Final evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebf39202",
   "metadata": {},
   "source": [
    "# Final evaluation \n",
    "This notebook takes a closer look into the final model as well as an overview of all the models experimented with during this project and outside models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "690474a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# packages for data handling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import svm, ensemble, tree\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import xgboost as xgb\n",
    "import torch\n",
    "\n",
    "# packages for plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# utilities packages\n",
    "from ipynb.fs.full.Functions import load_model, model_eval, load_train_test_data\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b985acc",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black; margin-bottom:5px\"></div>\n",
    "<div style=\"border-bottom: 3px solid black\"></div>\n",
    "\n",
    "## 1) Loading model and Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a1a78a",
   "metadata": {},
   "source": [
    "As shown in the fine_tuning notebook, the best models were the SVM/SVC and the random forest with no oversample, therefore those will be the ones further analysed here.\n",
    "However the best representative of every model will also be loaded for better comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f218f2c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A snippet of our training data:\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PhysicallyActive</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sleep</th>\n",
       "      <th>SoundSleep</th>\n",
       "      <th>JunkFood</th>\n",
       "      <th>Stress</th>\n",
       "      <th>BPLevel</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>UrinationFreq</th>\n",
       "      <th>BMI_high</th>\n",
       "      <th>highBP_yes</th>\n",
       "      <th>RegularMedicine_yes</th>\n",
       "      <th>Pdiabetes_yes</th>\n",
       "      <th>Alcohol_yes</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Smoking_yes</th>\n",
       "      <th>Family_Diabetes_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.514087</td>\n",
       "      <td>-0.775318</td>\n",
       "      <td>-0.293345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.261602</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>-0.293345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.009116</td>\n",
       "      <td>-2.378816</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>-0.775318</td>\n",
       "      <td>0.251438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "188  3.0               0.0 -0.514087 -0.775318   -0.293345       0.0     1.0   \n",
       "145  0.0               2.0 -1.261602  0.026431   -0.293345       0.0     0.0   \n",
       "87   3.0               1.0 -2.009116 -2.378816   -0.838128       0.0     2.0   \n",
       "262  3.0               2.0 -0.887844  0.828180   -0.838128       0.0     1.0   \n",
       "45   1.0               1.0 -0.887844 -0.775318    0.251438       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  BMI_high  highBP_yes  \\\n",
       "188      2.0    -0.416387            0.0         0         1.0   \n",
       "145      1.0    -0.416387            0.0         0         0.0   \n",
       "87       2.0    -0.416387            1.0         0         1.0   \n",
       "262      1.0    -0.416387            1.0         0         1.0   \n",
       "45       0.0    -0.416387            0.0         0         0.0   \n",
       "\n",
       "     RegularMedicine_yes  Pdiabetes_yes  Alcohol_yes  Gender_Male  \\\n",
       "188                  1.0            0.0          0.0          1.0   \n",
       "145                  0.0            0.0          1.0          1.0   \n",
       "87                   1.0            0.0          0.0          1.0   \n",
       "262                  1.0            0.0          1.0          1.0   \n",
       "45                   0.0            0.0          0.0          1.0   \n",
       "\n",
       "     Smoking_yes  Family_Diabetes_yes  \n",
       "188          0.0                  0.0  \n",
       "145          1.0                  1.0  \n",
       "87           0.0                  0.0  \n",
       "262          0.0                  1.0  \n",
       "45           0.0                  1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 232 entries with 18 columns in our training data.\n",
      "There are 41 entries with 18 columns in our testing data.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = load_train_test_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a7a76dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[09:51:52] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-030221e36e1a46bfb-1/xgboost/xgboost-ci-windows/src/learner.cc:553: \n",
