Model Training Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training and Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will develop our models and perform model selection. The models we chose for this project are:\n",
    "\n",
    "- Naive base (base model)\n",
    "- Logistic Regression (gave best resutls)\n",
    "- Knn\n",
    "- SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "import pandas as pd\n",
    "from prettytable import PrettyTable\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from tabulate import tabulate\n",
    "import seaborn as sns\n",
    "# ROC Curves\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_precision_recall_curve, precision_recall_curve, average_precision_score\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score, precision_recall_fscore_support, roc_curve, f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors, datasets\n",
    "import pandas\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix \n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets first split the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into 70% train and 30% t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    49605\n",
      "1     4927\n",
      "Name: readmitted, dtype: int64\n",
      "0    12402\n",
      "1     1232\n",
      "Name: readmitted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Create a function to split the data\n",
    "DiabeticDataset = pd.read_csv('DiabeticDataset.csv')\n",
    "DiabeticDataset = DiabeticDataset.drop(columns=['Unnamed: 0'])\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.20, random_state=0)\n",
    "\n",
    "# For each pair of train and test indices,\n",
    "X = DiabeticDataset.drop('readmitted', axis=1)\n",
    "y = DiabeticDataset.readmitted \n",
    "for trainIndexes, testIndexes in split.split(X, y):\n",
    "    X_train, y_train = X.iloc[trainIndexes], y.iloc[trainIndexes]\n",
    "    X_test, y_test = X.iloc[testIndexes], y.iloc[testIndexes]\n",
    "\n",
    "#Print X and Y\n",
    "print(y_train.value_counts())\n",
    "print(y_test.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>admission_type_id</th>\n",
       "      <th>discharge_disposition_id</th>\n",
       "      <th>admission_source_id</th>\n",
       "      <th>time_in_hospital</th>\n",
       "      <th>num_lab_procedures</th>\n",
       "      <th>num_procedures</th>\n",
       "      <th>num_medications</th>\n",
       "      <th>...</th>\n",
       "      <th>citoglipton</th>\n",
       "      <th>insulin</th>\n",
       "      <th>glyburide-metformin</th>\n",
       "      <th>glipizide-metformin</th>\n",
       "      <th>glimepiride-pioglitazone</th>\n",
       "      <th>metformin-rosiglitazone</th>\n",
       "      <th>metformin-pioglitazone</th>\n",
       "      <th>change</th>\n",
       "      <th>diabetesMed</th>\n",
       "      <th>readmitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>-20</td>\n",
       "      <td>10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>-20</td>\n",
       "      <td>10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   race  gender  age  admission_type_id  discharge_disposition_id  \\\n",
       "0     1       1    5                  5                        25   \n",
       "1     1       1   15                  1                         1   \n",
       "2     2       1   25                  1                         1   \n",
       "3     1       0   35                  1                         1   \n",
       "4     1       0   45                  1                         1   \n",
       "\n",
       "   admission_source_id  time_in_hospital  num_lab_procedures  num_procedures  \\\n",
       "0                    1                 1                  41               0   \n",
       "1                    7                 3                  59               0   \n",
       "2                    7                 2                  11               5   \n",
       "3                    7                 2                  44               1   \n",
       "4                    7                 1                  51               0   \n",
       "\n",
       "   num_medications  ...  citoglipton  insulin  glyburide-metformin  \\\n",
       "0                1  ...          -20      -20                  -20   \n",
       "1               18  ...          -20       10                  -20   \n",
       "2               13  ...          -20      -20                  -20   \n",
