  \n",
       "69                   1  \n",
       "72                   1  \n",
       "81                   0  \n",
       "86                   0  \n",
       "96                   0  \n",
       "101                  0  \n",
       "102                  0  \n",
       "106                  0  \n",
       "107                  0  \n",
       "112                  0  \n",
       "115                  0  \n",
       "127                  1  \n",
       "131                  1  \n",
       "\n",
       "[25 rows x 7361 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thirdT_diffe = binary_data_thirdT[thirdT_diffe_list]\n",
    "thirdT_diffe = thirdT_diffe.join(binary_data_thirdT[\"adverse_pregnancy\"])\n",
    "thirdT_diffe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f27909c",
   "metadata": {},
   "source": [
    "# third trimester model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5403e1",
   "metadata": {},
   "source": [
    "## case: pregnancy complications (all diseases) control: healthy pregnancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "10c75630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 7360)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "X = thirdT_diffe.loc[:, thirdT_diffe.columns != \"adverse_pregnancy\"].values\n",
    "Y = thirdT_diffe[\"adverse_pregnancy\"].values \n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "41d87706",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/svm/_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25, 21)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(X, Y)\n",
    "model = SelectFromModel(lsvc, prefit=True)\n",
    "X = model.transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c7bcb6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a8c65043",
   "metadata": {},
   "outputs": [],
   "source": [
    "thirdT_model = Sequential()\n",
    "thirdT_model.add(Dense(8, input_dim=x_train.shape[1], activation='relu'))\n",
    "thirdT_model.add(Dense(8, activation='relu'))\n",
    "thirdT_model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "300daafe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_18 (Dense)            (None, 8)                 176       \n",
      "                                                                 \n",
      " dense_19 (Dense)            (None, 8)                 72        \n",
      "                                                                 \n",
      " dense_20 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 257\n",
      "Trainable params: 257\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "thirdT_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ce243a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "thirdT_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "15140960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "2/2 [==============================] - 1s 169ms/step - loss: 598.8491 - accuracy: 0.5000 - val_loss: 600.0736 - val_accuracy: 0.2500\n",
      "Epoch 2/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 520.7585 - accuracy: 0.5000 - val_loss: 570.3906 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 478.7435 - accuracy: 0.5714 - val_loss: 551.5676 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 432.0530 - accuracy: 0.5714 - val_loss: 534.3549 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 390.9300 - accuracy: 0.6429 - val_loss: 514.2747 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 353.7612 - accuracy: 0.6429 - val_loss: 490.7762 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 312.6929 - accuracy: 0.6429 - val_loss: 467.6489 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 285.9939 - accuracy: 0.6429 - val_loss: 443.3744 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 251.6324 - accuracy: 0.6429 - val_loss: 420.2966 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 222.6910 - accuracy: 0.5714 - val_loss: 399.2338 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 197.6276 - accuracy: 0.5714 - val_loss: 379.3407 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 172.6967 - accuracy: 0.5714 - val_loss: 359.2513 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 166.5391 - accuracy: 0.6429 - val_loss: 339.8969 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 144.6091 - accuracy: 0.6429 - val_loss: 323.1430 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 137.4334 - accuracy: 0.6429 - val_loss: 306.9471 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/200\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 135.2811 - accuracy: 0.6429 - val_loss: 294.4245 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/200\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 136.2560 - accuracy: 0.6429 - val_loss: 285.1769 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 135.6207 - accuracy: 0.6429 - val_loss: 278.9334 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/200\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 131.0153 - accuracy: 0.7143 - val_loss: 275.2660 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 126.8805 - accuracy: 0.7143 - val_loss: 273.0015 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 120.9162 - accuracy: 0.7143 - val_loss: 271.5699 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 116.4057 - accuracy: 0.7143 - val_loss: 271.0862 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 112.5376 - accuracy: 0.7143 - val_loss: 271.3107 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/200\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 106.6096 - accuracy: 0.7143 - val_loss: 271.9674 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/200\n",
