Survey_VSDC_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "d6c3665b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import os\n",
    "from collections import deque\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score, classification_report, precision_recall_curve, average_precision_score \n",
    "#from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "import operator\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import openpyxl\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "59e17323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading Paths\n"
     ]
    }
   ],
   "source": [
    "print(\"Reading Paths\")\n",
    "feature='APGDF';\n",
    "train_dataset_real = '/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/'+ feature + '/training/0PR/'\n",
    "train_dataset_spoof = '/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/'+ feature + '/training/1PR/'\n",
    "test_dataset_real = '/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/'+ feature +'/testing/0PR/'\n",
    "test_dataset_spoof = '/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/'+ feature +'/testing/1PR/'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "a4b68a71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of real samples 1008\n",
      "length of spoof samples 3704\n",
      " Creating Feature Space \n",
      "(4712, 60)\n",
      " Reading & Creating training Features and Labels\n",
      "Total number of training real labels  1008\n",
      "Total number of training samples  4712\n",
      "Total number of training labels  4712\n"
     ]
    }
   ],
   "source": [
    "import glob, os\n",
    "\n",
    "train_name_r= []\n",
    "train_names_r= []\n",
    "train_labels= []\n",
    "train_names= []\n",
    "train_name_sp = []\n",
    "n_label_sp = []\n",
    "n_label_r = []\n",
    "train_names_sp = []\n",
    "\n",
    "\n",
    "os.chdir(\"/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/\"+ feature +\"/training/0PR/\")\n",
    "for file in sorted(glob.glob(\"*.xlsx\")):\n",
    "    n_label_r=file.split('.')\n",
    "    train_name_r=n_label_r[0]\n",
    "    train_names_r.append(train_name_r)\n",
    "    \n",
    "\n",
    "os.chdir(\"/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/\"+ feature +\"/training/1PR/\")\n",
    "for file in sorted(glob.glob(\"*.xlsx\")):\n",
    "    #print(file)\n",
    "    n_label_sp=file.split('.')\n",
    "    train_name_sp=n_label_sp[0]\n",
    "    train_names_sp.append(train_name_sp)\n",
    "    \n",
    "    \n",
    "print('length of real samples', len(train_names_r))\n",
    "print('length of spoof samples', len(train_names_sp))\n",
    "\n",
    "totallength = len(train_names_r) + len(train_names_sp)\n",
    "\n",
    "    \n",
    "print(\" Creating Feature Space \")\n",
    "train_features_space = np.zeros((totallength, 60), dtype='float')\n",
    "print(train_features_space.shape)\n",
    "\n",
    "train_features_sp = []*totallength\n",
    "\n",
    "print(\" Reading & Creating training Features and Labels\")\n",
    "try:\n",
    "    for i in range(len(train_names_r)):\n",
    "        #print(train_dataset_real+train_names_r[i]+'.xlsx')\n",
    "        tr_features = pd.read_excel(train_dataset_real+train_names_r[i]+'.xlsx', engine='openpyxl', header=None)\n",
    "        tr_features = tr_features.to_numpy()\n",
    "        train_features_space[i]=tr_features \n",
    "        train_labels.append(0) # 0=real\n",
    "except ValueError:\n",
    "    print('training real file containing issues: ')\n",
    "    print(train_dataset_real+train_names_r[i]+'.xlsx')\n",
    "    raise Exception \n",
    "    pass\n",
    "\n",
    "change_loop = i + 1\n",
    "print('Total number of training real labels ', len(train_labels))\n",
    "\n",
    "try:\n",
    "    for i in range(len(train_names_sp)):\n",
    "        #print(train_dataset_spoof+train_names_sp[i]+'.xlsx')\n",
