heartDisease_MLP - Attention - Low Dimensions XAI - Experiment 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\Mubashir Iqbal\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\Mubashir Iqbal\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as mplot\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "import os\n",
    "import subprocess\n",
    "import sklearn\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.datasets import load_files\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, accuracy_score, f1_score, precision_score, recall_score,  precision_recall_fscore_support\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    " \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    " \n",
    "\n",
    "\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.utils import to_categorical\n",
    "from keras.datasets import mnist \n",
    " \n",
    "from tensorflow.keras import layers, Input\n",
    "from tensorflow.keras.optimizers import AdamW\n",
    "from tensorflow.keras.metrics import AUC, Precision, Recall, F1Score\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "import pickle \n",
    "\n",
    "import shap\n",
    "import lime\n",
    "from lime import lime_tabular\n",
    "import random\n",
    "\n",
    "import dalex as dx \n",
    "\n",
    "from tabulate import tabulate \n",
    "\n",
    "# Define custom metrics\n",
    "recall = Recall()\n",
    "precision = Precision()\n",
    "f1_score = F1Score()\n",
    "auc = AUC()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of fileData: (37079, 40)\n",
      "Column Headings: Index(['Gender', 'Age', 'X60-sec-pulse', 'Systolic', 'Diastolic', 'Weight',\n",
      "       'Height', 'Body-Mass-Index', 'White-Blood-Cells', 'Lymphocyte',\n",
      "       'Monocyte', 'Eosinophils', 'Basophils', 'Red-Blood-Cells', 'Hemoglobin',\n",
      "       'Platelet-count', 'Segmented-Neutrophils', 'Hematocrit', 'Albumin',\n",
      "       'ALP', 'AST', 'ALT', 'Cholesterol', 'Creatinine', 'Glucose', 'Iron',\n",
      "       'LDH', 'Phosphorus', 'Bilirubin', 'Protein', 'Uric.Acid',\n",
      "       'Triglycerides', 'Total-Cholesterol', 'HDL', 'Glycohemoglobin',\n",
      "       'Moderate-work', 'Diabetes', 'Blood-Rel-Diabetes', 'Blood-Rel-Stroke',\n",
      "       'CoronaryHeartDisease'],\n",
      "      dtype='object')\n",
      "Number of Records: 37079\n",
      "\n",
      "Number of Missing Values: 0\n",
      "Number of duplicate records removed: 0\n",
      "Shape of fileData: (37079, 40)\n",
      "Shape of fileData End: (37079, 40)\n",
      "\n",
      "\n",
      "columns of x:: 20 \n",
      "\n",
      " and features of X: Index(['Age', 'Gender', 'Blood-Rel-Stroke', 'Triglycerides',\n",
      "       'Blood-Rel-Diabetes', 'Cholesterol', 'Platelet-count', 'Diabetes',\n",
      "       'Albumin', 'Hemoglobin', 'Moderate-work', 'Diastolic', 'Protein',\n",
      "       'Height', 'X60-sec-pulse', 'White-Blood-Cells', 'Bilirubin',\n",
      "       'Hematocrit', 'HDL', 'Systolic'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "dataSetIndex = 6\n",
    "needToMakePictureOfTrees = 0\n",
    "''' ---------------------------------------------------------- '''\n",
    "dataSetFilePath = \"\"\n",
    "dataSetName = \"\"\n",
    "dataSetResultDirectory = \"./\"\n",
    "\n",
    "if(dataSetIndex == 0):\n",
    "    dataSetFilePath = \"./heartDisease/0_statLog_dataSet.csv\"\n",
    "    dataSetName = \"SateLog_DataSet\"\n",
    "elif (dataSetIndex == 1):\n",
    "    dataSetFilePath = \"./heartDisease/1_heart_statlog_cleveland_hungary_final.csv\"\n",
    "    dataSetName = \"ALL_StateLog_CleveLand_Hungary\"\n",
    "elif (dataSetIndex == 2):\n",
    "    dataSetFilePath = \"./heartDisease/2_cleveland.csv\"\n",
    "    dataSetName = \"Cleveland\"\n",
    "elif (dataSetIndex == 3):\n",
    "    dataSetFilePath = \"./heartDisease/3_framingham.csv\"\n",
    "    dataSetName = \"framingham\"\n",
    "elif (dataSetIndex == 4):\n",
    "    dataSetFilePath = \"./heartDisease/4_CardiacPrediction.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "elif (dataSetIndex == 5):\n",
    "    dataSetFilePath = \"./heartDisease/5_CardiacPredictionLessDimensions.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "elif (dataSetIndex == 6):\n",
    "    dataSetFilePath = \"./heartDisease/6_CardiacPredictionFewDimensions.xlsx\"\n",
    "    dataSetName = \"CardiacPrediction\"\n",
    "else:\n",
    "    dataSetFilePath = \"\"\n",
    "    dataSetName = \"\"\n",
    "\n",
    "if(dataSetIndex==4 or dataSetIndex==5 or dataSetIndex==6):\n",
    "    #fileData = pd.read_excel(dataSetFilePath, sheet_name='CoroHeartDis')\n",
    "    fileData = pd.read_excel(dataSetFilePath)\n",
    "else:\n",
    "    fileData = pd.read_csv(dataSetFilePath)\n",
    "\n",
    "print(\"Shape of fileData: {}\".format(fileData.shape))\n",
    "print(\"Column Headings: {}\".format(fileData.__dataframe__().column_names()))\n",
    "print(\"Number of Records: {}\".format(fileData.__dataframe__().num_rows()))\n",
    "\n",
    "\n",
    "missingValues = fileData.isnull().any().sum()\n",
    "print(f\"\\nNumber of Missing Values: {missingValues}\")\n",
    "\n",
    "num_rows_before = fileData.shape[0]\n",
    "# Remove duplicate records based on all columns\n",
    "fileData.drop_duplicates(inplace=True)\n",
    "# Check the number of rows after removing duplicates\n",
    "num_rows_after = fileData.shape[0]\n",
    "# Print the number of duplicate records removed\n",
    "num_duplicates_removed = num_rows_before - num_rows_after\n",
    "print(f\"Number of duplicate records removed: {num_duplicates_removed}\")\n",
    " \n",
    " # Preprocess Steps from the ChatGPT\n",
    "# 1. Handling Missing Values:\n",
    "fileData = fileData.dropna()\n",
    "print(\"Shape of fileData: {}\".format(fileData.shape))              \n",
    "#fileData.replace({'?': np.nan}).dropna().astype(float)\n",
    "#fileData = fileData.fillna(0) \n",
    "\n",
    "fileData = fileData.fillna(0) \n",
    "\n",
    "print(\"Shape of fileData End: {}\".format(fileData.shape))\n",
    "\n",
    "finalResultTable = [ ['Index', 'Method', 'Accuracy %','Recall %','Precision %','F1 Score','AUC'], ]  \n",
    "\n",
    "\n",
    "#X = fileData.drop(fileData.__dataframe__().column_names()[-1], axis=1)  # Features\n",
    "#cols = ['Gender', 'Age','Annual-Family-Income', 'Cholesterol', 'Diabetes', 'Triglycerides', 'Red-Cell-Distribution-Width', 'X60-sec-pulse', 'Height', 'Albumin', 'Blood-Rel-Stroke', 'Blood-Rel-Diabetes', 'HDL', 'Moderate-work','Iron', 'Hemoglobin','Protein', 'SEQN'   ] \n",