      "  If you are loading a serialized model (like pickle in Python, RDS in R) generated by\n",
      "  older XGBoost, please export the model by calling `Booster.save_model` from that version\n",
      "  first, then load it back in current version. See:\n",
      "\n",
      "    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html\n",
      "\n",
      "  for more details about differences between saving model and serializing.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm = load_model(svm.SVC(),scoring='f1_macro')\n",
    "forest = load_model(ensemble.RandomForestClassifier(),scoring='f1_macro')\n",
    "LR = load_model(LogisticRegression(random_state=0),scoring='f1_macro')\n",
    "XGB = load_model(xgb.XGBClassifier(seed=0),scoring='f1_macro')\n",
    "tree = load_model(tree.DecisionTreeClassifier(random_state=0),scoring='f1_macro', ros='ros')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86eac361",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black\"></div>\n",
    "\n",
    "### 1.2) Adding predictions to dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cdbbdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1_predicted_train = svm.predict(X_train)\n",
    "y1_predicted_test = svm.predict(X_test)\n",
    "\n",
    "y2_predicted_train = forest.predict(X_train)\n",
    "y2_predicted_test = forest.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af424b1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train['target'] = y_train\n",
    "X_train['prediction_svm'] = y1_predicted_train\n",
    "X_train['prediction_forest'] = y2_predicted_train\n",
    "X_train['set'] = 'train'\n",
    "\n",
    "\n",
    "X_test['target'] = y_test\n",
    "X_test['prediction_svm'] = y1_predicted_test\n",
    "X_test['prediction_forest'] = y2_predicted_test\n",
    "X_test['set'] = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e167678",
   "metadata": {},
   "outputs": [
    {
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       "      <th>PhysicallyActive</th>\n",
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       "      <th>Gender_Male</th>\n",
       "      <th>Smoking_yes</th>\n",
       "      <th>Family_Diabetes_yes</th>\n",
       "      <th>target</th>\n",
       "      <th>prediction_svm</th>\n",
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       "      <th>set</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
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       "      <td>test</td>\n",
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       "    <tr>\n",
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       "      <td>test</td>\n",
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       "      <td>test</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>train</td>\n",
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       "      <td>-2.472478</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.074723</td>\n",
       "      <td>1.629930</td>\n",
       "      <td>1.885788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.514087</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>0.251438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.327209</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>1.341005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "202  2.0               2.0  0.420306  0.026431   -0.838128       0.0     1.0   \n",
       "29   3.0               0.0 -1.448480 -0.775318    0.251438       0.0     1.0   \n",
       "116  0.0               2.0 -0.700966  0.026431   -0.293345       1.0     2.0   \n",
       "123  0.0               3.0  0.607184 -0.775318    0.251438       1.0     1.0   \n",
       "160  0.0               3.0 -1.635359  0.828180   -3.017261       0.0     0.0   \n",
       "..   ...               ...       ...       ...         ...       ...     ...   \n",
       "49   3.0               0.0 -0.327209 -2.378816   -0.838128       0.0     3.0   \n",
       "154  0.0               2.0 -1.448480 -0.775318   -2.472478       3.0     1.0   \n",
       "137  0.0               0.0 -1.074723  1.629930    1.885788       1.0     1.0   \n",
       "3    2.0               3.0 -0.514087  0.828180    0.251438       0.0     1.0   \n",
       "165  0.0               2.0 -0.327209  0.828180    1.341005       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  ...  RegularMedicine_yes  \\\n",
       "202      2.0    -0.416387            0.0  ...                  0.0   \n",
       "29       1.0     2.894495            1.0  ...                  1.0   \n",
       "116      1.0    -0.416387            0.0  ...                  0.0   \n",
       "123      1.0     1.790868            1.0  ...                  0.0   \n",
       "160      1.0    -0.416387            0.0  ...                  0.0   \n",
       "..       ...          ...            ...  ...                  ...   \n",