       "3               16  ...          -20       10                  -20   \n",
       "4                8  ...          -20        0                  -20   \n",
       "\n",
       "   glipizide-metformin  glimepiride-pioglitazone  metformin-rosiglitazone  \\\n",
       "0                  -20                       -20                      -20   \n",
       "1                  -20                       -20                      -20   \n",
       "2                  -20                       -20                      -20   \n",
       "3                  -20                       -20                      -20   \n",
       "4                  -20                       -20                      -20   \n",
       "\n",
       "   metformin-pioglitazone  change  diabetesMed  readmitted  \n",
       "0                     -20      -1           -1           0  \n",
       "1                     -20       1            1           0  \n",
       "2                     -20      -1            1           0  \n",
       "3                     -20       1            1           0  \n",
       "4                     -20       1            1           0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DiabeticDataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Model Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by building the initial model without balancing or tuning to see the results we get. This will be our baseline model and we will try to improve on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#naive bayes for unbalanced data\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n",
    "from sklearn.naive_bayes import MultinomialNB,GaussianNB,BernoulliNB\n",
    "\n",
    "nb=BernoulliNB()\n",
    "\n",
    "# Perform training and prediction.\n",
    "nb.fit(X_train, y_train)\n",
    "nb1_pred = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.91\n",
      "Precision is 0.00\n",
      "Recall is 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.5951357015625883\n"
     ]
    }
   ],
   "source": [
    "#confusion matrix\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, nbi_pred)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, nbi_pred)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, nbi_pred)))\n",
    "predictions_nbi = nb.predict(X_train)\n",
    "train_score = round(accuracy_score(y_train, predictions_nbi), 3)\n",
    "cm_train = cm(y_train, predictions_nbi)\n",
    "\n",
    "predictions_nb1 = nb.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_nb1), 3)\n",
    "cm_val = cm(y_test, predictions_nb1)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = nb.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see from the results above that the we are getting good accuracy overall, precision is low. However Recall which is the most important metric for our use case is very low. We will try to improve that by balancing the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We balanced the data using undersampling and oversampling. Under sampling gave us better results overall so we decided to go with it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Sampling Counter({0: 15810, 1: 4750})\n",
      "After Sampling Counter({0: 49605, 1: 49605})\n",
      "1    39684\n",
      "0    39684\n",
      "Name: readmitted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#count results before sampling\n",
    "print('Before Sampling {}'.format(Counter(y)))\n",
    "sm = SMOTE(random_state=20)\n",
    "X, y = sm.fit_resample(X_train, y_train)\n",
    "print('After Sampling {}'.format(Counter(y)))\n",
    "\n",
    "#Split the data again\n",
    "for trainIndexes, testIndexes in split.split(X, y):\n",
    "    X_train_sampled, y_train_sampled = X.iloc[trainIndexes], y.iloc[trainIndexes]\n",
    "    \n",
    "print(y_train_sampled.value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive bayes with Balanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we balanced the data, lets try Naive bayes with balanced data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb=BernoulliNB()\n",
    "\n",
    "# Perform training and prediction.\n",
    "nb.fit(X_train_sampled, y_train_sampled)\n",
    "nb_pred = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.55\n",
      "Precision is 0.09\n",
      "Recall is 0.42\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.48481222655750955\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, nb_pred)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, nb_pred)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, nb_pred)))\n",
