heartDisease_MLP After XAI Feature Selection - Experiment 1 .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, 51)\n",
      "Column Headings: Index(['SEQN', 'Gender', 'Age', 'Annual-Family-Income',\n",
      "       'Ratio-Family-Income-Poverty', 'X60-sec-pulse', 'Systolic', 'Diastolic',\n",
      "       'Weight', 'Height', 'Body-Mass-Index', 'White-Blood-Cells',\n",
      "       'Lymphocyte', 'Monocyte', 'Eosinophils', 'Basophils', 'Red-Blood-Cells',\n",
      "       'Hemoglobin', 'Mean-Cell-Vol', 'Mean-Cell-Hgb-Conc.',\n",
      "       'Mean-cell-Hemoglobin', 'Platelet-count', 'Mean-Platelet-Vol',\n",
      "       'Segmented-Neutrophils', 'Hematocrit', 'Red-Cell-Distribution-Width',\n",
      "       'Albumin', 'ALP', 'AST', 'ALT', 'Cholesterol', 'Creatinine', 'Glucose',\n",
      "       'GGT', 'Iron', 'LDH', 'Phosphorus', 'Bilirubin', 'Protein', 'Uric.Acid',\n",
      "       'Triglycerides', 'Total-Cholesterol', 'HDL', 'Glycohemoglobin',\n",
      "       'Vigorous-work', 'Moderate-work', 'Health-Insurance', 'Diabetes',\n",
      "       'Blood-Rel-Diabetes', 'Blood-Rel-Stroke', '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, 51)\n",
      "Shape of fileData End: (37079, 51)\n",
      "\n",
      "\n",
      "columns of x:: 20 \n",
      "\n",
      " and features of X: Index(['Triglycerides', 'Blood-Rel-Diabetes', 'Gender', 'Blood-Rel-Stroke',\n",
      "       'Cholesterol', 'Body-Mass-Index', 'Diabetes', 'Diastolic', 'Basophils',\n",
      "       'Eosinophils', 'Height', 'Albumin', 'Phosphorus', 'Total-Cholesterol',\n",
      "       'LDH', 'Weight', 'Protein', 'White-Blood-Cells', 'AST', 'Uric.Acid'],\n",
      "      dtype='object')\n",
      "Shape of fileData: (37079, 51) , 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",
      "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",
      "\n",
      "\n",
      "\n",
      " ------------------------------------------------------------\n",
      " Directory Path: ./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/ \n",
      " ------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "dataSetIndex = 4\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",
    "\n",
    "cols = ['Triglycerides','Blood-Rel-Diabetes','Gender','Blood-Rel-Stroke','Cholesterol','Body-Mass-Index','Diabetes','Diastolic','Basophils','Eosinophils','Height','Albumin','Phosphorus','Total-Cholesterol','LDH','Weight','Protein','White-Blood-Cells','AST','Uric.Acid']\n",
    "\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_After_XAI_Features_Selection_Experiment_1_\" + dataSetName)\n",
    "dataSetResultDirectory += \"/\"\n",
    "if not os.path.isdir(dataSetResultDirectory):\n",
    "    os.makedirs(dataSetResultDirectory)\n",
    "\n",
    "dataSetName += \"_{}\".format(fileData.shape)\n",
    "\n",
    "\n",
    "\n",
    "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",
    "print(\"Target Column Name:: {} \\n\".format(fileData.__dataframe__().column_names()[-1]))\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "smote = SMOTE(random_state=42)\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",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(\"\\n X Train: Shape:: {}\".format(X_train.shape))\n",
    "print(\" X Test: Shape:: {}\".format(X_test.shape))   \n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_train:\n",
    "    if(i == 0): negativeClass += 1\n",
    "    if(i == 1): 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",
    "print(\"\\n\\n\") \n",
    "positiveClass =  0\n",
    "negativeClass = 0\n",
    "for i in y_test:\n",
    "    if(i == 0): negativeClass += 1\n",
    "    if(i == 1): 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",
    "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",
    "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",
    "#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",
    "# 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",
    "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",
    "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",
    "print(\"features shape: {}   and dType: {}\".format(features.shape, features.dtype))\n",
    "print(\"target shape: {}   and dType: {}\".format(target.shape, target.dtype)) \n",
    "\n",
    "print(\"\\n\\n\\n ------------------------------------------------------------\")\n",
    "print(\" Directory Path: {} \".format(dataSetResultDirectory))\n",