    "cols = ['Age','Gender','Blood-Rel-Stroke','Triglycerides','Blood-Rel-Diabetes','Cholesterol','Platelet-count','Diabetes','Albumin','Hemoglobin','Moderate-work','Diastolic','Protein','Height','X60-sec-pulse','White-Blood-Cells','Bilirubin','Hematocrit','HDL','Systolic' ] \n",
    "X = fileData[cols]\n",
    "Y = fileData[fileData.__dataframe__().column_names()[-1]]  # Labels\n",
    "\n",
    "columns = fileData.__dataframe__().column_names() \n",
    "totalRecords = (fileData.__dataframe__().num_rows())\n",
    "\n",
    "print(\"\\n\")\n",
    "print(\"columns of x:: {} \\n\\n and features of X: {}\".format(len(X.columns), X.columns))\n",
    "\n",
    "dataSetResultDirectory = \"./\"\n",
    "dataSetResultDirectory += (\"DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_\" + dataSetName)\n",
    "dataSetResultDirectory += \"/\"\n",
    "if not os.path.isdir(dataSetResultDirectory):\n",
    "    os.makedirs(dataSetResultDirectory)\n",
    "\n",
    "dataSetName += \"_{}\".format(fileData.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target Column Name: CoronaryHeartDisease\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = fileData.__dataframe__().column_names() \n",
    "totalRecords = (fileData.__dataframe__().num_rows())\n",
    "columnsForGraph = []\n",
    "columnsForGraph.clear()\n",
    "tableDataRow = []\n",
    "for column in columns:\n",
    "    singleColumnCount = fileData[column].value_counts()\n",
    "    if(len(singleColumnCount) < 3):\n",
    "        #print('Column Name:{} -> total records:{}'.format(column, totalRecords ) )\n",
    "        #print('Number of classes:', len(singleColumnCount))\n",
    "        #print('Class distribution:')\n",
    "        #print(singleColumnCount)\n",
    "        #print(\"np Array: {}\".format(np.array(singleColumnCount)))\n",
    "        #print(\"index: 0: {} -> {} %\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100))\n",
    "        #print(\"index: 1: {} -> {} %\".format(np.array(singleColumnCount)[1], ( np.array(singleColumnCount)[1] /totalRecords) * 100))  \n",
    "        #print('---------------------------------------------------------------')\n",
    "        columnsForGraph.append(column)\n",
    " \n",
    "\n",
    "tableDataRow = [\n",
    "    ['Index', 'Column Name', 'Total Classes','Class A Records','Class B Records'],\n",
    "    \n",
    "]\n",
    "\n",
    "indexx = 1\n",
    "for column in columnsForGraph:\n",
    "    singleColumnCount = fileData[column].value_counts()\n",
    "    singleRowInTable = [] \n",
    "    singleRowInTable.append(indexx)\n",
    "    singleRowInTable.append(column)\n",
    "    singleRowInTable.append(len(singleColumnCount))\n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[1], (np.array(singleColumnCount)[1] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    indexx += 1\n",
    "    tableDataRow.append(singleRowInTable) \n",
    "\n",
    "# Create the figure and axes\n",
    "fig, ax = mplot.subplots() \n",
    "table = mplot.table(cellText=tableDataRow, loc='center') \n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(12) \n",
    "table.scale(2.0, 2.0) \n",
    "print(\"Target Column Name: {}\".format(columns[-1]))\n",
    "\n",
    "\n",
    "dataSetString = \"Dataset:  {}, Total Records: {}, No. Features: {}\".format(dataSetName, totalRecords, fileData.__dataframe__().num_columns())\n",
    "target =\"Target Column Name: {} , No of Classes: {}\".format(columns[-1], len(fileData[columns[-1]].value_counts()))\n",
    "distributionOfTargetClassA =\"Class A Records: {} , {:.2f} %\".format(np.array(fileData[columns[-1]].value_counts())[0], (np.array(fileData[columns[-1]].value_counts())[0] /totalRecords) * 100)\n",
    "distributionOfTargetClassB =\"Class B Records: {} , {:.2f} %\".format(np.array(fileData[columns[-1]].value_counts())[1], (np.array(fileData[columns[-1]].value_counts())[1] /totalRecords) * 100)\n",
    "\n",
    "fig.text(-0.1, +0.25,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "fig.text(-0.1, +0.20,  target, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "fig.text(-0.1, 0.15,  distributionOfTargetClassA, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, 0.10,  distributionOfTargetClassB, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    " \n",
    " \n",
    "mplot.axis('off')\n",
    "mplot.title(f'Exploring Dataset - {dataSetName}' ,fontsize=16, fontweight='bold') \n",
    "\n",
    "picturePath = \"{}1.DataSet_analysis_{}.png\".format(dataSetResultDirectory, dataSetName)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "#mplot.savefig('DataSet_analysis.png', dpi=300)\n",
    "mplot.show()\n",
    "mplot.close()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of fileData: (37079, 40) , target Len:37079\n",
      "X: (37079, 20) , Y:(37079,)\n",
      "Target Column Name:: CoronaryHeartDisease \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find the number of physical cores for the following reason:\n",
      "found 0 physical cores < 1\n",
      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
      "  File \"c:\\Users\\Mubashir Iqbal\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 282, in _count_physical_cores\n",
      "    raise ValueError(f\"found {cpu_count_physical} physical cores < 1\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " X Train: Shape:: (56914, 20)\n",
      " X Test: Shape:: (14228, 20)\n",
      "Train DataSet Positive Class Records:: 28457\n",
      "Train DataSet Negative Class Records:: 28457\n",
      "Train DataSet Total Records:: 56914\n",
      "\n",
      "\n",
      "\n",
      "Test DataSet Positive Class Records:: 7114\n",
      "Test DataSet Negative Class Records:: 7114\n",
      "Test DataSet Total Records:: 14228\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of fileData: {} , target Len:{}\".format(fileData.shape, len(Y)))\n",
    "print(\"X: {} , Y:{}\".format(X.shape, Y.shape))\n",
    "#print(\"\\n\\nX: head:: \\n{}\".format(X.head()))\n",
    "#print(\"\\n\\nY: head::\\n {}\".format(Y.head()))\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=42)\n",
    " \n",
    "\n",
    "print(\"Target Column Name:: {} \\n\".format(fileData.__dataframe__().column_names()[-1]))\n",
    "\n",
    "\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "smote = SMOTE(random_state=42)\n",
    "\n",
    "'''\n",
    "# Undersample the majority class\n",
    "X_train, y_train = rus.fit_resample(X_train, y_train)\n",
    "X_test, y_test = rus.fit_resample(X_test, y_test)\n",