    "predictions_nb = nb.predict(X_train_sampled)\n",
    "train_score = round(accuracy_score(y_train_sampled, predictions_nb), 3)\n",
    "cm_train = cm(y_train_sampled, predictions_nb)\n",
    "\n",
    "predictions_nbt = nb.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_nbt), 3)\n",
    "cm_val = cm(y_test, predictions_nbt)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = nb.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see from the results abovr that recall went up from 0.00 to 0.41. Since this is a cancer like problem, recall is the most important metric for us because we want to be able to have high accuracy in prediction the TP. (readmitted in <30) so we choose this one as our best NB model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start with a base logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.90919759424967\n",
      "[[12394     8]\n",
      " [ 1230     2]]\n",
      "Accuracy is 0.91\n",
      "Precision is 0.20\n",
      "Recall is 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.6302023448249863\n"
     ]
    }
   ],
   "source": [
    "# Instanciate an object from Logistic Regression class.\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# Perform training and prediction.\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_train)\n",
    "      \n",
    "# Return training accuracy and confusion matrix.\n",
    "TrainAcc = accuracy_score(y_train, y_pred)\n",
    "TrainConf = confusion_matrix(y_train, y_pred)\n",
    "LR = lr\n",
    "\n",
    "y_test_pred = LR.predict(X_test)\n",
    "TestAcc = accuracy_score(y_test, y_test_pred)\n",
    "TestConf = confusion_matrix(y_test, y_test_pred)\n",
    "print(TestAcc)\n",
    "print(TestConf)\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, y_test_pred)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, y_test_pred)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, y_test_pred)))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "\n",
    "predictions_lri = LR.predict(X_train)\n",
    "train_score = round(accuracy_score(y_train, predictions_lri), 3)\n",
    "cm_train = cm(y_train, predictions_lri)\n",
    "\n",
    "predictions_lriT = LR.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_lriT), 3)\n",
    "cm_val = cm(y_test, predictions_lriT)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = LR.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see from above that we have 0 precision and recall and 89% accuracy. What the model is basically doing here is it's trying to get hight accuracy by predicting all patients as not readmitted within 30 days. Thay is why we have to focus on getting recall scores higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning our logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first run it on balanced data.\n",
    "We will use a grid search to find the best parameters for our Logistic Regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Penalty: l2\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "\n",
    "#What to tune?\n",
    "penalty = ['l1', 'l2']\n",
    "C = np.logspace(0, 4, 10, 50)\n",
    "\n",
    "hyperparameters = dict(C=C, penalty=penalty)\n",
    "\n",
    "# Grid Search\n",
    "lr = GridSearchCV(lr, hyperparameters, cv=10)\n",
    "# Best Model\n",
    "best = lr.fit(X_train_sampled, y_train_sampled)\n",
    "print('Best Penalty:', best.best_estimator_.get_params()['penalty']) # l1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we got the best parameter, lets train our new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6534119544400766\n",
      "[[24985 14699]\n",
      " [12809 26875]]\n",
      "Accuracy is 0.65\n",
      "Precision is 0.65\n",
      "Recall is 0.68\n"
     ]
    }
   ],
   "source": [
    "#start new logistic regression with tuned params\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "lr = LogisticRegressionCV(\n",
    "                            cv=10 \n",
    "                           , penalty='l2' #lasso\n",
    "                           , max_iter=500\n",
    "                           , random_state=7)\n",
    "\n",
    "# Perform training and prediction.\n",
    "lr.fit(X_train_sampled, y_train_sampled)\n",
    "y_pred_lr = lr.predict(X_train_sampled)\n",
    "      \n",
    "# Return training accuracy and confusion matrix.\n",
    "TrainAcc = accuracy_score(y_train_sampled, y_pred_lr)\n",