    "print(\" ------------------------------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 [==============================] - 12s 4ms/step - loss: 0.4277 - accuracy: 0.8049 - recall: 0.8200 - precision: 0.7959 - f1_score: 0.6667 - auc: 0.8845\n",
      "Epoch 2/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3727 - accuracy: 0.8378 - recall: 0.8557 - precision: 0.8262 - f1_score: 0.6667 - auc: 0.9134\n",
      "Epoch 3/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.3536 - accuracy: 0.8471 - recall: 0.8667 - precision: 0.8340 - f1_score: 0.6667 - auc: 0.9222\n",
      "Epoch 4/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3396 - accuracy: 0.8554 - recall: 0.8831 - precision: 0.8368 - f1_score: 0.6667 - auc: 0.9279\n",
      "Epoch 5/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3291 - accuracy: 0.8618 - recall: 0.8897 - precision: 0.8427 - f1_score: 0.6667 - auc: 0.9321\n",
      "Epoch 6/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3189 - accuracy: 0.8669 - recall: 0.8964 - precision: 0.8464 - f1_score: 0.6667 - auc: 0.9359\n",
      "Epoch 7/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3108 - accuracy: 0.8712 - recall: 0.9024 - precision: 0.8495 - f1_score: 0.6667 - auc: 0.9389\n",
      "Epoch 8/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.3051 - accuracy: 0.8741 - recall: 0.9056 - precision: 0.8518 - f1_score: 0.6667 - auc: 0.9406\n",
      "Epoch 9/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2983 - accuracy: 0.8777 - recall: 0.9107 - precision: 0.8543 - f1_score: 0.6667 - auc: 0.9428\n",
      "Epoch 10/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2956 - accuracy: 0.8781 - recall: 0.9114 - precision: 0.8545 - f1_score: 0.6667 - auc: 0.9440\n",
      "Epoch 11/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2909 - accuracy: 0.8821 - recall: 0.9156 - precision: 0.8582 - f1_score: 0.6667 - auc: 0.9455\n",
      "Epoch 12/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2880 - accuracy: 0.8837 - recall: 0.9171 - precision: 0.8596 - f1_score: 0.6667 - auc: 0.9464\n",
      "Epoch 13/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2826 - accuracy: 0.8848 - recall: 0.9174 - precision: 0.8612 - f1_score: 0.6667 - auc: 0.9483\n",
      "Epoch 14/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2798 - accuracy: 0.8876 - recall: 0.9209 - precision: 0.8633 - f1_score: 0.6667 - auc: 0.9489\n",
      "Epoch 15/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2779 - accuracy: 0.8900 - recall: 0.9220 - precision: 0.8666 - f1_score: 0.6667 - auc: 0.9496\n",
      "Epoch 16/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2736 - accuracy: 0.8902 - recall: 0.9256 - precision: 0.8644 - f1_score: 0.6667 - auc: 0.9507\n",
      "Epoch 17/40\n",
      "2277/2277 [==============================] - 8s 3ms/step - loss: 0.2710 - accuracy: 0.8937 - recall: 0.9288 - precision: 0.8678 - f1_score: 0.6667 - auc: 0.9515\n",
      "Epoch 18/40\n",
      "2277/2277 [==============================] - 8s 4ms/step - loss: 0.2679 - accuracy: 0.8922 - recall: 0.9261 - precision: 0.8673 - f1_score: 0.6667 - auc: 0.9528\n",
      "Epoch 19/40\n",
      "2277/2277 [==============================] - 9s 4ms/step - loss: 0.2666 - accuracy: 0.8933 - recall: 0.9275 - precision: 0.8681 - f1_score: 0.6667 - auc: 0.9530\n",
      "Epoch 20/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2633 - accuracy: 0.8945 - recall: 0.9279 - precision: 0.8698 - f1_score: 0.6667 - auc: 0.9542\n",
      "Epoch 21/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2627 - accuracy: 0.8961 - recall: 0.9290 - precision: 0.8716 - f1_score: 0.6667 - auc: 0.9543\n",
      "Epoch 22/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2604 - accuracy: 0.8972 - recall: 0.9312 - precision: 0.8719 - f1_score: 0.6667 - auc: 0.9547\n",
      "Epoch 23/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2586 - accuracy: 0.8971 - recall: 0.9292 - precision: 0.8732 - f1_score: 0.6667 - auc: 0.9554\n",
      "Epoch 24/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2550 - accuracy: 0.8997 - recall: 0.9316 - precision: 0.8758 - f1_score: 0.6667 - auc: 0.9563\n",
      "Epoch 25/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2537 - accuracy: 0.9009 - recall: 0.9335 - precision: 0.8762 - f1_score: 0.6667 - auc: 0.9566\n",