    "'''\n",
    "\n",
    "# Oversample the minority class using SMOTE\n",
    "X_train, y_train = smote.fit_resample(X_train, y_train)\n",
    "#X_test, y_test = rus.fit_resample(X_test, y_test)\n",
    "X_test, y_test = smote.fit_resample(X_test, y_test) \n",
    "\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "\n",
    "\n",
    "print(\"\\n X Train: Shape:: {}\".format(X_train.shape))\n",
    "print(\" X Test: Shape:: {}\".format(X_test.shape))  \n",
    " \n",
    " \n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_train:\n",
    "    if(i == 0):\n",
    "        negativeClass += 1\n",
    "    if(i == 1):\n",
    "        positiveClass += 1\n",
    "print(\"Train DataSet Positive Class Records:: {}\".format(positiveClass)) \n",
    "print(\"Train DataSet Negative Class Records:: {}\".format(negativeClass)) \n",
    "print(\"Train DataSet Total Records:: {}\".format(positiveClass + negativeClass)) \n",
    "\n",
    "print(\"\\n\\n\") \n",
    "\n",
    "\n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_test:\n",
    "    if(i == 0):\n",
    "        negativeClass += 1\n",
    "    if(i == 1):\n",
    "        positiveClass += 1\n",
    "print(\"Test DataSet Positive Class Records:: {}\".format(positiveClass)) \n",
    "print(\"Test DataSet Negative Class Records:: {}\".format(negativeClass)) \n",
    "print(\"Test DataSet Total Records:: {}\".format(positiveClass + negativeClass)) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columnsForGraph = []\n",
    "columnsForGraph.clear()\n",
    "tableDataRow = []\n",
    "\n",
    "\n",
    "# Concatenate feature and target data for both training and testing sets\n",
    "df_train = pd.concat([X_train, y_train], axis=1)\n",
    "df_test = pd.concat([X_test, y_test], axis=1)\n",
    "df_combined = pd.concat([df_train, df_test], axis=0, ignore_index=True)\n",
    "\n",
    "columns = df_combined.__dataframe__().column_names() \n",
    "totalRecords = (df_combined.__dataframe__().num_rows())\n",
    "for column in columns:\n",
    "    singleColumnCount = df_combined[column].value_counts()\n",
    "    if(len(singleColumnCount) < 3):\n",
    "        #print('Column Name:{} -> total records:{}'.format(column, totalRecords ) )\n",
    "        #print('Number of classes:', len(singleColumnCount))\n",
    "        #print('Class distribution:')\n",
    "        #print(singleColumnCount)\n",
    "        #print(\"np Array: {}\".format(np.array(singleColumnCount)))\n",
    "        #print(\"index: 0: {} -> {} %\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100))\n",
    "        #print(\"index: 1: {} -> {} %\".format(np.array(singleColumnCount)[1], ( np.array(singleColumnCount)[1] /totalRecords) * 100))  \n",
    "        #print('---------------------------------------------------------------')\n",
    "        columnsForGraph.append(column)\n",
    " \n",
    "tableDataRow = [ ['Index', 'Column Name', 'Total Classes','Class A Records','Class B Records'], ]\n",
    "\n",
    "indexx = 1\n",
    "for column in columnsForGraph:\n",
    "    singleColumnCount = df_combined[column].value_counts()\n",
    "    singleRowInTable = [] \n",
    "    singleRowInTable.append(indexx)\n",
    "    singleRowInTable.append(column)\n",
    "    singleRowInTable.append(len(singleColumnCount))\n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[0], (np.array(singleColumnCount)[0] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    cellDataString = \"{} -> {:.2f}%\".format(np.array(singleColumnCount)[1], (np.array(singleColumnCount)[1] /totalRecords) * 100)\n",
    "    singleRowInTable.append((cellDataString)) \n",
    "    indexx += 1\n",
    "    tableDataRow.append(singleRowInTable) \n",
    "\n",
    " \n",
    "# Determine the number of rows in the table (excluding the header)\n",
    "num_rows = len(tableDataRow) + 1\n",
    "# Calculate the desired figure size based on the number of rows\n",
    "fig_width = 6  # Set the desired width of the figure\n",
    "fig_height = num_rows * 0.5  # Adjust the scaling factor to control the height\n",
    "\n",
    "fig, ax = mplot.subplots(figsize=(fig_width, fig_height)) \n",
    "table = mplot.table(cellText=tableDataRow, loc='center') \n",
    "\n",
    "table.auto_set_column_width(col=list(range(len(tableDataRow[0]))))\n",
    "\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(12) \n",
    "table.scale(2.0, 2.0) \n",
    "\n",
    "dataSetString = \"Dataset:  {}, Total Records: {}, No. Features: {}\".format(dataSetName, totalRecords, df_combined.__dataframe__().num_columns())\n",
    "target =\"Target Column Name: {} , No of Classes: {}\".format(columns[-1], len(df_combined[columns[-1]].value_counts()))\n",
    "distributionOfTargetClassA =\"Class A Records: {} , {:.2f} %\".format(np.array(df_combined[columns[-1]].value_counts())[0], (np.array(df_combined[columns[-1]].value_counts())[0] /totalRecords) * 100)\n",
    "distributionOfTargetClassB =\"Class B Records: {} , {:.2f} %\".format(np.array(df_combined[columns[-1]].value_counts())[1], (np.array(df_combined[columns[-1]].value_counts())[1] /totalRecords) * 100)\n",
    "\n",
    "fig.text(-0.1, +0.10,  dataSetString, horizontalalignment='left', wrap=False , fontsize=12 )  \n",
    "fig.text(-0.1, +0.02,  target, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "fig.text(-0.1, -0.06,  distributionOfTargetClassA, horizontalalignment='left', wrap=False , fontsize=12  )   \n",
    "fig.text(-0.1, -0.14,  distributionOfTargetClassB, horizontalalignment='left', wrap=False  , fontsize=12 )   \n",
    "\n",
    "remarks = \"You need to distribute the target class in equal number of records in training-set.\"\n",
    "#fig.text(-0.2, -0.15,  remarks, horizontalalignment='left', wrap=True ,fontsize=12, fontweight='bold' )   \n",
    " \n",
    "mplot.axis('off')\n",
    "mplot.title(f'Exploring Dataset after SMOTE' ,fontsize=16, fontweight='bold') \n",
    "picturePath = \"{}02.DataSet_analysis_After_dataAugmentation_{}.png\".format(dataSetResultDirectory, dataSetName)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "#mplot.savefig(picturePath,  dpi=300 )\n",
    "mplot.show()\n",
    "mplot.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeCorrelationPic(correlationMatrix, numberOfTopFeatures, targetColumnName):     \n",
    "    correlation_values = correlationMatrix.abs()\n",
    "    sorted_correlation = correlation_values.unstack().sort_values(ascending=False)\n",
    "    sorted_correlation = sorted_correlation[sorted_correlation != 1.0]\n",
    "\n",