      "2/2 [==============================] - 0s 31ms/step - loss: 103.4536 - accuracy: 0.7143 - val_loss: 273.6333 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 98.3938 - accuracy: 0.7143 - val_loss: 275.5964 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 92.9007 - accuracy: 0.7143 - val_loss: 277.8545 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 91.1478 - accuracy: 0.6429 - val_loss: 278.4766 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/200\n",
      "2/2 [==============================] - 0s 30ms/step - loss: 91.6220 - accuracy: 0.6429 - val_loss: 277.3033 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 89.2547 - accuracy: 0.6429 - val_loss: 273.5360 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 84.7638 - accuracy: 0.6429 - val_loss: 269.1512 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 81.9126 - accuracy: 0.7143 - val_loss: 266.0071 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 80.9983 - accuracy: 0.7143 - val_loss: 264.4200 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 79.7318 - accuracy: 0.7143 - val_loss: 264.6738 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/200\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 76.5470 - accuracy: 0.7143 - val_loss: 265.2917 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/200\n",
      "2/2 [==============================] - 0s 32ms/step - loss: 73.9035 - accuracy: 0.7143 - val_loss: 266.2934 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 72.2845 - accuracy: 0.6429 - val_loss: 265.0413 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 69.0302 - accuracy: 0.6429 - val_loss: 260.6922 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 69.8628 - accuracy: 0.7143 - val_loss: 255.6758 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 69.3442 - accuracy: 0.7143 - val_loss: 253.1454 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 68.3653 - accuracy: 0.7143 - val_loss: 251.5706 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/200\n",
      "2/2 [==============================] - 0s 47ms/step - loss: 66.3888 - accuracy: 0.7143 - val_loss: 251.1726 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/200\n",
      "2/2 [==============================] - 0s 35ms/step - loss: 64.8933 - accuracy: 0.7857 - val_loss: 251.7586 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 64.2060 - accuracy: 0.7857 - val_loss: 252.1539 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 63.4149 - accuracy: 0.7857 - val_loss: 252.3338 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 62.7216 - accuracy: 0.7857 - val_loss: 252.1765 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 61.5992 - accuracy: 0.7857 - val_loss: 251.8875 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 60.7904 - accuracy: 0.7857 - val_loss: 251.2902 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 59.5458 - accuracy: 0.7857 - val_loss: 250.7412 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 58.2930 - accuracy: 0.7857 - val_loss: 250.2563 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 57.4319 - accuracy: 0.7857 - val_loss: 249.5441 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 56.1588 - accuracy: 0.7857 - val_loss: 248.8689 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 54.7582 - accuracy: 0.7857 - val_loss: 248.2685 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 53.7438 - accuracy: 0.7857 - val_loss: 247.8531 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 52.5055 - accuracy: 0.7857 - val_loss: 247.4185 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 51.4919 - accuracy: 0.7143 - val_loss: 244.7703 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 50.1417 - accuracy: 0.7857 - val_loss: 242.7136 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 49.1868 - accuracy: 0.8571 - val_loss: 241.1828 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 48.4617 - accuracy: 0.8571 - val_loss: 240.1064 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 48.1096 - accuracy: 0.8571 - val_loss: 239.4866 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 47.4941 - accuracy: 0.8571 - val_loss: 239.1238 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 46.8384 - accuracy: 0.8571 - val_loss: 238.9866 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 47.2978 - accuracy: 0.7857 - val_loss: 236.0054 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 46.1617 - accuracy: 0.8571 - val_loss: 231.0074 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 47.7574 - accuracy: 0.7857 - val_loss: 228.1373 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 47.9819 - accuracy: 0.7857 - val_loss: 226.8289 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 46.8255 - accuracy: 0.7857 - val_loss: 226.8237 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 44.7235 - accuracy: 0.8571 - val_loss: 227.0796 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 44.0393 - accuracy: 0.8571 - val_loss: 227.4364 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 43.1618 - accuracy: 0.8571 - val_loss: 227.8948 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 42.7420 - accuracy: 0.8571 - val_loss: 228.5162 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 41.9737 - accuracy: 0.8571 - val_loss: 229.1744 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 41.3684 - accuracy: 0.8571 - val_loss: 229.8905 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 40.7488 - accuracy: 0.7143 - val_loss: 228.7686 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 40.1757 - accuracy: 0.7857 - val_loss: 228.0809 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 