       "49       2.0     2.894495            1.0  ...                  1.0   \n",
       "154      1.0    -0.416387            1.0  ...                  0.0   \n",
       "137      1.0    -0.416387            0.0  ...                  0.0   \n",
       "3        1.0    -0.416387            0.0  ...                  0.0   \n",
       "165      2.0    -0.416387            0.0  ...                  0.0   \n",
       "\n",
       "     Pdiabetes_yes  Alcohol_yes  Gender_Male  Smoking_yes  \\\n",
       "202            0.0          1.0          1.0          0.0   \n",
       "29             0.0          0.0          0.0          0.0   \n",
       "116            0.0          0.0          1.0          0.0   \n",
       "123            0.0          0.0          0.0          0.0   \n",
       "160            0.0          0.0          1.0          0.0   \n",
       "..             ...          ...          ...          ...   \n",
       "49             0.0          0.0          0.0          0.0   \n",
       "154            0.0          0.0          1.0          0.0   \n",
       "137            0.0          0.0          0.0          0.0   \n",
       "3              0.0          0.0          1.0          0.0   \n",
       "165            0.0          1.0          1.0          1.0   \n",
       "\n",
       "     Family_Diabetes_yes  target  prediction_svm  prediction_forest    set  \n",
       "202                  0.0       1               0                  0   test  \n",
       "29                   0.0       1               1                  1   test  \n",
       "116                  1.0       0               0                  0   test  \n",
       "123                  1.0       0               0                  0   test  \n",
       "160                  0.0       0               0                  0   test  \n",
       "..                   ...     ...             ...                ...    ...  \n",
       "49                   1.0       1               1                  1  train  \n",
       "154                  0.0       0               0                  0  train  \n",
       "137                  1.0       0               0                  0  train  \n",
       "3                    0.0       0               0                  0  train  \n",
       "165                  1.0       0               0                  0  train  \n",
       "\n",
       "[273 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging all data into one dataset\n",
    "X_full = pd.concat([X_test,X_train])\n",
    "X_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d04b69d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A snippet of our training data:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PhysicallyActive</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sleep</th>\n",
       "      <th>SoundSleep</th>\n",
       "      <th>JunkFood</th>\n",
       "      <th>Stress</th>\n",
       "      <th>BPLevel</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>UrinationFreq</th>\n",
       "      <th>BMI_high</th>\n",
       "      <th>highBP_yes</th>\n",
       "      <th>RegularMedicine_yes</th>\n",
       "      <th>Pdiabetes_yes</th>\n",
       "      <th>Alcohol_yes</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Smoking_yes</th>\n",
       "      <th>Family_Diabetes_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.514087</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.261602</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>-0.293345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.009116</td>\n",
       "      <td>-2.378816</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>-0.775318</td>\n",
       "      <td>0.251438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "188  3.0               0.0 -0.514087 -0.775318   -0.293345       0.0     1.0   \n",
       "145  0.0               2.0 -1.261602  0.026431   -0.293345       0.0     0.0   \n",
       "87   3.0               1.0 -2.009116 -2.378816   -0.838128       0.0     2.0   \n",
       "262  3.0               2.0 -0.887844  0.828180   -0.838128       0.0     1.0   \n",
       "45   1.0               1.0 -0.887844 -0.775318    0.251438       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  BMI_high  highBP_yes  \\\n",
       "188      2.0    -0.416387            0.0         0         1.0   \n",
       "145      1.0    -0.416387            0.0         0         0.0   \n",
       "87       2.0    -0.416387            1.0         0         1.0   \n",
       "262      1.0    -0.416387            1.0         0         1.0   \n",
       "45       0.0    -0.416387            0.0         0         0.0   \n",
       "\n",
       "     RegularMedicine_yes  Pdiabetes_yes  Alcohol_yes  Gender_Male  \\\n",
       "188                  1.0            0.0          0.0          1.0   \n",
       "145                  0.0            0.0          1.0          1.0   \n",