    "    num_features = numberOfTopFeatures  # Number of top features to display\n",
    "    top_features = sorted_correlation.head(num_features)\n",
    "    print(\"Top\", num_features, \"features based on correlation:\")\n",
    "    print(top_features)\n",
    " \n",
    "    top_features = correlationMatrix.abs().nlargest(numberOfTopFeatures, targetColumnName)[targetColumnName].index\n",
    "    top_correlation_matrix = correlationMatrix.loc[top_features, top_features]\n",
    "\n",
    "    mplot.figure(figsize=(10, 8))\n",
    "    sns.heatmap(top_correlation_matrix, annot=True, cmap='coolwarm')\n",
    "    # Set the title of the plot\n",
    "    mplot.title('Correlation Heatmap ({})'.format(dataSetName))\n",
    "    \n",
    "    picturePath = \"Correlation_Matrix_DateSetName_{}.png\".format(dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    mplot.show()\n",
    "    mplot.close()\n",
    "\n",
    "def makeConfusionMatrixPic(method, dataSet, classifierObj , X_test, y_test, predicted_Y):\n",
    "    display = ConfusionMatrixDisplay.from_estimator(classifierObj, X_test, y_test, display_labels=['Healthy', \"Heart Disease\"], cmap=mplot.cm.Blues) #, normalize=\"true\"\n",
    "    display.ax_.set_title(\"Confusion Matrix ({} Model)\".format(method))\n",
    "    ax_.set_xlabel('\\nPredicted Values')\n",
    "    ax_.set_ylabel('Actual Values ')\n",
    "\n",
    "\n",
    "    accuracyString =\"Accuracy {}: {:.2f}\".format(method, accuracy_score(y_test, predicted_Y)*100.0 ) \n",
    "    recallString =  'Recall {}: {:.2f}'.format(method, recall_score(y_test, predicted_Y) * 100.0)\n",
    "    precisionString = 'Precision {}: {:.2f}'.format(method, precision_score(y_test, predicted_Y) * 100.0) \n",
    "    dataSetString = \"Dataset: {}\".format(dataSet)\n",
    "\n",
    "    \n",
    "    if(classifierObj.n_features_in_ > 10):\n",
    "        featureListString = 'Total Features: {}'.format(classifierObj.n_features_in_) \n",
    "    else:\n",
    "        featureListString = 'Features: {}'.format(classifierObj.feature_names_in_) \n",
    "    \n",
    "    display.figure_.text(0.010, -0.05,  accuracyString, horizontalalignment='left', wrap=False )  \n",
    "    display.figure_.text(0.010, -0.09,  recallString, horizontalalignment='left', wrap=False )      \n",
    "    display.figure_.text(0.010, -0.13,  precisionString, horizontalalignment='left', wrap=False ) \n",
    "    display.figure_.text(0.010, -0.17,  dataSetString, horizontalalignment='left', wrap=False ) \n",
    "    display.figure_.text(0.010, -0.28,  featureListString, horizontalalignment='left', wrap=False ) \n",
    " \n",
    "    picturePath = \"{}Confusion_Matrix_{}_{}.png\".format(dataSetResultDirectory, method, dataSetName)\n",
    "    mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "    #print(\"{} Confusion Matrix saved:: path: {}\".format(method, picturePath))\n",
    "    #os.startfile(picturePath)\n",
    "    mplot.show()\n",
    "    mplot.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (56914, 20)   and dType: 20\n",
      "X_train_scaler shape: (56914, 20)   and dType: float64\n",
      "X_test_scaler shape: (14228, 20)   and dType: float64\n",
      "y_train shape: (56914,)   and dType: int64\n",
      "y_test_scaler shape: (14228, 1)   and dType: float32\n",
      "features shape: (56914, 20)   and dType: float64\n",
      "target shape: (56914, 1)   and dType: float64\n"
     ]
    }
   ],
   "source": [
    "X_train_normalized = tf.keras.utils.normalize(X_train, axis=1)\n",
    "X_test_normalized = tf.keras.utils.normalize(X_test, axis=1)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaler = scaler.fit_transform(X_train)\n",
    "X_test_scaler = scaler.fit_transform(X_test) \n",
    "# Our vectorized labels\n",
    "\n",
    "X_train_f32 = np.asarray(X_train).astype(np.float32)  #.astype('float32').reshape((-1,1))\n",
    "X_test_f32 = np.asarray(X_test).astype(np.float32)\n",
    "\n",
    "#y_train_scaler = np.asarray(y_train).astype('float32').reshape((-1,1))\n",
    "y_test_scaler = np.asarray(y_test).astype('float32').reshape((-1,1))\n",
    "\n",
    " \n",
    "\n",
    "# Separate features and target variable\n",
    "features = X_train_scaler # data.iloc[:, :-1]\n",
    "target = np.asarray(y_train).astype('float64').reshape((-1,1))  #data['CoronaryHeartDisease']\n",
    " \n",
    " \n",
    "\n",
    "print(\"X_train shape: {}   and dType: {}\".format(X_train.shape, len(X_train.columns)))\n",
    "print(\"X_train_scaler shape: {}   and dType: {}\".format(X_train_scaler.shape, X_train_scaler.dtype))\n",
    "print(\"X_test_scaler shape: {}   and dType: {}\".format(X_test_scaler.shape, X_test_scaler.dtype)) \n",
    "\n",
    "\n",
    "print(\"y_train shape: {}   and dType: {}\".format(y_train.shape, y_train.dtype))  \n",
    "print(\"y_test_scaler shape: {}   and dType: {}\".format(y_test_scaler.shape, y_test_scaler.dtype))  \n",
    "\n",
    "\n",
    "print(\"features shape: {}   and dType: {}\".format(features.shape, features.dtype))\n",
    "print(\"target shape: {}   and dType: {}\".format(target.shape, target.dtype)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Node: 10\n",
      "Number 2 of Node: 13.0\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "# Define the attention layer\n",
    "print(\"Number of Node: {}\".format(features.shape[1]//2))\n",
    "print(\"Number 2 of Node: {}\".format(features.shape[1]//1.5))\n",
    "\n",
    "\n",
    "class AttentionLayer(tf.keras.layers.Layer):\n",
    "    def call(self, inputs, **kwargs):\n",
    "        q, v = inputs\n",
    "        attention = tf.keras.layers.Attention()([q, v])\n",
    "        return attention\n",
    "\n",
    "# Build the model\n",
    "input_layer = Input(shape=(features.shape[1],))\n",
    "\n",
    "# Traditional neural network part\n",
    "x = layers.Dense(features.shape[1]*2, activation='relu')(input_layer)\n",
    "x = layers.Dense(features.shape[1], activation='relu')(x) \n",
    "x = layers.Dense(features.shape[1], activation='relu')(x) \n",
    "x = layers.BatchNormalization()(x)\n",
    " \n",
    "attention = AttentionLayer()([x, x]) \n",
    "x = layers.Concatenate()([x, attention])\n",
    "x = layers.Dense(features.shape[1], activation='relu')(x)\n",
    "x = layers.BatchNormalization()(x)\n",
    "x = layers.Dense(features.shape[1]//2, activation='relu')(x)\n",
    "# Output layer\n",
    "output_layer = layers.Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "\n",
    "\n",
    "model = tf.keras.Model(inputs=input_layer, outputs=output_layer) \n",