39.5767 - accuracy: 0.8571 - val_loss: 227.9790 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 39.3601 - accuracy: 0.7857 - val_loss: 224.7355 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 38.8515 - accuracy: 0.8571 - val_loss: 219.1952 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 38.8243 - accuracy: 0.8571 - val_loss: 215.1674 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 38.7531 - accuracy: 0.8571 - val_loss: 212.0800 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 38.5193 - accuracy: 0.8571 - val_loss: 209.7389 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 38.9486 - accuracy: 0.7857 - val_loss: 208.9365 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 38.1325 - accuracy: 0.7857 - val_loss: 209.7063 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 36.9956 - accuracy: 0.8571 - val_loss: 212.0772 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 36.5217 - accuracy: 0.8571 - val_loss: 214.3774 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 35.8366 - accuracy: 0.7857 - val_loss: 216.2520 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 35.4038 - accuracy: 0.7857 - val_loss: 217.6517 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 34.9029 - accuracy: 0.7857 - val_loss: 218.5632 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 34.0842 - accuracy: 0.7857 - val_loss: 219.2784 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 33.2061 - accuracy: 0.7857 - val_loss: 219.9073 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 32.5337 - accuracy: 0.8571 - val_loss: 220.6237 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.0228 - accuracy: 0.8571 - val_loss: 221.4558 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 31.4601 - accuracy: 0.8571 - val_loss: 222.3233 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 32.7898 - accuracy: 0.7857 - val_loss: 219.6252 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 30.8127 - accuracy: 0.8571 - val_loss: 214.4208 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.7996 - accuracy: 0.8571 - val_loss: 210.5451 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 30.6815 - accuracy: 0.8571 - val_loss: 207.5818 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.4318 - accuracy: 0.8571 - val_loss: 205.4058 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 30.1067 - accuracy: 0.8571 - val_loss: 204.0383 - val_accuracy: 0.0000e+00\n",
      "Epoch 100/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 29.6501 - accuracy: 0.8571 - val_loss: 203.3277 - val_accuracy: 0.0000e+00\n",
      "Epoch 101/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 29.3167 - accuracy: 0.8571 - val_loss: 203.1057 - val_accuracy: 0.0000e+00\n",
      "Epoch 102/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 28.7250 - accuracy: 0.8571 - val_loss: 203.1123 - val_accuracy: 0.0000e+00\n",
      "Epoch 103/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 28.1149 - accuracy: 0.8571 - val_loss: 203.3194 - val_accuracy: 0.0000e+00\n",
      "Epoch 104/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 27.5550 - accuracy: 0.8571 - val_loss: 203.7444 - val_accuracy: 0.0000e+00\n",
      "Epoch 105/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 26.8432 - accuracy: 0.8571 - val_loss: 204.3753 - val_accuracy: 0.0000e+00\n",
      "Epoch 106/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 26.4622 - accuracy: 0.8571 - val_loss: 205.2397 - val_accuracy: 0.0000e+00\n",
      "Epoch 107/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 25.7675 - accuracy: 0.8571 - val_loss: 206.1421 - val_accuracy: 0.0000e+00\n",
      "Epoch 108/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 25.2103 - accuracy: 0.8571 - val_loss: 207.1151 - val_accuracy: 0.0000e+00\n",
      "Epoch 109/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 24.6104 - accuracy: 0.8571 - val_loss: 208.0905 - val_accuracy: 0.0000e+00\n",
      "Epoch 110/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 23.7942 - accuracy: 0.8571 - val_loss: 209.1245 - val_accuracy: 0.0000e+00\n",
      "Epoch 111/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 25ms/step - loss: 23.4033 - accuracy: 0.8571 - val_loss: 210.3795 - val_accuracy: 0.0000e+00\n",
      "Epoch 112/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 22.7447 - accuracy: 0.8571 - val_loss: 211.6492 - val_accuracy: 0.0000e+00\n",
      "Epoch 113/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 22.5928 - accuracy: 0.7857 - val_loss: 210.0734 - val_accuracy: 0.0000e+00\n",
      "Epoch 114/200\n",
      "2/2 [==============================] - 0s 29ms/step - loss: 21.7527 - accuracy: 0.8571 - val_loss: 209.0170 - val_accuracy: 0.0000e+00\n",
      "Epoch 115/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 21.3297 - accuracy: 0.8571 - val_loss: 208.5430 - val_accuracy: 0.0000e+00\n",
      "Epoch 116/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 21.0049 - accuracy: 0.8571 - val_loss: 208.4986 - val_accuracy: 0.0000e+00\n",
      "Epoch 117/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 20.4487 - accuracy: 0.8571 - val_loss: 208.6419 - val_accuracy: 0.0000e+00\n",
      "Epoch 118/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 19.9143 - accuracy: 0.8571 - val_loss: 208.9373 - val_accuracy: 0.0000e+00\n",
      "Epoch 119/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 19.2129 - accuracy: 0.8571 - val_loss: 209.3976 - val_accuracy: 0.0000e+00\n",
      "Epoch 120/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 18.8368 - accuracy: 0.8571 - val_loss: 209.8774 - val_accuracy: 0.0000e+00\n",