       "87                   1.0            0.0          0.0          1.0   \n",
       "262                  1.0            0.0          1.0          1.0   \n",
       "45                   0.0            0.0          0.0          1.0   \n",
       "\n",
       "     Smoking_yes  Family_Diabetes_yes  \n",
       "188          0.0                  0.0  \n",
       "145          1.0                  1.0  \n",
       "87           0.0                  0.0  \n",
       "262          0.0                  1.0  \n",
       "45           0.0                  1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 232 entries with 18 columns in our training data.\n",
      "There are 41 entries with 18 columns in our testing data.\n"
     ]
    }
   ],
   "source": [
    "# reloading data for further analysis\n",
    "X_train, X_test, y_train, y_test = load_train_test_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec1b8dd",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black; margin-bottom:5px\"></div>\n",
    "<div style=\"border-bottom: 3px solid black\"></div>\n",
    "\n",
    "## 2) Visualizing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b1cc25c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation: f1_score\n",
      "76.07% for the train data\n",
      "81.48% for the test data\n",
      "\n",
      "Evaluation: accuracy_score\n",
      "83.19% for the train data\n",
      "87.80% for the test data\n",
      "\n",
      "Evaluation: recall_score\n",
      "82.67% for the train data\n",
      "84.62% for the test data\n",
      "\n",
      "Evaluation: precision_score\n",
      "70.45% for the train data\n",
      "78.57% for the test data\n",
      "\n",
      "Evaluation: confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM results\n",
    "_= model_eval(svm,X_train, X_test, y_train, y_test,confusion=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed2dcd0",
   "metadata": {},
   "source": [
    "On the SVM's confusion matrix we can see that 5 from the 41 samples in testing was wrongfully predicted, with 3 being false positives and 2 false negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca303c4c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation: f1_score\n",
      "82.19% for the train data\n",
      "75.00% for the test data\n",
      "\n",
      "Evaluation: accuracy_score\n",
      "88.79% for the train data\n",
      "85.37% for the test data\n",
      "\n",
      "Evaluation: recall_score\n",
      "80.00% for the train data\n",
      "69.23% for the test data\n",
      "\n",
      "Evaluation: precision_score\n",
      "84.51% for the train data\n",
      "81.82% for the test data\n",
      "\n",
      "Evaluation: confusion_matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# random forest results\n",
    "_= model_eval(forest,X_train, X_test, y_train, y_test,confusion=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf6c367",
   "metadata": {},
   "source": [
    "On the Random Forest confusion matrix we can see that 6 from the 41 samples in testing was wrongfully predicted, with 2 being false positives and 4 false negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e645b4a3",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8638cf97",
   "metadata": {},
   "source": [
    "Now we can look at how the models compare against each other during the training rounds. The observed models are the best variations obtained during the experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e335335",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_avg(x, n=3) :\n",
    "    arr = np.cumsum(x, dtype=float)\n",
    "    arr[n:] = arr[n:] - arr[:-n]\n",
    "    return arr[n - 1:] / n\n",
    "\n",
    "\n",
    "def plot_learning_curve(models,n=3):\n",
    "    '''\n",
    "    This function will plot the learning curve for the given model \n",
    "    \n",
    "    '''\n",
    "    # import packages needed\n",
    "    import pandas as pd\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import sklearn\n",
    "    from sklearn.metrics import f1_score\n",
    "\n",
    "    \n",
    "    custom_f1 = f1_score\n",
    "    custom_f1.__dict__['average'] = 'macro'\n",
    "    custom_f1.average = 'macro'\n",
    "    \n",
    "    # get data\n",
    "    X_train, X_test, y_train, y_test = load_train_test_data(verbose=False)\n",
    "    \n",
    "    \n",
    "    \n",
    "    colors = ['green','blue','red','purple','black','yellow']\n",
    "    steps = 40\n",
    "    plt.figure(figsize=(10,5))\n",
    "    scores = []\n",
    "    for j,model in enumerate(models):\n",
    "        model_score = []\n",
    "        for i in np.linspace(.1,1,steps):\n",
    "            new_model = sklearn.clone(model)\n",
    "            sample_size = int(len(X_train)*i)\n",
    "            new_model.fit(X_train.head(sample_size),y_train.head(sample_size))\n",
    "            pred_test = new_model.predict(X_test)\n",