    "optimizer = AdamW(learning_rate=0.001)\n",
    "model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy', recall, precision, f1_score, auc])\n",
    "\n",
    "  \n",
    "# Display the model summary\n",
    "#model.summary()\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "WARNING:tensorflow:From c:\\Users\\Mubashir Iqbal\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\Mubashir Iqbal\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "2277/2277 [==============================] - 13s 4ms/step - loss: 0.3625 - accuracy: 0.8436 - recall: 0.8651 - precision: 0.8295 - f1_score: 0.6667 - auc: 0.9176\n",
      "Epoch 2/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.3072 - accuracy: 0.8728 - recall: 0.9019 - precision: 0.8523 - f1_score: 0.6667 - auc: 0.9404\n",
      "Epoch 3/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.2911 - accuracy: 0.8798 - recall: 0.9113 - precision: 0.8573 - f1_score: 0.6667 - auc: 0.9461\n",
      "Epoch 4/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.2782 - accuracy: 0.8854 - recall: 0.9141 - precision: 0.8644 - f1_score: 0.6667 - auc: 0.9503\n",
      "Epoch 5/40\n",
      "2277/2277 [==============================] - 10s 4ms/step - loss: 0.2677 - accuracy: 0.8908 - recall: 0.9217 - precision: 0.8680 - f1_score: 0.6667 - auc: 0.9534\n",
      "Epoch 6/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.2564 - accuracy: 0.8962 - recall: 0.9268 - precision: 0.8734 - f1_score: 0.6667 - auc: 0.9566\n",
      "Epoch 7/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.2467 - accuracy: 0.9007 - recall: 0.9333 - precision: 0.8761 - f1_score: 0.6667 - auc: 0.9592\n",
      "Epoch 8/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2398 - accuracy: 0.9045 - recall: 0.9364 - precision: 0.8802 - f1_score: 0.6667 - auc: 0.9613\n",
      "Epoch 9/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2331 - accuracy: 0.9079 - recall: 0.9413 - precision: 0.8824 - f1_score: 0.6667 - auc: 0.9633\n",
      "Epoch 10/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2300 - accuracy: 0.9097 - recall: 0.9437 - precision: 0.8837 - f1_score: 0.6667 - auc: 0.9638\n",
      "Epoch 11/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2260 - accuracy: 0.9121 - recall: 0.9447 - precision: 0.8870 - f1_score: 0.6667 - auc: 0.9650\n",
      "Epoch 12/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2218 - accuracy: 0.9141 - recall: 0.9460 - precision: 0.8892 - f1_score: 0.6667 - auc: 0.9662\n",
      "Epoch 13/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2193 - accuracy: 0.9152 - recall: 0.9482 - precision: 0.8895 - f1_score: 0.6667 - auc: 0.9663\n",
      "Epoch 14/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2163 - accuracy: 0.9166 - recall: 0.9476 - precision: 0.8922 - f1_score: 0.6667 - auc: 0.9672\n",
      "Epoch 15/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2120 - accuracy: 0.9193 - recall: 0.9484 - precision: 0.8962 - f1_score: 0.6667 - auc: 0.9685\n",
      "Epoch 16/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2102 - accuracy: 0.9211 - recall: 0.9527 - precision: 0.8960 - f1_score: 0.6667 - auc: 0.9688\n",
      "Epoch 17/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2086 - accuracy: 0.9209 - recall: 0.9490 - precision: 0.8986 - f1_score: 0.6667 - auc: 0.9694\n",
      "Epoch 18/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2054 - accuracy: 0.9220 - recall: 0.9504 - precision: 0.8993 - f1_score: 0.6667 - auc: 0.9700\n",
      "Epoch 19/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2059 - accuracy: 0.9226 - recall: 0.9527 - precision: 0.8986 - f1_score: 0.6667 - auc: 0.9698\n",
      "Epoch 20/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2035 - accuracy: 0.9234 - recall: 0.9542 - precision: 0.8989 - f1_score: 0.6667 - auc: 0.9704\n",
      "Epoch 21/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2022 - accuracy: 0.9241 - recall: 0.9533 - precision: 0.9007 - f1_score: 0.6667 - auc: 0.9706\n",
      "Epoch 22/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1974 - accuracy: 0.9254 - recall: 0.9547 - precision: 0.9018 - f1_score: 0.6667 - auc: 0.9720\n",
      "Epoch 23/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1957 - accuracy: 0.9267 - recall: 0.9551 - precision: 0.9038 - f1_score: 0.6667 - auc: 0.9720\n",
      "Epoch 24/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1926 - accuracy: 0.9283 - recall: 0.9559 - precision: 0.9060 - f1_score: 0.6667 - auc: 0.9729\n",
      "Epoch 25/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.1919 - accuracy: 0.9294 - recall: 0.9551 - precision: 0.9085 - f1_score: 0.6667 - auc: 0.9731\n",
      "Epoch 26/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1924 - accuracy: 0.9284 - recall: 0.9554 - precision: 0.9064 - f1_score: 0.6667 - auc: 0.9730\n",
      "Epoch 27/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.1893 - accuracy: 0.9301 - recall: 0.9562 - precision: 0.9088 - f1_score: 0.6667 - auc: 0.9740\n",
      "Epoch 28/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1877 - accuracy: 0.9310 - recall: 0.9579 - precision: 0.9091 - f1_score: 0.6667 - auc: 0.9741\n",
      "Epoch 29/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1866 - accuracy: 0.9317 - recall: 0.9574 - precision: 0.9106 - f1_score: 0.6667 - auc: 0.9742\n",
      "Epoch 30/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1861 - accuracy: 0.9319 - recall: 0.9578 - precision: 0.9107 - f1_score: 0.6667 - auc: 0.9745\n",
      "Epoch 31/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1830 - accuracy: 0.9326 - recall: 0.9589 - precision: 0.9109 - f1_score: 0.6667 - auc: 0.9750\n",
      "Epoch 32/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1818 - accuracy: 0.9341 - recall: 0.9590 - precision: 0.9136 - f1_score: 0.6667 - auc: 0.9752\n",
      "Epoch 33/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1824 - accuracy: 0.9333 - recall: 0.9592 - precision: 0.9119 - f1_score: 0.6667 - auc: 0.9751\n",
      "Epoch 34/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1819 - accuracy: 0.9347 - recall: 0.9604 - precision: 0.9134 - f1_score: 0.6667 - auc: 0.9751\n",
      "Epoch 35/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1802 - accuracy: 0.9343 - recall: 0.9594 - precision: 0.9135 - f1_score: 0.6667 - auc: 0.9756\n",
      "Epoch 36/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1786 - accuracy: 0.9357 - recall: 0.9617 - precision: 0.9142 - f1_score: 0.6667 - auc: 0.9756\n",
      "Epoch 37/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.1783 - accuracy: 0.9351 - recall: 0.9601 - precision: 0.9143 - f1_score: 0.6667 - auc: 0.9761\n",