      "Epoch 121/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 19.2684 - accuracy: 0.7857 - val_loss: 207.6315 - val_accuracy: 0.0000e+00\n",
      "Epoch 122/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 18.0165 - accuracy: 0.8571 - val_loss: 205.9972 - val_accuracy: 0.0000e+00\n",
      "Epoch 123/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 17.6689 - accuracy: 0.8571 - val_loss: 204.9901 - val_accuracy: 0.0000e+00\n",
      "Epoch 124/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 17.4444 - accuracy: 0.8571 - val_loss: 204.6324 - val_accuracy: 0.0000e+00\n",
      "Epoch 125/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 17.1084 - accuracy: 0.8571 - val_loss: 204.5986 - val_accuracy: 0.0000e+00\n",
      "Epoch 126/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 16.5796 - accuracy: 0.8571 - val_loss: 204.8258 - val_accuracy: 0.0000e+00\n",
      "Epoch 127/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 16.2474 - accuracy: 0.8571 - val_loss: 205.4183 - val_accuracy: 0.0000e+00\n",
      "Epoch 128/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 15.8075 - accuracy: 0.8571 - val_loss: 206.1549 - val_accuracy: 0.0000e+00\n",
      "Epoch 129/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 15.3254 - accuracy: 0.8571 - val_loss: 206.9962 - val_accuracy: 0.0000e+00\n",
      "Epoch 130/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 15.4805 - accuracy: 0.7857 - val_loss: 204.1378 - val_accuracy: 0.0000e+00\n",
      "Epoch 131/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 15.0592 - accuracy: 0.8571 - val_loss: 198.7178 - val_accuracy: 0.0000e+00\n",
      "Epoch 132/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 15.1209 - accuracy: 0.8571 - val_loss: 194.7346 - val_accuracy: 0.0000e+00\n",
      "Epoch 133/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 15.2816 - accuracy: 0.8571 - val_loss: 191.8075 - val_accuracy: 0.0000e+00\n",
      "Epoch 134/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 15.1972 - accuracy: 0.8571 - val_loss: 189.8369 - val_accuracy: 0.0000e+00\n",
      "Epoch 135/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 15.0282 - accuracy: 0.8571 - val_loss: 188.4455 - val_accuracy: 0.0000e+00\n",
      "Epoch 136/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.8194 - accuracy: 0.8571 - val_loss: 187.5247 - val_accuracy: 0.0000e+00\n",
      "Epoch 137/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 14.5780 - accuracy: 0.8571 - val_loss: 186.9859 - val_accuracy: 0.0000e+00\n",
      "Epoch 138/200\n",
      "2/2 [==============================] - 0s 37ms/step - loss: 14.3101 - accuracy: 0.8571 - val_loss: 186.7575 - val_accuracy: 0.0000e+00\n",
      "Epoch 139/200\n",
      "2/2 [==============================] - 0s 28ms/step - loss: 13.8970 - accuracy: 0.8571 - val_loss: 186.8468 - val_accuracy: 0.0000e+00\n",
      "Epoch 140/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 13.6911 - accuracy: 0.8571 - val_loss: 187.3757 - val_accuracy: 0.0000e+00\n",
      "Epoch 141/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 13.2668 - accuracy: 0.8571 - val_loss: 188.0137 - val_accuracy: 0.0000e+00\n",
      "Epoch 142/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 12.7008 - accuracy: 0.8571 - val_loss: 188.7918 - val_accuracy: 0.0000e+00\n",
      "Epoch 143/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 12.4217 - accuracy: 0.8571 - val_loss: 189.8267 - val_accuracy: 0.0000e+00\n",
      "Epoch 144/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 11.9296 - accuracy: 0.8571 - val_loss: 190.8744 - val_accuracy: 0.0000e+00\n",
      "Epoch 145/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 11.4584 - accuracy: 0.8571 - val_loss: 191.9319 - val_accuracy: 0.0000e+00\n",
      "Epoch 146/200\n",
      "2/2 [==============================] - 0s 27ms/step - loss: 10.8101 - accuracy: 0.8571 - val_loss: 193.0613 - val_accuracy: 0.0000e+00\n",
      "Epoch 147/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 10.5414 - accuracy: 0.8571 - val_loss: 194.4277 - val_accuracy: 0.0000e+00\n",
      "Epoch 148/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 10.0096 - accuracy: 0.8571 - val_loss: 195.7422 - val_accuracy: 0.0000e+00\n",
      "Epoch 149/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 9.4743 - accuracy: 0.8571 - val_loss: 197.0271 - val_accuracy: 0.0000e+00\n",
      "Epoch 150/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 8.9888 - accuracy: 0.8571 - val_loss: 198.3357 - val_accuracy: 0.0000e+00\n",
      "Epoch 151/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 8.3678 - accuracy: 0.8571 - val_loss: 199.7007 - val_accuracy: 0.0000e+00\n",
      "Epoch 152/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 8.3902 - accuracy: 0.7857 - val_loss: 198.3021 - val_accuracy: 0.0000e+00\n",
      "Epoch 153/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 7.7984 - accuracy: 0.8571 - val_loss: 197.3676 - val_accuracy: 0.0000e+00\n",
      "Epoch 154/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 7.5175 - accuracy: 0.8571 - val_loss: 196.8097 - val_accuracy: 0.0000e+00\n",
      "Epoch 155/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 7.1064 - accuracy: 0.8571 - val_loss: 196.6089 - val_accuracy: 0.0000e+00\n",
      "Epoch 156/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 6.7198 - accuracy: 0.8571 - val_loss: 196.8891 - val_accuracy: 0.0000e+00\n",
      "Epoch 157/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 6.4444 - accuracy: 0.8571 - val_loss: 197.4347 - val_accuracy: 0.0000e+00\n",