    "TrainConf = confusion_matrix(y_train_sampled, y_pred_lr)\n",
    "LR = lr\n",
    "\n",
    "print(TrainAcc)\n",
    "print(TrainConf)\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_train_sampled, y_pred_lr)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_train_sampled, y_pred_lr)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_train_sampled, y_pred_lr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6020243508874872\n",
      "[[7565 4837]\n",
      " [ 589  643]]\n",
      "Accuracy is 0.60\n",
      "Precision is 0.12\n",
      "Recall is 0.52\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.5888408630153914\n"
     ]
    }
   ],
   "source": [
    "y_test_pred_lr = LR.predict(X_test)\n",
    "TestAcc = accuracy_score(y_test, y_test_pred_lr)\n",
    "TestConf = confusion_matrix(y_test, y_test_pred_lr)\n",
    "print(TestAcc)\n",
    "print(TestConf)\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, y_test_pred_lr)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, y_test_pred_lr)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, y_test_pred_lr)))\n",
    "predictions_lrb = LR.predict(X_train_sampled)\n",
    "train_score = round(accuracy_score(y_train_sampled, predictions_lrb), 3)\n",
    "cm_train = cm(y_train_sampled, predictions_lrb)\n",
    "\n",
    "predictions_lrbT = LR.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_lrbT), 3)\n",
    "cm_val = cm(y_test, predictions_lrbT)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = LR.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could see that our model improved a lot. Specially with Recall. This is our best performing model so far as it highest the highest recall and AUC scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are going to try a baseline mode with KNN with K = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.8899809300278715\n",
      "Precision is 0.10\n",
      "Recall is 0.03\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.5075609990114708\n"
     ]
    }
   ],
   "source": [
    "# instantiate learning model (k = 3)\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# fitting the model\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# predict the response\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "# evaluate accuracy\n",
    "print(\"accuracy: {}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, pred)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, pred)))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "\n",
    "predictions_knni = knn.predict(X_train)\n",
    "train_score = round(accuracy_score(y_train, predictions_knni), 3)\n",
    "cm_train = cm(y_train, predictions_knni)\n",
    "\n",
    "predictions_knnit = knn.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_knnit), 3)\n",
    "cm_val = cm(y_test, predictions_knnit)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = knn.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we are getting very low precision and recall. lets try to fix it.\n",
    "Now lets try to tune our model and find the best k. We will use 10-f0ld cross validation and grid search to find the best k. we will also run it on the balanced dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#performing a 10-fold cross validation on our dataset using a generated list of odd K’s ranging from 1 to 50.\n",
    "# creating odd list of K for KNN\n",
    "neighbors = list(range(1, 50, 2))\n",
    "\n",
    "# empty list that will hold cv scores\n",
    "cv_scores = []\n",
    "\n",
    "# perform 10-fold cross validation\n",
    "for k in neighbors:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X_train_sampled, y_train_sampled, cv=10, scoring='roc_auc')\n",
    "    cv_scores.append(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal number of neighbors is 5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# changing to misclassification error\n",
    "mse = [1 - x for x in cv_scores]\n",
    "\n",
    "# determining best k\n",
    "optimal_k = neighbors[mse.index(min(mse))]\n",
    "print(\"The optimal number of neighbors is {}\".format(optimal_k))\n",
    "\n",
    "# plot misclassification error vs k\n",
    "plt.plot(neighbors, mse)\n",
    "plt.xlabel(\"Number of Neighbors K\")\n",
    "plt.ylabel(\"Misclassification Error\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are giving us the best k as K= . Now lets run our new model with weights = 'uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.58046061317295\n",
      "[[7363 5039]\n",