      "Epoch 38/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1767 - accuracy: 0.9356 - recall: 0.9611 - precision: 0.9144 - f1_score: 0.6667 - auc: 0.9763\n",
      "Epoch 39/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1767 - accuracy: 0.9365 - recall: 0.9617 - precision: 0.9156 - f1_score: 0.6667 - auc: 0.9761\n",
      "Epoch 40/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.1756 - accuracy: 0.9370 - recall: 0.9630 - precision: 0.9155 - f1_score: 0.6667 - auc: 0.9764\n"
     ]
    }
   ],
   "source": [
    "numberOfEpochs = 40\n",
    "batchSizeOfTraining = 25\n",
    "history = 0\n",
    "\n",
    "history = model.fit(features, target, epochs=numberOfEpochs, batch_size=batchSizeOfTraining)\n",
    "\n",
    "model2 = model\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_test_float64 = np.asarray(y_test).astype('float64').reshape((-1,1))  #data['CoronaryHeartDisease']\n",
    "history = model2.fit(X_test_scaler, y_test_float64, epochs=2, batch_size=batchSizeOfTraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445/445 [==============================] - 2s 3ms/step - loss: 0.1718 - accuracy: 0.9354 - recall: 0.9528 - precision: 0.9208 - f1_score: 0.6667 - auc: 0.9813\n",
      "Test loss: 17.181837558746338\n",
      "Test accuracy: 93.54090690612793\n",
      "Test recall: 95.27691602706909\n",
      "Test precision: 92.07987785339355\n",
      "Test F1 score: [66.66979]\n",
      "Test AUC: 98.13074469566345\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the evaluate method\n",
    "y_test_float64 = np.asarray(y_test).astype('float64').reshape((-1,1))  #data['CoronaryHeartDisease']\n",
    "\n",
    "loss, accuracy, recall_value, precision_value, f1_score_value, auc_value = model2.evaluate(X_test_scaler, y_test_float64)\n",
    "\n",
    "# Print the results\n",
    "print('Test loss: {}'.format(loss*100))\n",
    "print('Test accuracy: {}'.format(accuracy*100))\n",
    "print('Test recall: {}'.format(recall_value*100))\n",
    "print('Test precision: {}'.format(precision_value*100))\n",
    "print('Test F1 score: {}'.format(f1_score_value*100))\n",
    "print('Test AUC: {}'.format(auc_value*100))\n",
    "\n",
    "\n",
    "picturePath = \"{}Model_Evaluation_{}_{}_Epoch_{}.png\".format(dataSetResultDirectory, \"Testing_Accuracy\", dataSetName, numberOfEpochs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445/445 [==============================] - 1s 2ms/step\n",
      "MLP with Attention layer Confusion Matrix saved:: path: ./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/Model_Evaluation_MLP with Attention layer_CardiacPrediction_(37079, 40)_Epoch_40.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.5  # Adjust the threshold as needed\n",
    "binary_predictions = (model2.predict(X_test_scaler) > threshold).astype(int)\n",
    "\n",
    "cm = confusion_matrix(y_test_float64, binary_predictions)\n",
    "display = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Healthy', 'Heart Disease'])\n",
    "\n",
    "display.plot(cmap=plt.cm.Blues, values_format=\".4g\") \n",
    "\n",
    "\n",
    "method = \"MLP with Attention layer\"\n",
    "display.ax_.set_title(\"Results {} Model\".format(method),fontsize=16, fontweight='bold')\n",
    "display.ax_.set_xlabel('\\nPredicted Values')\n",
    "display.ax_.set_ylabel('Actual Values ')\n",
    "\n",
    "accuracyString =\"Accuracy {}: {:.2f}%\".format(method, accuracy*100.0 ) \n",
    "recallString =  'Recall {}: {:.2f}%'.format(method, recall_value* 100.0)\n",
    "precisionString = 'Precision {}: {:.2f}%'.format(method, precision_value * 100.0) \n",
    "f1String = \"F1 Score: {:.2f}\".format(f1_score_value[0] * 100.0)\n",
    "featureListString = \"AUC Score: {:.2f}%\".format(auc_value * 100.0)\n",
    "totaldataSetString = \"Total Dataset records: {}\".format((len(features)+len(X_test_scaler)))\n",
    "testingdataSetString = \"Testing records: {} , {:.1f}%\".format(len(X_test_scaler), ( ( len(X_test_scaler) / (len(features)+len(X_test_scaler)) )*100.0  ))\n",
    "numberOfEpochsString = \"Number of Epoches: {}\".format(numberOfEpochs)\n",
    "batchSizeOfTrainingString = \"BatchSize for Epoch: {}\".format(batchSizeOfTraining)\n",
    "\n",
    "display.figure_.text(0.010, -0.05,  accuracyString, horizontalalignment='left', wrap=False )  \n",
    "display.figure_.text(0.010, -0.09,  recallString, horizontalalignment='left', wrap=False )      \n",
    "display.figure_.text(0.010, -0.13,  precisionString, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.17,  f1String, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.21,  featureListString, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.26,  totaldataSetString, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.30,  testingdataSetString, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.34,  numberOfEpochsString, horizontalalignment='left', wrap=False ) \n",
    "display.figure_.text(0.010, -0.38,  batchSizeOfTrainingString, horizontalalignment='left', wrap=False ) \n",
    " \n",
    "picturePath = \"{}Model_Evaluation_{}_{}_Epoch_{}.png\".format(dataSetResultDirectory, method, dataSetName, numberOfEpochs)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "print(\"{} Confusion Matrix saved:: path: {}\".format(method, picturePath))\n",
    "\n",
    "mplot.show()\n",
    "mplot.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate AUC\n",
    "auc_score = roc_auc_score(y_test, binary_predictions)\n",
    "# Calculate ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, binary_predictions)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = {:.2f})'.format(auc_score))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "picturePath = \"{}Model_Evaluation_ROC_{}_{}_Epoch_{}.png\".format(dataSetResultDirectory, method, dataSetName, numberOfEpochs)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plot accuracy\n",
    "plt.plot(history.history['accuracy'], label='Accuracy')\n",
    "# Plot precision\n",
    "plt.plot(history.history['precision'], label='Precision' )\n",
    "# Plot loss\n",
    "plt.plot(history.history['loss'], label='Loss')\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.title('Training Evaluation Metrics')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Metrics Value')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "picturePath = \"{}Model_Training_Evaluation_{}_{}_Epoch_{}.png\".format(dataSetResultDirectory, method, dataSetName, numberOfEpochs)\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Features: 56914 -> Selected for SHAP:: 3983\n",