      "Epoch 158/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 6.1820 - accuracy: 0.7857 - val_loss: 195.0724 - val_accuracy: 0.0000e+00\n",
      "Epoch 159/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.9228 - accuracy: 0.8571 - val_loss: 193.2218 - val_accuracy: 0.0000e+00\n",
      "Epoch 160/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 5.8652 - accuracy: 0.8571 - val_loss: 191.7991 - val_accuracy: 0.0000e+00\n",
      "Epoch 161/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.8153 - accuracy: 0.8571 - val_loss: 190.7672 - val_accuracy: 0.0000e+00\n",
      "Epoch 162/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 5.7474 - accuracy: 0.8571 - val_loss: 190.0419 - val_accuracy: 0.0000e+00\n",
      "Epoch 163/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 5.6523 - accuracy: 0.8571 - val_loss: 189.5777 - val_accuracy: 0.0000e+00\n",
      "Epoch 164/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.5767 - accuracy: 0.8571 - val_loss: 189.3613 - val_accuracy: 0.0000e+00\n",
      "Epoch 165/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.5072 - accuracy: 0.8571 - val_loss: 189.2987 - val_accuracy: 0.0000e+00\n",
      "Epoch 166/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.3894 - accuracy: 0.8571 - val_loss: 189.3247 - val_accuracy: 0.0000e+00\n",
      "Epoch 167/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 5.3134 - accuracy: 0.8571 - val_loss: 189.4481 - val_accuracy: 0.0000e+00\n",
      "Epoch 168/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.2022 - accuracy: 0.8571 - val_loss: 189.6246 - val_accuracy: 0.0000e+00\n",
      "Epoch 169/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.1305 - accuracy: 0.8571 - val_loss: 189.8693 - val_accuracy: 0.0000e+00\n",
      "Epoch 170/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 5.0245 - accuracy: 0.8571 - val_loss: 190.1437 - val_accuracy: 0.0000e+00\n",
      "Epoch 171/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.9317 - accuracy: 0.8571 - val_loss: 190.4789 - val_accuracy: 0.0000e+00\n",
      "Epoch 172/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.8424 - accuracy: 0.8571 - val_loss: 190.8719 - val_accuracy: 0.0000e+00\n",
      "Epoch 173/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.7492 - accuracy: 0.8571 - val_loss: 191.3116 - val_accuracy: 0.0000e+00\n",
      "Epoch 174/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 4.6813 - accuracy: 0.8571 - val_loss: 191.7772 - val_accuracy: 0.0000e+00\n",
      "Epoch 175/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.5861 - accuracy: 0.8571 - val_loss: 192.2203 - val_accuracy: 0.0000e+00\n",
      "Epoch 176/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.4987 - accuracy: 0.8571 - val_loss: 192.6448 - val_accuracy: 0.0000e+00\n",
      "Epoch 177/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.4027 - accuracy: 0.8571 - val_loss: 193.0627 - val_accuracy: 0.0000e+00\n",
      "Epoch 178/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.3408 - accuracy: 0.8571 - val_loss: 193.4999 - val_accuracy: 0.0000e+00\n",
      "Epoch 179/200\n",
      "2/2 [==============================] - 0s 24ms/step - loss: 4.2636 - accuracy: 0.8571 - val_loss: 193.9185 - val_accuracy: 0.0000e+00\n",
      "Epoch 180/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.1606 - accuracy: 0.8571 - val_loss: 194.3416 - val_accuracy: 0.0000e+00\n",
      "Epoch 181/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 4.0972 - accuracy: 0.8571 - val_loss: 194.8337 - val_accuracy: 0.0000e+00\n",
      "Epoch 182/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 4.0227 - accuracy: 0.8571 - val_loss: 195.3459 - val_accuracy: 0.0000e+00\n",
      "Epoch 183/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.9333 - accuracy: 0.8571 - val_loss: 195.8681 - val_accuracy: 0.0000e+00\n",
      "Epoch 184/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.8743 - accuracy: 0.8571 - val_loss: 196.3946 - val_accuracy: 0.0000e+00\n",
      "Epoch 185/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 3.7923 - accuracy: 0.8571 - val_loss: 196.8841 - val_accuracy: 0.0000e+00\n",
      "Epoch 186/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 3.7172 - accuracy: 0.8571 - val_loss: 197.3429 - val_accuracy: 0.0000e+00\n",
      "Epoch 187/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.6292 - accuracy: 0.8571 - val_loss: 197.7868 - val_accuracy: 0.0000e+00\n",
      "Epoch 188/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.5675 - accuracy: 0.8571 - val_loss: 198.2560 - val_accuracy: 0.0000e+00\n",
      "Epoch 189/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.5119 - accuracy: 0.8571 - val_loss: 198.7297 - val_accuracy: 0.0000e+00\n",
      "Epoch 190/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.4252 - accuracy: 0.8571 - val_loss: 199.1847 - val_accuracy: 0.0000e+00\n",
      "Epoch 191/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.3590 - accuracy: 0.8571 - val_loss: 199.6624 - val_accuracy: 0.0000e+00\n",
      "Epoch 192/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.2960 - accuracy: 0.8571 - val_loss: 200.1564 - val_accuracy: 0.0000e+00\n",
      "Epoch 193/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.2209 - accuracy: 0.8571 - val_loss: 200.6584 - val_accuracy: 0.0000e+00\n",
      "Epoch 194/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.1710 - accuracy: 0.8571 - val_loss: 201.1625 - val_accuracy: 0.0000e+00\n",
      "Epoch 195/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 3.0889 - accuracy: 0.8571 - val_loss: 201.6379 - val_accuracy: 0.0000e+00\n",