    "        ts_features = pd.read_excel(train_dataset_spoof+train_names_sp[i]+'.xlsx', engine='openpyxl', header=None)\n",
    "        ts_features = ts_features.to_numpy()\n",
    "        train_features_space[change_loop+i]=ts_features \n",
    "        train_labels.append(1) # 1=spoof\n",
    "except ValueError: \n",
    "        print('training spoof file containing issues: ')\n",
    "        print(train_dataset_spoof+train_names_sp[i]+'.xlsx')\n",
    "        pass\n",
    "\n",
    "\n",
    "print('Total number of training samples ',len(train_features_space))\n",
    "print('Total number of training labels ', len(train_labels))\n",
    "# print('Training feature shape',train_features_sp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "e4d18f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Total number of training samples ',len(train_features_space))\n",
    "# print(train_features_space[4711])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "297f78c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing testing features and labels\n",
      "length of real samples 678\n",
      "length of spoof samples 2474\n",
      " Creating Feature Space \n",
      "(3152, 60)\n",
      " Reading & Creating training Features and Labels\n",
      "Total number of training samples  3152\n",
      "Total number of training labels  3152\n"
     ]
    }
   ],
   "source": [
    "print('Preparing testing features and labels')\n",
    "\n",
    "import glob, os\n",
    "\n",
    "test_name_r= []\n",
    "test_names_r= []\n",
    "test_labels= []\n",
    "test_names= []\n",
    "test_name_sp = []\n",
    "n_label_sp = []\n",
    "n_label_r = []\n",
    "test_names_sp = []\n",
    "\n",
    "os.chdir(\"/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/\"+ feature +\"/testing/0PR/\")\n",
    "for file in glob.glob(\"*.xlsx\"):\n",
    "    n_label_r=file.split('.')\n",
    "    test_name_r=n_label_r[0]\n",
    "    #print(test_name_r)\n",
    "    test_names_r.append(test_name_r)\n",
    "   \n",
    "\n",
    "os.chdir(\"/scratch/projects/smiles/Multi_Features/Awais_new_vsdc/VSDC/\"+ feature +\"/testing/1PR/\")\n",
    "for file in glob.glob(\"*.xlsx\"):\n",
    "    n_label_sp=file.split('.')\n",
    "    test_name_sp=n_label_sp[0]\n",
    "    test_names_sp.append(test_name_sp)\n",
    "    \n",
    "    \n",
    "print('length of real samples', len(test_names_r))\n",
    "print('length of spoof samples', len(test_names_sp))    \n",
    "\n",
    "totallength1 = len(test_names_r) + len(test_names_sp)\n",
    "test_features_sp = []*totallength1\n",
    "\n",
    "print(\" Creating Feature Space \")\n",
    "test_features_space = np.zeros((totallength1, 60), dtype='float')\n",
    "print(test_features_space.shape)\n",
    "\n",
    "print(\" Reading & Creating training Features and Labels\")\n",
    "try:\n",
    "    for i in range(len(test_names_r)):\n",
    "        t_features = pd.read_excel(test_dataset_real+test_names_r[i]+'.xlsx', engine='openpyxl', header=None)\n",
    "        t_features = t_features.to_numpy()\n",
    "        test_features_space[i]=t_features \n",
    "        test_labels.append(0)\n",
    "except ValueError:\n",
    "    print('training real file containing issues: ')\n",
    "    print(test_dataset_real+test_names_r[i]+'.xlsx')\n",
    "    pass\n",
    "\n",
    "change_loop=i+1\n",
    "\n",
    "try:\n",
    "    for i in range(len(test_names_sp)):\n",
    "#       len(train_names)\n",
    "#       print(train_dataset_path+train_names[i]+'.xlsx')\n",
    "        ts_features = pd.read_excel(test_dataset_spoof+test_names_sp[i]+'.xlsx', engine='openpyxl', header=None)\n",
    "        ts_features = ts_features.to_numpy()\n",
    "        test_features_space[change_loop+i]=ts_features \n",
    "        test_labels.append(1)\n",
    "except ValueError:\n",
    "    print('testing spoof file containing  issues: ')\n",
    "    print(test_dataset_spoof+test_names_sp[i]+'.xlsx')\n",