      "Total Test: 14228 -> Selected for SHAP:: 995\n"
     ]
    }
   ],
   "source": [
    "percentage = 0.07\n",
    "numberOfFeatures = int(len(features) *percentage)\n",
    "print(\"Total Features: {} -> Selected for SHAP:: {}\".format(len(features), numberOfFeatures))\n",
    "featuresForShap = X_train.columns #features[0:numberOfFeatures]\n",
    "#print(\" Features Name: {}\".format(  featuresForShap))\n",
    "\n",
    "numberOftest = int(len(X_test_scaler) * percentage)\n",
    "print(\"Total Test: {} -> Selected for SHAP:: {}\".format(len(X_test_scaler), numberOftest))\n",
    "testForShap = X_test_scaler[0:len(featuresForShap)]\n",
    "testForShap = X_test_scaler[0:numberOftest]\n",
    "#print(\" testForShap Name: {}\".format(  testForShap))\n",
    "\n",
    "shap_explainer = shap.Explainer(model, feature_names=featuresForShap, masker=shap.maskers.Independent(data=testForShap)) \n",
    "\n",
    "#shap_explainer = shap.Explainer(model) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PermutationExplainer explainer: 996it [02:35,  6.20it/s]                         \n"
     ]
    }
   ],
   "source": [
    "shap_values = shap_explainer.shap_values(testForShap)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate feature importance based on the mean absolute SHAP values\n",
    "feature_names = X.columns.tolist()\n",
    "# Calculate average SHAP values across all instances\n",
    "avg_shap_values = np.mean(shap_values, axis=0)\n",
    "# Print average SHAP values according to feature names\n",
    "print(\"Average SHAP values:\")\n",
    "for i in range(len(feature_names)):\n",
    "    pass\n",
    "    #print(f\"{feature_names[i]}: {avg_shap_values[i]*100.0}\")\n",
    "\n",
    "\n",
    "avg_shap_values2 = {}\n",
    "\n",
    "howManyFeatures = 15\n",
    "# Calculate feature importance based on the mean absolute SHAP values\n",
    "feature_importance = np.abs(shap_values).mean(axis=0)\n",
    "top_features_indices = np.argsort(feature_importance)[::-1][:howManyFeatures]\n",
    "# Select only the top features and corresponding SHAP values\n",
    "featureNamesSHAP = X.columns[top_features_indices]\n",
    "top_features = testForShap[:, top_features_indices]\n",
    "top_shap_values = shap_values[:, top_features_indices]\n",
    "\n",
    "print(\"\\n\\n--------------------------------------------------\")\n",
    "print(\"--------------------------------------------------\")\n",
    "print(\"Top SHAP values:\")\n",
    "for i in range(len(top_features_indices)):\n",
    "    feature_index = top_features_indices[i]\n",
    "    feature_name = feature_names[feature_index]\n",
    "    shap_value = np.mean(np.abs(top_shap_values[:, i]))\n",
    "    avg_shap_values2[i]= (shap_value * 100.0)\n",
    "    print(f\"{feature_name}: {shap_value * 100.0}\")\n",
    " \n",
    " \n",
    "\n",
    "# Plot the summary plot for the top 15 features\n",
    "shap.summary_plot(top_shap_values, top_features, feature_names=featureNamesSHAP, plot_type=\"bar\", show=False)\n",
    "ax = mplot.gca() \n",
    "ax.set_title(\"XAI SHAP Explainer Sorted  ({} Features)\".format(howManyFeatures) ,fontsize=16, fontweight='bold')     \n",
    "dataSetString = \"Dataset:  {}\".format(dataSetName)\n",
    "testingDatasetString =\"length of SHAP dataset: {}\".format(len(testForShap))\n",
    "shapTypeString =\"SHAP Type: {}\".format(repr(shap_explainer)) \n",
    "ax.figure.text(0.020, -0.05,  dataSetString, horizontalalignment='left', wrap=False )  \n",
    "ax.figure.text(0.020, -0.09,  testingDatasetString, horizontalalignment='left', wrap=False )   \n",
    "ax.figure.text(0.020, -0.13,  shapTypeString, horizontalalignment='left', wrap=False )   \n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_Bar_{}_{}_Sorted_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "mplot.show()\n",
    "#os.startfile(picturePath)\n",
    "mplot.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the summary plot for the top 15 features\n",
    "shap.summary_plot(top_shap_values, top_features, feature_names=featureNamesSHAP, show=False)\n",
    "ax = mplot.gca() \n",
    "ax.set_title(\"XAI SHAP Explainer Sorted  ({} Features)\".format(howManyFeatures) ,fontsize=16, fontweight='bold')     \n",
    "dataSetString = \"Dataset:  {}\".format(dataSetName)\n",
    "testingDatasetString =\"length of SHAP dataset: {}\".format(len(testForShap))\n",
    "shapTypeString =\"SHAP Type: {}\".format(repr(shap_explainer)) \n",
    "ax.figure.text(0.020, -0.05,  dataSetString, horizontalalignment='left', wrap=False )  \n",
    "ax.figure.text(0.020, -0.09,  testingDatasetString, horizontalalignment='left', wrap=False )   \n",
    "ax.figure.text(0.020, -0.13,  shapTypeString, horizontalalignment='left', wrap=False )   \n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_Bar_{}_{}_Sorted_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "mplot.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "mplot.show()\n",
    "#os.startfile(picturePath)\n",
    "mplot.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = X.columns.tolist() \n",
    " \n",
    "\n",
    "top_10_feature_names = [feature_names[i] for i in top_features_indices]\n",
    "top_10_shap_values = shap_values[:, top_features_indices] \n",
    "# Create a DataFrame for visualization\n",
    "df_top_10 = pd.DataFrame(data=top_10_shap_values, columns=top_10_feature_names)\n",
    "# Plotting with Seaborn's violinplot\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.violinplot(data=df_top_10, inner=\"quartile\", palette=\"muted\") \n",
    "plt.title('MLP Model with SHAP (XAI) Violin Plot')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_Violinplot_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight') \n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting a Bubble Chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "for i, feature in enumerate(top_10_feature_names):\n",
    "    size = np.abs(df_top_10[feature]) * 100  # Adjust the scale as needed\n",
    "    plt.scatter(x=[i] * len(df_top_10), y=df_top_10[feature], s=size, label=feature, alpha=0.6)\n",
    " \n",
    "plt.title('MLP Model with SHAP (XAI) Bubble Chart')\n",
    "plt.xlabel('Feature Index')\n",
    "plt.ylabel('SHAP Values')\n",
    "plt.xticks(range(len(top_10_feature_names)), top_10_feature_names, rotation=45, ha='right')\n",
    "plt.legend()\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_BubbleChart_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bubble Chart \n",