      " [ 681  551]]\n",
      "Accuracy is 0.58\n",
      "Precision is 0.10\n",
      "Recall is 0.45\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.5300508584706698\n"
     ]
    }
   ],
   "source": [
    "# instantiate learning model (k = 5)\n",
    "knn = KNeighborsClassifier(n_neighbors=5, weights = 'uniform') #weights = 'distance' assigns weights proportional to the inverse of the distance from the query point. \n",
    "\n",
    "# fitting the model\n",
    "knn.fit(X_train_sampled, y_train_sampled)\n",
    "\n",
    "# predict the response\n",
    "pred_knn = knn.predict(X_test)\n",
    "\n",
    "TestAcc = accuracy_score(y_test, pred_knn)\n",
    "TestConf = confusion_matrix(y_test, pred_knn)\n",
    "print(TestAcc)\n",
    "print(TestConf)\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, pred_knn)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, pred_knn)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, pred_knn)))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "\n",
    "predictions_knnb = knn.predict(X_train_sampled)\n",
    "train_score = round(accuracy_score(y_train_sampled, predictions_knnb), 3)\n",
    "cm_train = cm(y_train_sampled, predictions_knnb)\n",
    "\n",
    "predictions_knnbt = knn.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_knnbt), 3)\n",
    "cm_val = cm(y_test, predictions_knnbt)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = knn.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we see recall improved a little bit but not as much as we need it to and it is still not performing better than the Logistic regression model. Now we will try with weights = 'distance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.58390787736541\n",
      "Accuracy is 0.58\n",
      "Precision is 0.10\n",
      "Recall is 0.45\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc_auc_score for  LR Initial:  0.5294716420895667\n"
     ]
    }
   ],
   "source": [
    "# instantiate learning model (k = 5)\n",
    "knn = KNeighborsClassifier(n_neighbors=5, weights = 'distance') #weights = 'distance' assigns weights proportional to the inverse of the distance from the query point. \n",
    "\n",
    "# fitting the model\n",
    "knn.fit(X_train_sampled, y_train_sampled)\n",
    "\n",
    "# predict the response\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "# evaluate accuracy\n",
    "print(\"accuracy: {}\".format(accuracy_score(y_test, pred)))\n",
    "\n",
    "print(\"Accuracy is {0:.2f}\".format(accuracy_score(y_test, pred_knn)))\n",
    "print(\"Precision is {0:.2f}\".format(precision_score(y_test, pred_knn)))\n",
    "print(\"Recall is {0:.2f}\".format(recall_score(y_test, pred_knn)))\n",
    "\n",
    "from sklearn.metrics import confusion_matrix as cm\n",
    "\n",
    "predictions_knnb1 = knn.predict(X_train_sampled)\n",
    "train_score = round(accuracy_score(y_train_sampled, predictions_knnb1), 3)\n",
    "cm_train = cm(y_train_sampled, predictions_knnb1)\n",
    "\n",
    "predictions_knnbt1 = knn.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_knnbt1), 3)\n",
    "cm_val = cm(y_test, predictions_knnbt1)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "y_score = knn.predict_proba(X_test)[:,1]\n",
    "print('roc_auc_score for  LR Initial: ', roc_auc_score(y_test, y_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Train\n",
      "[[12313    89]\n",
      " [ 1215    17]]\n",
      "Test\n",
      "Accuracy: 0.9043567551708963\n",
      "Precision: 0.16037735849056603\n",
      "Recall: 0.013798701298701298\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC- 50.33\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "svc = svm.LinearSVC() # use LinearSVC for faster computation\n",
    "svc.fit(X_train, y_train)\n",
    "y_pred_test = svc.predict(X_test)\n",
    "print(\"Confusion Matrix - Train\")\n",
    "print(confusion_matrix( y_test,y_pred_test))\n",
    "\n",
    "print(\"Test\")\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred_test))\n",
    "print(\"Precision:\",metrics.precision_score(y_test,y_pred_test))\n",
    "print(\"Recall:\",metrics.recall_score(y_test,y_pred_test))\n",
    "\n",
    "predictions_svm = svc.predict(X_train)\n",
    "train_score = round(accuracy_score(y_train, predictions_svm), 3)\n",
    "cm_train = cm(y_train, predictions_svm)\n",
    "\n",
    "predictions_svmi = svc.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_svmi), 3)\n",