      "Epoch 26/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2540 - accuracy: 0.9010 - recall: 0.9321 - precision: 0.8776 - f1_score: 0.6667 - auc: 0.9567\n",
      "Epoch 27/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2496 - accuracy: 0.9023 - recall: 0.9340 - precision: 0.8783 - f1_score: 0.6667 - auc: 0.9577\n",
      "Epoch 28/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2497 - accuracy: 0.9024 - recall: 0.9327 - precision: 0.8795 - f1_score: 0.6667 - auc: 0.9578\n",
      "Epoch 29/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2497 - accuracy: 0.9025 - recall: 0.9357 - precision: 0.8774 - f1_score: 0.6667 - auc: 0.9580\n",
      "Epoch 30/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2471 - accuracy: 0.9040 - recall: 0.9365 - precision: 0.8794 - f1_score: 0.6667 - auc: 0.9586\n",
      "Epoch 31/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2490 - accuracy: 0.9035 - recall: 0.9353 - precision: 0.8795 - f1_score: 0.6667 - auc: 0.9580\n",
      "Epoch 32/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2461 - accuracy: 0.9045 - recall: 0.9337 - precision: 0.8823 - f1_score: 0.6667 - auc: 0.9589\n",
      "Epoch 33/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2430 - accuracy: 0.9063 - recall: 0.9383 - precision: 0.8819 - f1_score: 0.6667 - auc: 0.9598\n",
      "Epoch 34/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2431 - accuracy: 0.9055 - recall: 0.9370 - precision: 0.8814 - f1_score: 0.6667 - auc: 0.9597\n",
      "Epoch 35/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2431 - accuracy: 0.9056 - recall: 0.9364 - precision: 0.8821 - f1_score: 0.6667 - auc: 0.9598\n",
      "Epoch 36/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2418 - accuracy: 0.9069 - recall: 0.9391 - precision: 0.8823 - f1_score: 0.6667 - auc: 0.9601\n",
      "Epoch 37/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2400 - accuracy: 0.9068 - recall: 0.9360 - precision: 0.8844 - f1_score: 0.6667 - auc: 0.9610\n",
      "Epoch 38/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2384 - accuracy: 0.9083 - recall: 0.9373 - precision: 0.8859 - f1_score: 0.6667 - auc: 0.9610\n",
      "Epoch 39/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2379 - accuracy: 0.9082 - recall: 0.9386 - precision: 0.8848 - f1_score: 0.6667 - auc: 0.9611\n",
      "Epoch 40/40\n",
      "2277/2277 [==============================] - 7s 3ms/step - loss: 0.2378 - accuracy: 0.9097 - recall: 0.9395 - precision: 0.8866 - f1_score: 0.6667 - auc: 0.9611\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445/445 [==============================] - 2s 3ms/step - loss: 0.2432 - accuracy: 0.9140 - recall: 0.9647 - precision: 0.8758 - f1_score: 0.6670 - auc: 0.9641\n",
      "Test loss: 24.32348281145096\n",
      "Test accuracy: 91.39724373817444\n",
      "Test recall: 96.4717447757721\n",
      "Test precision: 87.58295178413391\n",
      "Test F1 score: [66.70106]\n",
      "Test AUC: 96.40672206878662\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445/445 [==============================] - 1s 2ms/step\n",
      "MLP with Attention layer Confusion Matrix saved:: path: ./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/Model_Evaluation_MLP with Attention layer_CardiacPrediction_(37079, 51)_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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PermutationExplainer explainer: 996it [02:33,  6.28it/s]                         \n"
     ]
    }
   ],
   "source": [
    "shap_values = shap_explainer.shap_values(testForShap)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n",
      "Top SHAP values:\n",
      "0, 1, Blood-Rel-Diabetes, 0.008166778183568245\n",
      "1, 0, Triglycerides, 0.005776828574968382\n",
      "2, 4, Cholesterol, 0.005277468538865546\n",
      "3, 2, Gender, 0.003938773185500308\n",
      "4, 15, Weight, 0.00252259271741176\n",
      "5, 16, Protein, 0.0024671625528747496\n",
      "6, 6, Diabetes, 0.0024228451920529684\n",
      "7, 5, Body-Mass-Index, 0.0022356489313952073\n",
      "8, 19, Uric.Acid, 0.0017803594974050128\n",
      "9, 12, Phosphorus, 0.0015063073817826228\n",
      "10, 8, Basophils, 0.0010351948012181768\n",
      "11, 7, Diastolic, 0.0005178837153181593\n",
      "12, 10, Height, 0.00031405942486609076\n",
      "13, 14, LDH, -8.675959575458243e-05\n",
      "14, 17, White-Blood-Cells, -0.0008389748293993662\n",
      "15, 3, Blood-Rel-Stroke, -0.0012404857157780266\n",
      "16, 13, Total-Cholesterol, -0.002245674389531239\n",