    "top_10_feature_names = [feature_names[i] for i in top_features_indices]\n",
    "top_10_avg_shap_values = avg_shap_values[top_features_indices]\n",
    "\n",
    "# Calculate the scale for bubble size based on the average values compared to others\n",
    "size_scale = np.abs(top_10_avg_shap_values) / np.max(np.abs(top_10_avg_shap_values))\n",
    "# Create a DataFrame for visualization\n",
    "df_top_10_avg_shap = pd.DataFrame({'Feature': top_10_feature_names, 'Average SHAP Value': top_10_avg_shap_values})\n",
    "# Plotting a Bubble Chart for top 10 average SHAP values\n",
    "plt.figure(figsize=(12, 6))\n",
    "size = size_scale * 1000  # Adjust the scale as needed\n",
    "plt.scatter(x=range(len(df_top_10_avg_shap)), y=df_top_10_avg_shap['Average SHAP Value'], s=size, alpha=0.6)\n",
    "\n",
    "plt.title('MLP Model with SHAP (XAI) Bubble Chart Average')\n",
    "plt.xlabel('Feature Index')\n",
    "plt.ylabel('Average SHAP Values')\n",
    "plt.xticks(range(len(df_top_10_avg_shap)), df_top_10_avg_shap['Feature'], rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_BubbleChartAverage_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting a boxplot for the top 10 features\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "x_axis_range = (-0.10, 0.10)  # Adjust the range as needed\n",
    "sns.boxplot(data=df_top_10, orient='v', palette='Set2')\n",
    "plt.title('MLP Model with SHAP (XAI) Box Plot')\n",
    "plt.xlabel('SHAP Values')\n",
    "plt.ylabel('Features')\n",
    "plt.xticks(range(len(df_top_10_avg_shap)), df_top_10_avg_shap['Feature'], rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_BoxPlot_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting an area chart for all SHAP values of the top 10 features\n",
    "plt.figure(figsize=(15, 10))\n",
    "for feature in top_10_feature_names:\n",
    "    sns.lineplot(x=range(df_top_10.shape[0]), y=df_top_10[feature], label=feature)\n",
    " \n",
    "plt.title('MLP Model with SHAP (XAI) Area Chart')\n",
    "\n",
    "plt.xlabel('Instances')\n",
    "plt.ylabel('SHAP Values')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_AreaChart_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# Assuming your features are in a pandas DataFrame, you can access feature names\n",
    "feature_names = X.columns.tolist()\n",
    "# Get the top 3 features based on their absolute average SHAP values\n",
    "top_3_feature_indices = np.argsort(np.abs(shap_values.mean(0)))[-3:]\n",
    "top_3_feature_names = [feature_names[i] for i in top_3_feature_indices]\n",
    "# Extract SHAP values for the top 3 features\n",
    "shap_values_top_3 = shap_values[:, top_3_feature_indices]\n",
    "# Create a DataFrame for visualization\n",
    "df_top_3 = pd.DataFrame(data=shap_values_top_3, columns=top_3_feature_names)\n",
    "\n",
    "# Create a 3D bubble plot\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    " \n",
    "\n",
    "# Scatter plot with adjusted size and color\n",
    "scatter = ax.scatter(df_top_3[top_3_feature_names[0]], df_top_3[top_3_feature_names[1]], df_top_3[top_3_feature_names[2]],\n",
    "                     s=800 * np.abs(df_top_3.mean(axis=1)),  # Adjust the size\n",
    "                     c=df_top_3.mean(axis=1), cmap='viridis', alpha=0.9, edgecolors='w', linewidth=0.8)  # Adjust the color\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel(top_3_feature_names[0])\n",
    "ax.set_ylabel(top_3_feature_names[1])\n",
    "ax.set_zlabel(top_3_feature_names[2])\n",
    "ax.set_title('3D Bubble Plot of Top 3 Features')\n",
    "plt.title('MLP Model with SHAP (XAI) 3D Bubble Plot')\n",
    "\n",
    "\n",
    "# Add colorbar\n",
    "colorbar = plt.colorbar(scatter, ax=ax, label='Average SHAP Value')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_BubblePlot_3D_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 40)_20240110 1017_0.9354090690612793percent.model\n",
      "INFO:tensorflow:Assets written to: ./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 40)_20240110 1017_0.9354090690612793percent.model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 40)_20240110 1017_0.9354090690612793percent.model\\assets\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "currentDateTime = datetime.datetime.now() \n",
    "currentDateTime = currentDateTime.strftime(\"%Y%m%d %H%M\") \n",
    "modelPath = \"{}model02_trained_{}_{}_{}percent.model\".format(dataSetResultDirectory, dataSetName, currentDateTime, accuracy)\n",
    "print(modelPath)\n",
    "model2.save(modelPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/ShapeExplainer_20240110_1017_93.54percent.pkl\n",
      "./DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction/ShapeExplainerValues_20240110_1017_93.54percent.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "import datetime\n",
    "currentDateTime = datetime.datetime.now() \n",
    "currentDateTime = currentDateTime.strftime(\"%Y%m%d_%H%M\") \n",
    "explainerPath = \"{}ShapeExplainer_{}_{:.2f}percent.pkl\".format(dataSetResultDirectory, currentDateTime, (accuracy*100))\n",
    "print(explainerPath)\n",
    "\n",
    "explainerValuePath = \"{}ShapeExplainerValues_{}_{:.2f}percent.pkl\".format(dataSetResultDirectory, currentDateTime, (accuracy*100))\n",
    "print(explainerValuePath)\n",
    "  \n",
    "# Save the SHAP values to a file using pickle\n",
    "with open(explainerValuePath, 'wb') as shap_values_file:\n",
    "    pickle.dump(shap_values, shap_values_file)\n",
    "\n",
    "\n",
    "# Save the SHAP values to a file using pickle\n",
    "with open(explainerPath, 'wb') as explainer_file:\n",
    "    pickle.dump(shap_explainer, explainer_file)\n",
    "\n",
    " \n",
    "\n",
    "# To load the saved explainer and SHAP values later:\n",
    "# with open('shap_explainer.pkl', 'rb') as explainer_file:\n",
    "#     loaded_explainer = pickle.load(explainer_file)\n",
    "# \n",
    "# with open('shap_values.pkl', 'rb') as shap_values_file:\n",
    "#     loaded_shap_values = pickle.load(shap_values_file)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "12 #  swarmplot (Beeswarm plot)\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.swarmplot(data=df_top_10, palette=\"muted\", size=3) \n",
    "plt.title('MLP Model with SHAP (XAI) Beeswarm Plot')\n",
    "plt.ylabel('SHAP Values')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_Beeswarmplot_{}_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, method, dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}