    "cm_val = cm(y_test, predictions_svmi)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "print(f\"AUC- {metrics.roc_auc_score(y_test, predictions_svmi) *100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.01, 'gamma': 1, 'kernel': 'rbf'}\n",
      "SVC(C=0.01, gamma=1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = {'C': [0.01, 0.1, 1, 10,], \n",
    "              'gamma': [1, 0.1, 0.01, 0.001],\n",
    "              'kernel': ['rbf']}\n",
    "\n",
    "grid = GridSearchCV(SVC(), param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "# print best parameter after tuning\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n",
      "Accuracy: 0.5704122047821623\n",
      "Precision: 0.10449803317940824\n",
      "Recall: 0.49594155844155846\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC- 53.69\n"
     ]
    }
   ],
   "source": [
    "svc = svm.SVC() \n",
    "svc.fit(X_train_sampled, y_train_sampled)\n",
    "y_pred_test = svc.predict(X_test)\n",
    "\n",
    "print(\"Test\")\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred_test))\n",
    "print(\"Precision:\",metrics.precision_score(y_test,y_pred_test))\n",
    "print(\"Recall:\",metrics.recall_score(y_test,y_pred_test))\n",
    "\n",
    "predictions_svm = svc.predict(X_train)\n",
    "train_score = round(accuracy_score(y_train, predictions_svm), 3)\n",
    "cm_train = cm(y_train, predictions_svm)\n",
    "\n",
    "predictions_svmb = svc.predict(X_test)\n",
    "val_score = round(accuracy_score(y_test, predictions_svmb), 3)\n",
    "cm_val = cm(y_test, predictions_svmb)\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(15,5)) \n",
    "sns.heatmap(cm_train, annot=True, fmt=\".0f\",ax=ax1)\n",
    "ax1.set_xlabel('Predicted Values')\n",
    "ax1.set_ylabel('Actual Values')\n",
    "ax1.set_title('Train Accuracy Score: {0}'.format(train_score), size = 15)\n",
    "sns.heatmap(cm_val, annot=True, fmt=\".0f\",ax=ax2)\n",
    "ax2.set_xlabel('Predicted Values')\n",
    "ax2.set_ylabel('Actual Values')\n",
    "ax2.set_title('Validation Accuracy Score: {0}'.format(val_score), size = 15)\n",
    "plt.show()\n",
    "print(f\"AUC- {metrics.roc_auc_score(y_test, predictions_svmb) *100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection and Comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Baseline Models Before Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our baseline models before tuning all have very low AUC at 0.50 and almost 0 precision and 0 recall for all. This is because of the highly inbalanced data. Our model is basically predicting all patient as will not be readmitted within 30 days in order to get high accuracy. This could be misleadin as in a problem like ours, The Nost of FN is much higher than the cost of FP. We tried to improve recall while maintain good accuracuy and precision. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curves Initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot') \n",
    "plt.style.use('seaborn-pastel')\n",
    "hfont = {'fontname':'DejaVu Sans'}\n",
    "plt.rcParams[\"font.family\"] = \"DejaVu Sans\"\n",
    "plt.rcParams['font.size'] = '12'\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "\n",
    "models = {'Naïve Bayes':pred\n",
    ", 'Logistic Regression':predictions_lriT\n",
    ", 'K-Nearest Neighbor':predictions_knnit,\n",
    "'SVM': predictions_svmi\n",
    "}\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m, pred in models.items():\n",
    "\n",
    "    # Compute FPR, TPR & AUC\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, pred)\n",
    "    auc = metrics.roc_auc_score(y_test, pred)\n",
    "\n",
    "    # Plot Metrics\n",
    "    plt.plot(fpr, tpr, label=f'{m} - {auc*100:.2f}%', linewidth=3)\n",
    "\n",
    "# Plot Settings\n",
    "plt.plot([0, 1], [0, 1], color='salmon', linewidth=4, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('Specificity (FPR)')\n",
    "plt.ylabel('Sensitivity (TPR)')\n",
    "plt.title('ROC Curves & AUC')\n",
    "plt.legend(loc=\"lower right\", facecolor='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All initial models performing at 0.50 accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR Curves Initial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 960x640 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot') #looks nice without as well\n",
    "plt.style.use('seaborn-pastel')\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.rcParams[\"font.family\"] = \"Helvetica\"\n",