      "17, 11, Albumin, -0.0022728389716955726\n",
      "18, 9, Eosinophils, -0.0029172022304098186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "\n",
    "avg_shap_values2 = {}\n",
    "\n",
    "howManyFeatures = 15\n",
    "# Calculate feature importance based on the mean absolute SHAP values\n",
    "feature_importance = (shap_values).mean(axis=0)\n",
    "top_features_indices_Sorted = np.argsort(feature_importance)[::-1] \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_Sorted]\n",
    "top_features = testForShap[:, top_features_indices_Sorted]\n",
    "top_shap_values = shap_values[:, top_features_indices_Sorted] \n",
    "\n",
    "print(\"\\n\\n--------------------------------------------------\")\n",
    "print(\"--------------------------------------------------\")\n",
    "print(\"Top SHAP values:\")\n",
    "for i in range(len(top_features_indices_Sorted)-1):\n",
    "    feature_index = top_features_indices_Sorted[i]\n",
    "    feature_name = feature_names[feature_index]\n",
    "    shap_value = (top_shap_values[:, i]).mean(axis=0) #np.mean(np.abs(shap_values[:, i]))\n",
    "    avg_shap_values2[i]= (shap_value * 100.0)\n",
    "    print(f\"{i}, {feature_index}, {feature_name}, {shap_value }\")\n",
    "    #print(f\"{feature_name}, { shap_value}\")\n",
    " \n",
    "top_10_shap_values = shap_values[:, top_features_indices] \n",
    "top_10_feature_names = X.columns[top_features_indices]\n",
    "# Create a DataFrame for visualization\n",
    "\n",
    "\n",
    "df_top_10 = pd.DataFrame(data=top_10_shap_values, columns=top_10_feature_names)\n",
    "\n",
    "shap.summary_plot(top_10_shap_values, testForShap[:, top_features_indices], feature_names=top_10_feature_names, 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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Sorted Average SHAP values:\n",
      "Blood-Rel-Diabetes, 0.008166778183568242\n",
      "Triglycerides, 0.005776828574968384\n",
      "Cholesterol, 0.00527746853886555\n",
      "Gender, 0.0039387731855003065\n",
      "Weight, 0.0025225927174117604\n",
      "Protein, 0.0024671625528747522\n",
      "Diabetes, 0.002422845192052967\n",
      "Body-Mass-Index, 0.002235648931395202\n",
      "Uric.Acid, 0.0017803594974050135\n",
      "Phosphorus, 0.0015063073817826213\n",
      "Basophils, 0.0010351948012181753\n",
      "Diastolic, 0.0005178837153181591\n",
      "Height, 0.0003140594248660904\n",
      "LDH, -8.675959575458239e-05\n",
      "White-Blood-Cells, -0.000838974829399367\n",
      "Blood-Rel-Stroke, -0.0012404857157780264\n",
      "Total-Cholesterol, -0.0022456743895312393\n",
      "Albumin, -0.0022728389716955735\n",
      "Eosinophils, -0.0029172022304098225\n",
      "AST, -0.003054784750673498\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_shap_values = np.mean(shap_values, axis=0)\n",
    "# Get indices that would sort the average SHAP values\n",
    "sorted_Average_indices = np.argsort(avg_shap_values)[::-1]\n",
    "# Arrange feature names and average SHAP values in ascending order\n",
    "sorted_feature_names = [feature_names[i] for i in sorted_Average_indices]\n",
    "sorted_avg_shap_values = avg_shap_values[sorted_Average_indices]\n",
    "# Print sorted feature names and average SHAP values\n",
    "print(\"\\n\\nSorted Average SHAP values:\")\n",
    "for i in range(len(sorted_feature_names)):\n",
    "    print(f\"{sorted_feature_names[i]}, {sorted_avg_shap_values[i]}\")\n",
    "\n",
    " \n",
    "\n",
    "shap.summary_plot(top_10_shap_values, testForShap[:, top_features_indices], feature_names=top_10_feature_names, plot_type=\"bar\", show=False)\n",
    "\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",
    "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",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_{}_Sorted_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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": [
    "# Plot the summary Bar\n",
    "shap.summary_plot(top_10_shap_values, testForShap[:, top_features_indices], feature_names=top_10_feature_names, 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",
    "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",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_Bar_{}_Sorted_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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": [