      "Epoch 196/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.0389 - accuracy: 0.8571 - val_loss: 202.0664 - val_accuracy: 0.0000e+00\n",
      "Epoch 197/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 3.0650 - accuracy: 0.7857 - val_loss: 199.6023 - val_accuracy: 0.0000e+00\n",
      "Epoch 198/200\n",
      "2/2 [==============================] - 0s 25ms/step - loss: 2.9543 - accuracy: 0.8571 - val_loss: 195.1006 - val_accuracy: 0.0000e+00\n",
      "Epoch 199/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 2.9672 - accuracy: 0.8571 - val_loss: 191.5974 - val_accuracy: 0.0000e+00\n",
      "Epoch 200/200\n",
      "2/2 [==============================] - 0s 26ms/step - loss: 3.2038 - accuracy: 0.7857 - val_loss: 189.6319 - val_accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "history = thirdT_model.fit(x_train, y_train, epochs=200,  validation_split=0.2, batch_size=10, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe8a203",
   "metadata": {},
   "source": [
    "### model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fb4e5d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[140.82736206054688, 0.7142857313156128]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = thirdT_model.evaluate(x_test, y_test, verbose=0)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10d93bdc",
   "metadata": {},
   "source": [
    "### plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1d55dcdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"], label=\"train\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"validation\")\n",
    "plt.title(\"model loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend([\"train\"], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "37432c9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 58ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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CgmbOnClJ6tevn0djElgAAICWLFmi6Ohoj46JjIxUVFTUNfd79tlnlZCQoF9++UUVK1bU888/r549e3p0LgILAABW5qUpoZSUFMXHx3t8zLVcuHBBn376qevXDodDhw8f1sWLFxUcHOz2uQgsAABYmMNLU0KhoaEKDw/3+JhrCQgIUFxcnIKCgrRz5069+uqrWrx4sfbu3auPPvrI7TU4DsMwDI+qs7v0NF9XAJhOXvz3vi4BMCW/Dg+W+jnO9/idV8apvPZnr4xzLWfPnlWvXr2UkpKiefPmqVu3bm4dx23NAABYmcPhnVcZCQkJ0Z133ilJ+uWXX9w+jsACAICFOfwcXnl5S3Z2tqZMmaJRo0YpKyuryH3yn3B76dIlt8clsAAAAK8JCAjQv//9b61du1axsbGFtmdnZ2vjxo2SpFatWrk9LoEFAAArM+GU0MCBAyVJU6ZM0aFDh1zvp6en64UXXlBSUpKcTqfb61ck7hICAMDaTPjguCeeeELbtm1TbGysevbsqfbt26tChQrauXOnzpw5o4YNG2r27Nny9/d3e0wCCwAAFmbGR/OXL19es2fPVnR0tJYvX67t27crLy9PN910kwYMGKDHH39clStX9mhMAgsAAPA6Pz8/RUVFufUkXHcQWAAAsDITTgmVBgILAABWZsIpodLAXUIAAMD06LAAAGBhDpu0HggsAABYGVNCAAAA5nDdHZbExERt3rxZZ86cUWZm5lX3HT169PWeDgAAXMGbnwNkZtcVWCZOnKgVK1Zccz/DMORwOAgsAAB4m02mhEocWJYsWaLly5dLkipVqqRGjRopKCjIa4UBAADkK3FgiYmJkcPh0MCBA/XMM8+oQoUK3qwLAAC4gymhqzt48KCqVq2qCRMmePThRQAAwHvM+FlCpaHEgcUwDNWvX5+wAgCAL9mkw1Li25qdTqeOHj2q3Nxcb9YDAABQSIkDy0MPPaS0tDR99NFH3qwHAAB4wuHwzsvk3JoSOnLkSKH32rdvrw4dOui1115TYmKiunfvrtq1a1918W3Dhg1LXikAACjELmtYHIZhGNfa6ZZbbrn+Ezkc2rVr13WP43Ppab6uADCdvPjvfV0CYEp+HR4s9XNkPXKPV8apsHC9V8YpLW51WNzINGUyBgAA+A2bLLp1K7CsX2/u1AUAgF3ZZUrIrcBSv3790q4DAACgWCW+S+iRRx7R5MmT3dp35MiR6t69e0lPBQAAiuPn8M7L5Er84LhNmza5/QyWAwcO6MSJEyU9FQAAKA5TQv+TkJCgefPmFXo/KSlJzzzzTLHHGYah48eP6+DBgwoNDS15lQAAwNbcCizNmjVTUlKSduzY4XrP4XDo9OnTWr16tVsn6tOnT4kKBAAAxXNYYDrHG9yeEnrxxRf18ccfu369cuVK1axZU127di32GIfDoaCgILVs2ZLAAgBAabDJlJBbD44rys0336z27dtr8eLF3q7J3HhwHFAID44DilYWD47Ledo75yg/5wuvjFNaSrzoduHChapcubI3awEAAChSiQNLx44dvVkHAAAoAR4cdw2zZs3yaH+Hw6Hhw4eX9HSmQesbKGxYxyhflwCY0lzjXOmfhEW3Vzdr1iy3U51hGDdMYAEAAGWvxIGlQ4cOxW7LyMhQcnKykpOT5XA49Oc//1k1a9Ys6akAAEBxmBK6ukWLFl1znx07dmjcuHHatGmTVqxYUdJTAQCA4tgksJT4s4Tc0bp1a7311ls6fPiw5syZU5qnAgAAN7BSDSyS1KpVKzVq1Ejr1q0r7VMBAGA/Dod3XiZX4ikhTwQGBurgwYNlcSoAAOzFr9R7D6ZQ6leZmJioAwcOqGrVqqV9KgAAcIMqcYflhx9+uOr27OxsJSYm6sMPP1ReXp46d+5c0lMBAIDiWGA6xxtKHFgef/xxt57DYhiGgoOD9fTTT5f0VAAAoDgElmu72ucm+vv7KyQkRO3atdOwYcPUpEmT6zkVAAAoCoHl6vbs2ePNOgAAAIpV4kW3CxYsuOY6FgAAUMr8/LzzMrkSd1jef/99nT9/Xhs2bOAOIAAAfMUmU0IljlSpqalq2rQpYQUAAJS6EndYmjZtqqNHj+rixYsKDg72Zk0AAMBddFiubtKkScrJydHgwYO1adMmZWdne7MuAADgDh7Nf3WLFy9Wo0aNtG3bNj366KPy8/NT5cqVFRgYWOT+DodDsbGxJS4UAADYV4kDy+eff17g17m5uUpNTS12f3ceMgcAADxkgTt8vKHEgWXq1KnerAMAAJSETRoCJQ4sx44dU7169dSvX79r7jtnzhwlJiaqb9++JT0dAAAoik0CS4n7SLNmzdKKFSvc2verr77S119/XdJTAQAAm3Orw3Ls2LEin2qbkpKimJiYYo8zDEPHjx/X/v37FRQUVPIqAQBA0WzSYXErsNSoUUMzZ85UcnKy6z2Hw6HDhw/rxRdfvObxhmGoc+fOJa8SAAAUycGi2/8JDAzU+PHjNX36dNd7x48fV0BAgGrWrFnscX5+fgoKClLLli31zDPPXH+1AADAltxedNurVy/16tXL9eubb75ZrVq10uLFi0ulMAAA4AamhK5uxIgRqlu3rjdrAQAAniKwXN2IESO8WQcAAECxShxYAACACdBhAQAApsddQgAAACW3atUqxcTEaM+ePcrIyFCNGjXUuXNnDR06VE2bNvVoLHvEMgAAblQOh3deXmQYhsaNG6dnnnlGW7duVbNmzXTHHXfI399fK1euVL9+/Yp8IO3V0GEBAMDKTLiGZfXq1VqzZo1q1aqlDz74QE6nU5KUm5urGTNmaO7cuRo/frzWrVvn9pPw6bAAAGBlJuyw5H9sz7hx41xhRZL8/f01ZswYtWjRQqdOndLGjRvdHpPAAgAAvKpKlSpq1qyZ2rdvX2ibw+FQkyZNJKnAR/5cC1NCAABYmQnvEnrnnXeK3Zabm6v4+HhJ8ugBtAQWAACszEvTOUuWLFF0dLRHx0RGRioqKsqjYz7++GMdO3ZMISEhuu2229w+jsACAACUkpLi6nx4cownfvjhB7322muSLq9vqVixotvHElgAALAyL3VYQkNDFR4e7vEx7oqNjdWYMWOUnZ2tgQMHqn///h6di8ACAICVeWkNS1RUlMfTO+5atGiRpk6dqtzcXA0aNEgTJ070eAwCCwAAKBWXLl3Syy+/rKVLl8rhcGjcuHEaOnRoicYisAAAYGUmfHCcJGVmZmr48OGKi4tTYGCgpk2bpvvvv7/E4xFYAACwMhMGltzcXFdYqV69uubNm6fWrVtf15gEFgAA4FVz5sxRXFycgoKCtHDhQrVo0eK6xySwAABgZSbrsKSlpemDDz6QJNWqVUvz5s0rdt/evXura9eubo1LYAEAwMpM9qTbTZs2KT09XZKUlJSkpKSkYveNiIggsAAAYAsm67Dcd9992rt3r9fHNVcsAwAAKAIdFgAArMxkHZbSQmABAMDKHPaYLLHHVQIAAEujwwIAgJX5MSUEAADMjikhAAAAc6DDAgCAlXGXEAAAMD2TPem2tNjjKgEAgKXRYQEAwMqYEgIAAKZnk7uECCwAAFiZTTos9ohlAADA0uiwAABgZTa5S4jAAgCAlTElBAAAYA50WAAAsDLuEgIAAKZnk09rtkcsAwAAlkaHBQAAK2NKCAAAmB53CQEAAJgDHRYAAKyMKSEAAGB6NrlLiMACAICVsYYFAADAHOiwAABgZaxhAQAApmeTNSz2iGUAAMDS6LAAAGBlTAkBAADT4y4hAAAAc