    "plt.rcParams['font.size'] = '12'\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.figure(figsize=(14, 10), dpi=80)\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m, pred in models.items():\n",
    "\n",
    "    # Calculate PR for Curves\n",
    "    precision, recall, thresholds = precision_recall_curve(y_test, pred)\n",
    "    ap = average_precision_score(y_test, pred)\n",
    "    \n",
    "    # Plot Metrics\n",
    "    plt.plot(recall, precision, label=f'{m} - {ap*100:.2f}%', linewidth=3)\n",
    "\n",
    "# Plot Settings\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0]) \n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('PR Curves & Average Precision')\n",
    "plt.legend(loc=\"best\", facecolor='white')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision and Recall are very low. Which means our model is not performing well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model results after tuning and balancing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tuning our models, we see some improvement in recall and precision scores. Specially after balancing the data. However this takes down the overall accuracy. In our problem setting, recall is most important so we are focusing on recall and AUC metric. we could see improvement from the graphs below, specially for Logisitic regression and Knn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curve Comparison (After Tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot') \n",
    "plt.style.use('seaborn-pastel')\n",
    "hfont = {'fontname':'DejaVu Sans'}\n",
    "plt.rcParams[\"font.family\"] = \"DejaVu Sans\"\n",
    "plt.rcParams['font.size'] = '12'\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "\n",
    "models = {'Naïve Bayes': nb_pred\n",
    ", 'Logistic Regression':y_test_pred_lr\n",
    ", 'K-Nearest Neighbor':pred_knn,\n",
    "          'SVM': predictions_svmb\n",
    "}\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m, pred in models.items():\n",
    "\n",
    "    # Compute FPR, TPR & AUC\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test, pred)\n",
    "    auc = metrics.roc_auc_score(y_test, pred)\n",
    "\n",
    "    # Plot Metrics\n",
    "    plt.plot(fpr, tpr, label=f'{m} - {auc*100:.2f}%', linewidth=3)\n",
    "\n",
    "\n",
    "# Plot Settings\n",
    "plt.plot([0, 1], [0, 1], color='salmon', linewidth=4, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('Specificity (FPR)')\n",
    "plt.ylabel('Sensitivity (TPR)')\n",
    "plt.title('ROC Curves & AUC')\n",
    "plt.legend(loc=\"lower right\", facecolor='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see improvement on AUC for both Logistic regression and Knn. Logistic regression has the best performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR Curves (After Tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 960x640 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1120x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot') #looks nice without as well\n",
    "plt.style.use('seaborn-pastel')\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.rcParams[\"font.family\"] = \"Helvetica\"\n",
    "plt.rcParams['font.size'] = '12'\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.figure(figsize=(14, 10), dpi=80)\n",
    "\n",
    "# Below for loop iterates through your models list\n",
    "for m, pred in models.items():\n",
    "\n",
    "    # Calculate PR for Curves\n",
    "    precision, recall, thresholds = precision_recall_curve(y_test, pred)\n",
    "    ap = average_precision_score(y_test, pred)\n",
    "    \n",
    "    # Plot Metrics\n",
    "    plt.plot(recall, precision, label=f'{m} - {ap*100:.2f}%', linewidth=3)\n",
    "\n",
    "# Plot Settings\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0]) #scaled to zoom in on curve\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('PR Curves & Average Precision')\n",
    "plt.legend(loc=\"best\", facecolor='white')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see improvements on the PR curves for Naive Bayes, Logistic Regression and Knn. SVM did not improve as much. Logistic regression is still performin the best. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yellowbrick\n",
      "  Downloading yellowbrick-1.3.post1-py3-none-any.whl (271 kB)\n",
      "\u001b[K     |████████████████████████████████| 271 kB 3.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scikit-learn>=0.20 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from yellowbrick) (0.23.2)\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from yellowbrick) (3.3.2)\n",