    "shap.summary_plot(top_shap_values, testForShap[:, top_features_indices], feature_names=featureNamesSHAP, plot_type=\"violin\", show=False, plot_size=(12, 6))\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_Violin_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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": [
    "shap.summary_plot(top_shap_values, testForShap[:, top_features_indices], feature_names=featureNamesSHAP, plot_type=\"layered_violin\", show=False )\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_layered_violin_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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": [
    "import matplotlib.pyplot as plt\n",
    "\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, 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": [
    "# Set the x-axis range\n",
    "x_axis_range = (-0.25, 0.25)  # Adjust the range as needed\n",
    "# Plotting with Seaborn's kdeplot\n",
    "plt.figure(figsize=(15, 6))\n",
    "for feature in top_10_feature_names:\n",
    "    sns.kdeplot(data=df_top_10[feature], label=feature, common_norm=False, common_grid=True, fill=True, clip=x_axis_range)\n",
    " \n",
    "plt.title('MLP Model with SHAP (XAI) KDE Plot')\n",
    "plt.xlabel('SHAP Values')\n",
    "plt.ylabel('Density')\n",
    "plt.legend()\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_KDEplot_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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": [
    "# Set the x-axis range\n",
    "x_axis_range = (-0.3, 0.3)  # Adjust the range as needed\n",
    "# Plotting with Seaborn's kdeplot\n",
    "plt.figure(figsize=(15, 6))\n",
    "for feature in top_10_feature_names:\n",
    "    sns.kdeplot(data=df_top_10[feature], label=feature, common_norm=True, common_grid=True, fill=True, clip=x_axis_range)\n",
    " \n",
    "plt.title('MLP Model with SHAP (XAI) KDE Plot')\n",
    "plt.xlabel('SHAP Values')\n",
    "plt.ylabel('Density')\n",
    "plt.legend()\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_KDEplot_2_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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, 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": [
    "22sfsdf\n",
    "# Plotting a boxplot for the top 10 features\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=df_top_10, orient='v', palette='Set2')\n",
    "\n",
    "\n",
    "plt.title('MLP Model with SHAP (XAI) Box Plot')\n",
    "plt.xlabel('SHAP Values')\n",
    "plt.ylabel('Features')\n",
    "plt.xticks(range(len(top_10_feature_names)), top_10_feature_names, rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_BoxPlot_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory, 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,  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=1500 * 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, 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 with Seaborn's swarmplot (Beeswarm plot) \n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.stripplot(data=df_top_10, palette=\"muted\", size=3, jitter=True)  # Use jitter=True for strip plot\n",
    "\n",
    "plt.title('MLP Model with SHAP (XAI) Strip Plot')\n",
    "plt.ylabel('SHAP Values')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "picturePath = \"{}XAI_SHAP_Explainer_SNS_Stripplot_{}_numberOfSamples_{}.png\".format(dataSetResultDirectory,  dataSetName, len(testForShap))\n",
    "plt.savefig(picturePath,  dpi=300, bbox_inches='tight')\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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 51)_20240111_1214_0.9139724373817444percent.model\n",
      "INFO:tensorflow:Assets written to: ./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 51)_20240111_1214_0.9139724373817444percent.model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/model02_trained_CardiacPrediction_(37079, 51)_20240111_1214_0.9139724373817444percent.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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/ShapeExplainer_20240111_1214_91.40percent.pkl\n",
      "./DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction/ShapeExplainerValues_20240111_1214_91.40percent.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": [
    "#  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": []
  }
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