    "            model_score.append(custom_f1(y_test,pred_test))\n",
    "        scores.append((model,model_score))\n",
    "        \n",
    "    # plotting\n",
    "        \n",
    "    for j,model_score in enumerate(scores):\n",
    "        plt.plot(np.linspace(.1,1,steps-n+1),rolling_avg(model_score[1],n=n), color=colors[j], \n",
    "                 linestyle='--',linewidth=1, markersize=1,label=type(model_score[0]).__name__)\n",
    "    \n",
    "    plt.xlabel('Training size')\n",
    "    plt.ylabel('F1 score')\n",
    "    plt.legend()\n",
    "    plt.title('F1 x Training Size')\n",
    "    plt.savefig('F1 x Training Size.png')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f82412f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve([forest,svm,LR,XGB,tree],n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285f7924",
   "metadata": {},
   "source": [
    "From the training process we can see an interesting information: both the SVM and the random forest managed to learn a good amount with very little data, however we also need to keep in mind that our test set is somewhat small, and flutuations in this graph could be an result of that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239604b6",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58de5084",
   "metadata": {},
   "source": [
    "One other thing we could do is look at the features importance, but since while using an non-linear svm model the feature lose their meaning we will need to consider only the values for the random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "775a979e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = sorted(list(zip(X_train.columns,forest.feature_importances_)), key = lambda x: x[1],reverse=True)\n",
    "feature,value = [a[0] for a in importance],[a[1] for a in importance]\n",
    "\n",
    "plt.bar(feature,value, zorder=3)\n",
    "plt.xticks(rotation=65, ha='right')\n",
    "plt.xlabel('Feature')\n",
    "plt.grid()\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature importance')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Feature importance.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c440608",
   "metadata": {},
   "source": [
    "As we can see from the values, the age and the usage of medicine has a deep impact on the model decision, however, the BPLevel is not as high as expected since during the data analysis there was an correlation of 0.4 , almost as high as the RegularMedicine."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bb5ecc",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952aeaee",
   "metadata": {},
   "source": [
    "Knowing the feature importance, we can also take a closer look at the wrong predictions and see if those were the cases where our models got the prediction wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e5f0df6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# wrong predictions\n",
    "wrongs = X_full[(X_full['prediction_svm'] != X_full['target']) | (X_full['prediction_forest'] != X_full['target'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e36023d",
   "metadata": {
    "scrolled": false
   },
   "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>Age</th>\n",
       "      <th>PhysicallyActive</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sleep</th>\n",
       "      <th>SoundSleep</th>\n",
       "      <th>JunkFood</th>\n",
       "      <th>Stress</th>\n",
       "      <th>BPLevel</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>UrinationFreq</th>\n",
       "      <th>...</th>\n",
       "      <th>RegularMedicine_yes</th>\n",
       "      <th>Pdiabetes_yes</th>\n",
       "      <th>Alcohol_yes</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Smoking_yes</th>\n",
       "      <th>Family_Diabetes_yes</th>\n",
       "      <th>target</th>\n",
       "      <th>prediction_svm</th>\n",
       "      <th>prediction_forest</th>\n",
       "      <th>set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.420306</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046549</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>0.796222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.894495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.794063</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>-1.927694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.233427</td>\n",
       "      <td>0.026431</td>\n",
       "      <td>0.796222</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.980941</td>\n",
       "      <td>-0.775318</td>\n",
       "      <td>0.251438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.327209</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>1.341005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>0.828180</td>\n",
       "      <td>-0.838128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "202  2.0               2.0  0.420306  0.026431   -0.838128       0.0     1.0   \n",