      "Requirement already satisfied: cycler>=0.10.0 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from yellowbrick) (0.10.0)\n",
      "Requirement already satisfied: numpy<1.20,>=1.16.0 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from yellowbrick) (1.19.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from yellowbrick) (1.5.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.20->yellowbrick) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn>=0.20->yellowbrick) (0.17.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.4.7)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2020.6.20)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (8.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.3.0)\n",
      "Requirement already satisfied: six in /Users/mac/opt/anaconda3/lib/python3.8/site-packages (from cycler>=0.10.0->yellowbrick) (1.15.0)\n",
      "Installing collected packages: yellowbrick\n",
      "Successfully installed yellowbrick-1.3.post1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importances of 44 Features using RandomForestClassifier'}, xlabel='relative importance'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from yellowbrick.datasets import load_occupancy\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "\n",
    "# Load the classification data set\n",
    "X, y = load_occupancy()\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=10)\n",
    "viz = FeatureImportances(model)\n",
    "viz.fit(X_train, y_train)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: -0.00001\n",
      "Feature: 1, Score: -0.00600\n",
      "Feature: 2, Score: -0.00739\n",
      "Feature: 3, Score: 0.01988\n",
      "Feature: 4, Score: -0.01066\n",
      "Feature: 5, Score: 0.02217\n",
      "Feature: 6, Score: -0.00766\n",
      "Feature: 7, Score: -0.00190\n",
      "Feature: 8, Score: 0.00171\n",
      "Feature: 9, Score: -0.01167\n",
      "Feature: 10, Score: 0.00524\n",
      "Feature: 11, Score: -0.00236\n",
      "Feature: 12, Score: -0.00086\n",
      "Feature: 13, Score: 0.00122\n",
      "Feature: 14, Score: -0.01058\n",
      "Feature: 15, Score: -0.01058\n",
      "Feature: 16, Score: -0.01192\n",
      "Feature: 17, Score: -0.00028\n",
      "Feature: 18, Score: -0.00334\n",
      "Feature: 19, Score: -0.00796\n",
      "Feature: 20, Score: -0.00615\n",
      "Feature: 21, Score: 0.00575\n",
      "Feature: 22, Score: 0.00281\n",
      "Feature: 23, Score: 0.00240\n",
      "Feature: 24, Score: 0.00148\n",
      "Feature: 25, Score: 0.00280\n",
      "Feature: 26, Score: 0.00374\n",
      "Feature: 27, Score: 0.00067\n",
      "Feature: 28, Score: 0.00270\n",
      "Feature: 29, Score: 0.00078\n",
      "Feature: 30, Score: -0.00059\n",
      "Feature: 31, Score: 0.00217\n",
      "Feature: 32, Score: 0.00267\n",
      "Feature: 33, Score: 0.00277\n",
      "Feature: 34, Score: 0.00260\n",
      "Feature: 35, Score: 0.00280\n",
      "Feature: 36, Score: 0.00280\n",
      "Feature: 37, Score: 0.01408\n",
      "Feature: 38, Score: 0.00223\n",
      "Feature: 39, Score: 0.00275\n",
      "Feature: 40, Score: 0.00280\n",
      "Feature: 41, Score: 0.00280\n",
      "Feature: 42, Score: 0.00279\n",
      "Feature: 43, Score: 0.00040\n",
      "Feature: 44, Score: 0.00143\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# logistic regression for feature importance\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from matplotlib import pyplot\n",
    "\n",
    "model = LogisticRegression()\n",
    "# fit the model\n",
    "model.fit(X_train_sampled, y_train_sampled)\n",
    "# get importance\n",
    "importance = model.coef_[0]\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running all our model, balancing the data and tuning them. We conclude that the tuned logistic regression model is giving us the best results based on its AUC and PR curves.\n",
    "Accuracy is 0.59\n",
    "Precision is 0.15\n",
    "Recall is 0.53\n",
    "\n",
    "One interesting finding is that SVM results did not change with balanced and unbalanced data. We concluded this was because balancing the data just recreates same rows from the data. This means that the Geometric margins will still be the same even after balancing the data.\n",
    "\n",
    "Knn slightly improve but still had low recall at 0.22.\n",
    "\n",
    "Naive base also improved a lot in terms of recall but still has low precision and low AUC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}