6DDAgCAlTElBAAATM8mdwkRWGBpS9dv1N8/XKaXn3hI/e+6zdflAD4x+eBO1Wjc6Kr7jK3WUBlpaWVUEeB9BBZY1s6Ew3r9k9W+LgPwqYpVq6pG40bKvXRJB3/8b7H75V26VIZVoUwxJQSY16ZdBzTqnx/qYmaWr0sBfKp+63BJUvL+BL3RtYePq4FP2OQuIQILLCUrO0fvfbZec1etU25enq/LAXyuwa2tJEnHd8b7uBL4jJ89Oiz2uErcEA6dSNED/2+K3lm5VpI0uv+DqlczxMdVAb7V4NYISdKxX3b7uBKgdBFYYBknz6Tp19OpurV5Iy39xxg91fs+X5cE+BwdFsjh8M7L5JgSgmXUrl5Vc8cP0Z1tWvq6FMAUHH5+qhdxiyQp9fgJ3TNmuJp37ayKVaso9div2vn5Wm1ZtlKGYfi4UpQqFt0C5tKoTqga1Qn1dRmAadR2NldAxYqSpNHrPlXFKlUKbL/tkQE6+LcRmtNngM6dOOmLEgGvsUcsA4AbUP50kCQd+u8WvdG1h0YG1dbfqjfSvx4erLRfT6hJp99pxBcx8i9f3oeVolQxJQQAMLPTh47oP/+cIyMvTzHjJrimfnIyMrTp42gl/XeLJm79Tje1vVVdnnhE3879wMcVo1TY5C4h2weWJUuWKDo62u39Y54fXIrVAID7Dv64SQd/3FTs9uT9B7RpcbS6Dn1cbfr2IrDA0mwfWFJSUhQfz+p6ADemI1t3SJJqNrn6o/thYRaYzvEG0weWqVOnlvhYh8Oh55577qr7hIaGKjw8vMTnAABfcjgc8itXTrk5OUVv/7/pguK24wbAXULmEB0drczMTNevPbk9z53AEhUVpaioKLfHzPvvF27vCwCladyGL9X09520Yfb7ih79TJH73NTuVknSr7v2lGVpgNeZPrCsXLlSw4YNU2Jioho2bKjevXv7uiQAMIXjv+xWizu6qG2/Xlo18WVlXbhQYHv1mxqq/UP9JEk/L1nuixJRFpgSMofGjRtr0aJFGjBggI4cOaKIiAh169bN12UBgM+tn/6Ofv/XvyikQX0NWTpfC/86TOdOJkuS6reO0JCl8xVYqZL2bYjT1hV8svkNyyZTQpa4yho1auif//ynypcvr5dfflnZ2dm+LgkAfC75QIL+9fBgZWdkKOLB7pp8KF4v7vhBk3b9Vy9u36g6NzuV9N8tmtv3YZ52eyPzc3jnZXKWCCySdMstt2jQoEE6fvy4Pv74Y1+XAwCmsHXFak1u00Xfvfuh0o7/qlrO5qpat7YSNv6kJSPG67XO9yj97FlflwlcN9NPCV3pySefVMWKFVWpUiVflwKTWP/2S74uAfC5k/sOaPGTo31dBnzFJlNClgosVapU0YgRI3xdBgAA5mGTRbf2iGUAAMDSLNVhAQAAv2GTKSF7XCUAADcoh8PhlVdpS0pKUps2bTR58uQSHU9gAQAAperUqVMaNmyYMjIySjwGgQUAACtz+HnnVUp2796tgQMHKiEh4brGYQ0LAABWZtI1LGlpaXr33Xe1cOFCZWdnq0GDBjp69GiJxzPnVQIAAEtbuHCh3n//fVWvXl1z5sxRnz59rms8OiwAAFiZSR+rX6dOHT377LMaOHCgAgMDFR8ff13jEVgAALAyL00JLVmyRNHR0R4dExkZqaioqCK39e/f3xtluRBYAACwMi/dkpySkuJxFyQlJcUr53YHgQUAACg0NFTh4eEeH1NWCCwAAFiZl6aEoqKiip3eMQMCCwAAVsaHHwIAAJgDHRYAAKzMpA+O8zYCCwAAVmbS57B4mz1iGQAAsDQ6LAAAWBlTQgAAwPQscpfQyJEjNXLkyBIfb49YBgAALI0OCwAAVsaUEAAAMD2LTAldLwILAABWZpMOiz2uEgAAWBodFgAArMzPHr0HAgsAABbmsMkaFnvEMgAAYGl0WAAAsDKbLLolsAAAYGVMCQEAAJgDHRYAAKyMKSEAAGB6TAkBAACYAx0WAACsjAfHAQAA07PJlBCBBQAAK7PJolt7XCUAALA0OiwAAFgZU0IAAMD87BFYmBICAACmR4cFAAArY0oIAACYnk0CC1NCAADA9OiwAABgafbosBBYAACwMqaEAAAAzIEOCwAAVmaPBguBBQAAa7NHYiGwAABgZaxhAQAAMAc6LAAAWJlNOiwEFgAALM0egYUpIQAAYHp0WAAAsDKmhAAAgPnZI7AwJQQAAEyPDgsAAFbGlBAAADA9mwQWpoQAAIDp0WEBAMDS7NFhIbAAAGBhDptMCRFYAACwMpsEFtawAAAA06PDAgCApdmjw0JgAQDAypgSAgAAMAc6LAAAWJlNOiwEFgAALM0egYUpIQAAYHp0WAAAsDKmhAAAgOnZI68wJQQAAMyPDgsAAJZmjxYLgQUAACtjDQsAADA9mwQW1rAAAADTo8MCAIClmbPDcvDgQb3zzjvavHmzTp8+rTp16uiBBx7Q0KFDFRwc7PF4dFgAALAyh8M7Ly/asWOH+vXrp88++0yhoaHq1q2b0tPTNXfuXEVFRen8+fMej0lgAQAAXpOTk6MxY8YoPT1dr776qqKjozVjxgx9/fXXuvvuu7Vv3z69+eabHo9LYAEAwMpM1mH5/PPPdezYMXXp0kV9+/Z1vR8YGKgpU6YoKChIMTExOnfunEfjElgAALA0h5de3hEbGytJ6t69e6FtISEh6tSpk3JychQXF+fRuAQWAADgNfv27ZMkhYWFFbm9RYsWkqS9e/d6NC53CQEAYGVems5ZsmSJoqOjPTomMjJSUVFRBd47efKkJKl27dpFHhMaGipJSk5O9uhcBBYP+XV40NclQJf/YKWkpCg0NLTQHxaUvbmGZ3PRKD382bChoKpeGSYlJUXx8fEeH/NbGRkZki6vWSlK/vvp6ekenYvAAkuKjo5WfHy8wsPD+Z8ycAX+bKCkQkNDFR4e7vExv+Xv76+8vLxrHmsYhkfnIrAAAABFRUV5JeQGBwcrNTVVWVlZRW7PzMyUJAUFBXk0LotuAQCA19SqVUtS0dNF0v/WruTv5y4CCwAA8Jr8u4P2799f5PYDBw4U2M9dBBYAAOA13bp1kyR99dVXhbadPXtWP/30kypUqKDOnTt7NC6BBQAAeM29996r+vXr65tvvtGSJUtc72dmZmrixIlKT09XZGSkqlev7tG4LLoFAABeExgYqGnTpmnw4MGaNGmSoqOj1aBBA23dulXJycmKiIjQ2LFjPR6XDgsAAPCqDh06aNmyZerRo4eOHz+ub775RpUrV9aIESO0YMECBQcHezwmHRYAAOB1TqdTM2bM8Np4dFgAAIDpEVgAAIDpEVgAAIDpsYYFlhQZGen6gDcA/8OfDdyoHIannz4EAABQxpgSAgAApkdgAQAApkdgAQAApkdggaUcPHhQ48eP11133aXWrVure/fumj59ui5evOjr0gDTSEpKUps2bTR58mRflwJ4DYEFlrFjxw7169dPn332mUJDQ9WtWzelp6dr7ty5ioqK0vnz531dIuBzp06d0rBhw5SRkeHrUgCvIrDAEnJycjRmzBilp6fr1VdfVXR0tGbMmKGvv/5ad999t/bt26c333zT12UCPrV7924NHDhQCQkJvi4F8DoCCyzh888/17Fjx9SlSxf17dvX9X5gYKCmTJmioKAgxcTE6Ny5cz6sEvCNtLQ0vf7664qMjNShQ4fUoEEDX5cEeB2BBZYQGxsrSerevXuhbSEhIerUqZNycnIUFxdX1qUBPrdw4UK9//77ql69uubMmaM+ffr4uiTA6wgssIR9+/ZJksLCworc3qJFC0nS3r17y6wmwCzq1KmjZ599VmvXrtXdd9/t63KAUsGj+WEJJ0+elCTVrl27yO35jyFPTk4us5oAs+jfv7+vSwBKHR0WWEL+HQ+BgYFFbs9/Pz09vcxqAgCUHQILLMHf39+t/fhoLAC4MRFYYAnBwcGSpKysrCK3Z2ZmSpKCgoLKrCYAQNkhsMASatWqJUlKSUkpcnv+2pX8/QAANxYCCywh/+6g/fv3F7n9wIEDBfYDANxYCCywhG7dukmSvvrqq0Lbzp49q59++kkVKlRQ586dy7gyAEBZILDAEu69917Vr19f33zzjZYsWeJ6PzMzUxMnTlR6eroiIyNVvXp1H1YJACgtPIcFlhAYGKhp06Zp8ODBmjRpkqKjo9WgQQNt3bpVycnJioiI0NixY31dJgCglNBhgWV06NBBy5YtU48ePXT8+HF98803qly5skaMGKEFCxa47iQCANx4HAYPrgAAACZHhwUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUAAJgegQUwgRUrVigsLEx33HGHV8YbNGiQwsLCNH36dK+MZ0bFXWNYWJjCwsK0ceNGr5xn//79hd67++67FRYWpmXLlnnlHACujcACAEVITk7WuHHjNHjwYF+XAkB8WjOAG8wXX3whSapXr951jRMXF6c1a9aodu3ahbbNnz9fOTk5qlWr1nWdA4D7CCwAbijNmjUr9XPcdNNNpX4OAAUxJQQAAEyPwIIbVv5C1vHjx+vChQt6/fXXdc8996hVq1a688479corr+jChQuSpD179mjUqFG67bbbFBERoQceeEDz58+XYRhFjn3w4EFNmjRJ9913nyIiItS+fXtFRkZq/vz5yszMLLamdevW6dFHH1WnTp3Utm1bDRo0SHFxcde8lj179ujZZ59Vt27dFBERoU6dOumJJ57Q2rVrS/bF+Y2jR48qLCxMv/vd75SXl6f58+frgQceUOvWrXXnnXdq7Nix2rVrV6HjfvrpJ4WFhSkyMlIJCQkaMGCAWrVqpc6dO2vatGkF9v366681dOhQde7cWREREeratavGjRun+Pj4Yus6d+6cZs2apZ49e6pNmza6/fbb9cILLyglJaXYY6626DYlJUX//Oc/1atXL7Vt21Zt2rRR37599f777ys7O7vAGM8//7wk6eTJk64x811t0W1aWppmzZqlPn36qG3btrr11lv1wAMPaNq0aUpOTi60f/736dixY5Wenq63335bPXr0UKtWrdSpUyc99dRT+vnnn4u9XsAumBLCDe/s2bP605/+pKSkJDVt2lT16tVTUlKSPvroI8XHx2vIkCEaPXq0/Pz81KRJE/n5+SkxMVFTp07V6dOnNW7cuALjrV69WhMnTlR2drYCAwPldDp18eJFbd++Xdu3b9fy5cv13nvvqU6dOgWO+8c//qGPP/5YklS3bl01aNBAO3bs0BNPPKGOHTsWW//ixYs1efJk5ebmKigoSC1atFBqaqri4uIUFxenP/zhD3rttdfk7+/vla/X888/r08//VTVqlVTixYtdOjQIX3xxRdat26dpk2bpp49exY65syZM3r00Ud14cIFNW/eXIcOHVLjxo0lSZcuXdJzzz2nzz77TJJUo0YNhYWF6ejRo1qzZo2+/PJLTZgwQX/5y18KjHn8+HE98cQTSkxMlL+/v1q0aKGsrCwtW7ZMGzZsUFBQkEfXtXnzZo0cOVKnT59WuXLl1KxZM2VnZ2v37t3atWuXNmzYoA8++EABAQFq166dzpw5o6SkJJUvX16tWrVy6xx79uzRkCFDlJycLD8/PzVr1kzlypXT/v379a9//UvLly/XzJkz1alTp0LHnjt3Tg899JD27dunWrVqqXnz5jpw4IBiY2P17bffavbs2erWrZtH1wzcUAzgBrV8+XLD6XQaTqfT6NKli7F9+3bXtpiYGNe2m2++2fjb3/5mnD9/3jAMw8jJyTGef/55w+l0Gq1btzYyMjJcx23bts1o2bKl4XQ6jRdeeMF1jGEYxq5du4zu3bsbTqfT6Nu3r5GTk+PatmrVKsPpdBrh4eHG6tWrXe+npaUZo0aNctXStWvXAtfwzTffGGFhYUZ4eLixYMEC49KlS65tGzduNDp37mw4nU5j+vTpBY77y1/+YjidTuOtt95y62t15MgRVw1Op9N49dVXjaysLMMwDCMjI8N48cUXDafTabRq1co4fPiw67gff/zRdcy9995rnDhxwjAMw7hw4YKRmZlpGIZhvPHGG4bT6TTuuOMO49tvv3Ude+nSJWPhwoVGy5YtjbCwMCMuLq5ATY8//rjhdDqNXr16FTjn9u3bja5du7rO+9trzH//+++/d72XmppqdOnSxXA6ncbgwYON5ORk17YdO3YYt912W6Gx8r9/fvt7YhiGcddddxlOp9OIjo52vXf+/Hnj9ttvN5xOp/HQQw8VqDklJcV48sknDafTabRv377Att9+n3733XeubSdPnjR69eplOJ1O449//GOhOgA7YUoItjBhwgS1bt3a9et+/fq57vCoX7++XnvtNVWqVEmSVK5cOQ0fPlySlJmZqcTERNdxM2bM0KVLl3T77bfrlVdecR0jSbfccovef/99BQYGKj4+Xp9//rlr29y5cyVJTz31lHr16uV6v0qVKnr99dfVpEmTIuuePn26DMPQ+PHj9cgjjxToonTu3FlTp06VJH344Yc6e/Zsyb44v9G9e3c9++yzCggIkCQFBgbqH//4h9q2bausrCy99957RR735JNPuu6oCQ4OVoUKFXTq1CnNnz9fkjR79mx17drVtb+/v78GDRqkxx57TIZh6O2333Zt27Fjh77//nv5+/tr1qxZatiwoWtb69at9eabb3p0TUuXLlVKSorq16+vmTNnKjQ01LWtVatWmjBhgiRp5cqVysvL82jsfB9//LGSk5NVs2ZNzZs3r0DNNWvW1IwZM+R0OnX+/HnX98NvvfTSS7r99ttdv65Vq5ZGjBgh6XL35uLFiyWqDbgREFhww3M4HIUeyOZwOFS/fn1JUpcuXQpNp1x5u2r+Opf09HT99NNPkqRHHnmkyHM1bNhQ9957ryRp/fr1kqQjR44oISFBktS3b99CxwQEBOjPf/5zofePHj2q3bt3S5L++Mc/Fnm+O++8UyEhIcrMzNQPP/xQ5D6eeuyxxwq953A4FBUVJUn6z3/+U+Rx7du3L/Tet99+q+zsbDVv3lzh4eFFHte7d29Jl0PK6dOnJUmxsbGuMYu6I6dDhw5q3rz5tS/m/+SP17t3bwUGBhba3qNHD3366adau3at/PxK9r/F/K9Lnz59VLVq1ULbAwICNGjQINe+xm/WR/n7+xf54MAr73rK/14E7Ig1LLjhVa5cuUAnJF/58uUlSdWrVy92myTXD5YjR44oJydHkhQREVHs+SIiIrRmzRodPHhQklwdmuDgYFdI+q1bbrml0HtXPmE1v+NTlKysrALnuR4Oh6PY9Rr5i05TUlJ04cKFQl/TK7sW+fKv4cSJExowYECR4175gzsxMVE1atRwfe2cTmextd588806cODAVa7mfw4fPuw6pigBAQFF/h54Ir/m4oLZldvOnDmj1NRUhYSEuLZVrVq1yDBVoUIF139funTpumoErIzAghtexYoVr7rd3b9RX/m328qVKxe7X/4P8vz2/blz5yTpqotEq1SpUui98+fPu/57y5Yt16zvyv1LqmLFiq6poN+6sv7z588XCixF/bDNr+nChQtuXUP+18qdr1lRXYzipKamXnO865X//eHO94Z0+fvjysByZUguzm+7MoCdEFgANwUHB7v++/z586pRo0aR+6WlpRXYv1q1apJ01fUH+V2SK+X/cK1WrZprKqq0ZWVlyTAMORyOQtuuDERFdaWKkh8We/TooRkzZrhdR/7X7GpTIFe7fbyoOs6fP1+qa0CCg4OVlpZ21eCY/72Rvz8A97GGBXDTTTfdpHLlLmf8X375pdj98rc1atRIklwLatPT013TBr9V1Afs5R+Xmpp61eeO/Pzzz0pISPDoB3hxcnNzXettfit/Pc1NN91UYJriavKvoajry5eRkaFNmzbpyJEjys3NLXBc/jmL4u50kCTXLdbF1ZGTk6MBAwZo1KhROnLkiNvjXqlp06aSdNXnyuR/b1StWrVAdwXAtRFYADcFBQXptttukyQtXLiwyH2OHDniWnyZv4CyQYMGrrULn3zySaFj8vLytHz58kLvN2vWzBV6PvrooyLPt3nzZj388MN68MEHtW3bNs8uqBgxMTFF1rh06VJJ0v333+/2WHfeeaf8/f2VmJio77//vsh95s+fr0GDBql3797KyMiQdPlOJUnatm1bkQ+s27Nnj3bs2OFRHZL02WefFXhAXL5vv/1WW7Zs0XfffefqnOVPFbo7DXPXXXdJkj799NMCnZR82dnZrt//K++WAuAeAgvggREjRqhcuXKKi4vTiy++WGDKIv+hYVlZWbr55pvVp08f17a//e1vkqRFixZp/vz5rltnMzIy9OKLL2rnzp1Fnm/06NGSpHfffVfvvfdegR+2P//8s2t7mzZtXGHqei1cuFCffPKJ6wf1xYsXNXHiRO3cuVMhISF6/PHH3R6rfv366t+/v6TLX4Mr7zDKy8vTsmXLNGvWLEnSww8/7FrjERYWpj/84Q8yDEMjRowo0GnZv3+/Ro0a5dF6joEDB6patWo6dOiQxo8f71rTIl2+O2nSpEmSpAEDBrim4vL/nZaW5tbdOQMGDFDt2rV16tQpPfnkkwU6NadPn9bo0aO1b98+BQcHa+TIkW7XDuAy1rAAHmjbtq0mT56sF154QdHR0Vq9erWaNWtWYLrH6XRq1qxZBRav3n777Ro/frzefPNNTZ06Ve+9957q1q2rxMREXbx4Uffdd5/WrVtX6Hw9e/ZUUlKSZs6cqTfeeEPz5s1T48aNdebMGR07dkzS5emT2bNne+0amzVrpr///e965513VKdOHSUkJCg9PV3VqlXTjBkz3F6/km/ChAk6efKkYmNj9fTTT6tWrVqqXbu2jh07pjNnzki6vMZlzJgxBY6bNGmSjh8/ri1btqhPnz5q0aKFHA6H9u/frypVqqhjx47atGmTWzXUqFFDs2bN0rBhw7R27VrFxsaqRYsWOnfunI4ePSrDMHT77be7AqB0OTT5+fkpKytL999/v2rVqqUPPvig2KmcKlWqaO7cuRo6dKi2bt2q7t27q3nz5q4n3ebk5KhatWp68803XVNUANxHhwXwUJ8+fbRq1SpFRkaqZs2a2r9/v86ePat27drppZdeUkxMTIGHhuUbMmSIFi5c6Jo62L9/v5o0aaK33nqryGef5Bs+fLiWLl2qXr16qVKlStqzZ4/Onj2rli1bavTo0Vq+fHmxC4BLYvr06Ro9erQqVqyovXv3qkaNGho0aJA+/fTTq36EQHEqVKigOXPmaPr06eratatycnK0e/du5ebmqlOnTpo2bZrefvvtQs/CqVKlihYsWKAJEybolltu0bFjx5ScnKwePXpo2bJlHn9icocOHbRmzRo9+uijqlevng4cOKDTp0+rdevWevnll/Xee+8VWJvTqFEjTZ06VY0bN1Zqaqp+/fVXV0gsTsuWLbVmzRoNHz5cLVq00JEjR5SUlKQmTZroqaee0urVqws8GA6A+xwG98kBtnf06FHdc889kqSvvvrKtXYGAMyCDgsAADA9AgsAADA9AgsAADA9AgsAADA9Ft0CAADTo8MCAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABMj8ACAABM7/8DMaaHpwc8DAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = thirdT_model.predict(x_test)\n",
    "predictions.round(1)\n",
    "confusion_matrix = sklearn.metrics.confusion_matrix(y_test, np.rint(predictions))\n",
    "sns.heatmap(confusion_matrix, annot=True, cmap=\"Reds\")\n",
    "plt.xlabel(\"model prediction\")\n",
    "plt.ylabel(\"truth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec28237",
   "metadata": {},
   "source": [
    "## barplot of accuracy scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7898a8a4",
   "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>accuracy score</th>\n",
       "      <th>trimester</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>t2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>t3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   accuracy score trimester\n",
       "0        0.857143        t1\n",
       "1        0.714286        t2\n",
       "2        0.714286        t3"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'accuracy score': [0.8571428656578064, 0.7142857313156128, 0.7142857313156128],\n",
    "           'trimester': ['t1', 't2', 't3']}\n",
    "\n",
    "data = pd.DataFrame(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e1b7a21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trimester', ylabel='accuracy score'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = data, x = 'trimester', y = 'accuracy score', color = 'powderblue', ci = None,)\n",
    "\n",
    "## how do model accuracy scores compare using data from each trimester?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}