       "34   3.0               0.0  0.046549  0.026431    0.796222       0.0     1.0   \n",
       "111  2.0               1.0  0.794063  0.026431   -1.927694       1.0     3.0   \n",
       "196  2.0               3.0  0.233427  0.026431    0.796222       2.0     0.0   \n",
       "260  3.0               0.0  0.980941 -0.775318    0.251438       0.0     2.0   \n",
       "132  1.0               0.0 -0.327209  0.828180    1.341005       0.0     1.0   \n",
       "117  3.0               2.0 -0.887844  0.828180   -0.838128       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  ...  RegularMedicine_yes  \\\n",
       "202      2.0    -0.416387            0.0  ...                  0.0   \n",
       "34       1.0     2.894495            0.0  ...                  0.0   \n",
       "111      1.0    -0.416387            0.0  ...                  0.0   \n",
       "196      1.0    -0.416387            0.0  ...                  0.0   \n",
       "260      1.0    -0.416387            0.0  ...                  0.0   \n",
       "132      1.0    -0.416387            0.0  ...                  0.0   \n",
       "117      1.0    -0.416387            1.0  ...                  1.0   \n",
       "\n",
       "     Pdiabetes_yes  Alcohol_yes  Gender_Male  Smoking_yes  \\\n",
       "202            0.0          1.0          1.0          0.0   \n",
       "34             0.0          0.0          0.0          0.0   \n",
       "111            0.0          0.0          1.0          0.0   \n",
       "196            0.0          0.0          1.0          0.0   \n",
       "260            0.0          0.0          1.0          0.0   \n",
       "132            0.0          0.0          1.0          0.0   \n",
       "117            0.0          1.0          1.0          0.0   \n",
       "\n",
       "     Family_Diabetes_yes  target  prediction_svm  prediction_forest   set  \n",
       "202                  0.0       1               0                  0  test  \n",
       "34                   1.0       0               1                  1  test  \n",
       "111                  0.0       0               1                  0  test  \n",
       "196                  0.0       1               1                  0  test  \n",
       "260                  0.0       1               1                  0  test  \n",
       "132                  1.0       1               0                  0  test  \n",
       "117                  1.0       0               1                  1  test  \n",
       "\n",
       "[7 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a closer look at the missclassifications for the test set\n",
    "wrongs[wrongs['set']=='test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "204eed99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PhysicallyActive</th>\n",
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       "      <th>UrinationFreq</th>\n",
       "      <th>...</th>\n",
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       "      <th>Family_Diabetes_yes</th>\n",
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       "      <td>3.0</td>\n",
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       "      <td>test</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.887844</td>\n",
       "      <td>0.828180</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "34   3.0               0.0  0.046549  0.026431    0.796222       0.0     1.0   \n",
       "117  3.0               2.0 -0.887844  0.828180   -0.838128       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  ...  RegularMedicine_yes  \\\n",
       "34       1.0     2.894495            0.0  ...                  0.0   \n",
       "117      1.0    -0.416387            1.0  ...                  1.0   \n",
       "\n",
       "     Pdiabetes_yes  Alcohol_yes  Gender_Male  Smoking_yes  \\\n",
       "34             0.0          0.0          0.0          0.0   \n",
       "117            0.0          1.0          1.0          0.0   \n",
       "\n",
       "     Family_Diabetes_yes  target  prediction_svm  prediction_forest   set  \n",
       "34                   1.0       0               1                  1  test  \n",
       "117                  1.0       0               1                  1  test  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a closer look at the missclassifications for the test set where the target was 0 and both anwsered 1\n",
    "wrongs[(wrongs['set']=='test') & (wrongs['prediction_svm']==1)& (wrongs['prediction_forest']==1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a424ef5",
   "metadata": {},
   "source": [
    "Indeed, these 2 samples had an age on the third group probably causing big impact on the decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd695425",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>PhysicallyActive</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sleep</th>\n",
       "      <th>SoundSleep</th>\n",
       "      <th>JunkFood</th>\n",
       "      <th>Stress</th>\n",
       "      <th>BPLevel</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>UrinationFreq</th>\n",
       "      <th>...</th>\n",
       "      <th>RegularMedicine_yes</th>\n",
       "      <th>Pdiabetes_yes</th>\n",
       "      <th>Alcohol_yes</th>\n",
       "      <th>Gender_Male</th>\n",
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       "      <th>202</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.828180</td>\n",
       "      <td>1.341005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.416387</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  PhysicallyActive       BMI     Sleep  SoundSleep  JunkFood  Stress  \\\n",
       "202  2.0               2.0  0.420306  0.026431   -0.838128       0.0     1.0   \n",
       "132  1.0               0.0 -0.327209  0.828180    1.341005       0.0     1.0   \n",
       "\n",
       "     BPLevel  Pregnancies  UrinationFreq  ...  RegularMedicine_yes  \\\n",
       "202      2.0    -0.416387            0.0  ...                  0.0   \n",
       "132      1.0    -0.416387            0.0  ...                  0.0   \n",
       "\n",
       "     Pdiabetes_yes  Alcohol_yes  Gender_Male  Smoking_yes  \\\n",
       "202            0.0          1.0          1.0          0.0   \n",
       "132            0.0          0.0          1.0          0.0   \n",
       "\n",
       "     Family_Diabetes_yes  target  prediction_svm  prediction_forest   set  \n",
       "202                  0.0       1               0                  0  test  \n",
       "132                  1.0       1               0                  0  test  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a closer look at the missclassifications for the test set where the target was 1 and both anwsered 0\n",
    "wrongs[(wrongs['set']=='test') & (wrongs['prediction_svm']==0)& (wrongs['prediction_forest']==0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47434d45",
   "metadata": {},
   "source": [
    "On the other side of the coin, these 2 other samples had no regular medicine usage, and the age was not on the 3 group, therefore it was very unlikely for our models to predict 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48dc9ab",
   "metadata": {},
   "source": [
    "These points could be considered outliers, however if not, it is known why the models made those decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c157fa",
   "metadata": {},
   "source": [
    "<div style=\"border-bottom: 3px solid black; margin-bottom:5px\"></div>\n",
    "<div style=\"border-bottom: 3px solid black\"></div>\n",
    "\n",
    "## 3) Comparing models against external work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e23c3f88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation: f1_score\n",
      "72.34% for the train data\n",
      "60.87% for the test data\n",
      "\n",
      "Evaluation: f1_score\n",
      "76.07% for the train data\n",
      "81.48% for the test data\n",
      "\n",
      "Evaluation: f1_score\n",
      "81.87% for the train data\n",
      "66.67% for the test data\n",
      "\n",
      "Evaluation: f1_score\n",
      "82.19% for the train data\n",
      "75.00% for the test data\n"
     ]
    }
   ],
   "source": [
    "our_models = [LR,svm,tree,forest]\n",
    "model_names = [type(LR).__name__,type(svm).__name__,type(tree).__name__,type(forest).__name__]\n",
    "ext_score = [0.816,0.832,0.794,0.925]\n",
    "our_score = []\n",
    "\n",
    "for model in our_models:\n",
    "    v_test = model_eval(model, X_train, X_test, y_train, y_test,\n",
    "                           acc=False, f1=True, recall=False, precision=False)[0][1]\n",
    "    our_score.append(v_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b55b74c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "first_model = model_names[0]\n",
    "\n",
    "\n",
    "for model,score in zip(model_names,our_score):\n",
    "    if model == first_model:\n",
    "        ax1.scatter(model,score,c='r',marker=\"o\",label='Our model', zorder=3,s=100)\n",
    "    else:\n",
    "        ax1.scatter(model,score,c='r',marker=\"o\", zorder=3,s=100)\n",
    "        \n",
    "for model,score in zip(model_names,ext_score):\n",
    "    if model == first_model:\n",
    "        ax1.scatter(model,score,c='b',marker=\"x\",label='External model', zorder=3,s=100)\n",
    "    else:\n",
    "        ax1.scatter(model,score,c='b',marker=\"x\", zorder=3,s=100)\n",
    "    \n",
    "plt.grid()\n",
    "plt.ylim(.35,1)\n",
    "plt.xticks()\n",
    "plt.title('Model comparison')\n",
    "plt.ylabel(\"F1 score\")\n",
    "plt.legend()\n",
    "plt.savefig('comparison.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a273a86f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}