    "    pass\n",
    "        \n",
    "        \n",
    "print('Total number of training samples ',len(test_features_space))\n",
    "print('Total number of training labels ', len(test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "cc7e751d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(test_features_space[3151])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "5d439f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4712,)\n",
      "(4712, 60)\n",
      "(3152,)\n",
      "(3152, 60)\n",
      "setting train test Data\n"
     ]
    }
   ],
   "source": [
    "training_labels = np.array(train_labels)\n",
    "Evaluation_labels = np.array(test_labels)\n",
    "training_features = np.array(train_features_space)\n",
    "Evaluation_features = np.array(test_features_space)\n",
    "print(training_labels.shape)\n",
    "print(training_features.shape)\n",
    "print(Evaluation_labels.shape)\n",
    "print(Evaluation_features.shape)\n",
    "print(\"setting train test Data\")\n",
    "#X_train, X_test, y_train, y_test = train_test_split(train_features_space, train_labels, train_size = 0.70)\n",
    "X_train=training_features\n",
    "X_test=Evaluation_features\n",
    "y_train=training_labels\n",
    "y_test=Evaluation_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "88a0179e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy of SVM(linear) classifier on test set: 0.89\n",
      "F1-score of SVM(linear) classifier on test set: 0.93\n",
      "Precision of SVM(linear) classifier on test set: 0.89\n",
      "Recall of SVM(linear) classifier on test set: 0.98\n",
      "\n",
      "\n",
      "Confusion Matrix: \n",
      " [[ 383  295]\n",
      " [  50 2424]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_svm = SVC(kernel=\"linear\",probability=True)\n",
    "model_svm.fit(X_train, y_train)\n",
    "y_pred_svm = model_svm.predict(X_test)\n",
    "# probs_svm = model_svm.predict_proba(X_test)\n",
    "# preds_svm = probs_svm[:,1]\n",
    "# fpr_svm, tpr_svm, threshold = metrics.roc_curve(y_test, preds_svm)\n",
    "# roc_auc_svm = metrics.auc(fpr_svm, tpr_svm)\n",
    "print('\\nAccuracy of SVM(linear) classifier on test set: {:.2f}'.format(model_svm.score(X_test, y_test)))\n",
    "print(\"F1-score of SVM(linear) classifier on test set: {:.2f}\".format(metrics.f1_score(y_test, y_pred_svm)))\n",
    "print(\"Precision of SVM(linear) classifier on test set: {:.2f}\".format(metrics.precision_score( y_test,y_pred_svm)))\n",
    "print(\"Recall of SVM(linear) classifier on test set: {:.2f}\\n\".format(metrics.recall_score(y_test,y_pred_svm)))\n",
    "\n",
    "\n",
    "sns.set(font_scale=1.0)\n",
    "conf_mtx_nb_svm = metrics.confusion_matrix(y_test, y_pred_svm)\n",
    "sns.heatmap(conf_mtx_nb_svm, annot=True, fmt='g')\n",
    "print('\\nConfusion Matrix: \\n', conf_mtx_nb_svm )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "8b7006dd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix: \n",
      " [[ 383  295]\n",
      " [  50 2424]]\n",
      "classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.56      0.69       678\n",
      "           1       0.89      0.98      0.93      2474\n",
      "\n",
      "    accuracy                           0.89      3152\n",
      "   macro avg       0.89      0.77      0.81      3152\n",
      "weighted avg       0.89      0.89      0.88      3152\n",
      "\n",
      "classification Report END\n",
      "Hand-crafted Features:  APGDF\n",
      "Score Report Start\n",
      "EER: 0.4351032448377581\n",
      "accuracy: 0.8905456852791879\n",
      "precision: [0.88452656 0.89150423]\n",
      "recall: [0.56489676 0.97978981]\n",
      "fscore: [0.68946895 0.93356441]\n",
      "support: [ 678 2474]\n",
      "Score Report End\n"
     ]
    }
   ],
   "source": [
    "sns.set(font_scale=1.0)\n",
    "conf_mtx_nb_svm = metrics.confusion_matrix(y_test, y_pred_svm)\n",
    "#sns.heatmap(conf_mtx_nb_svm, annot=True, fmt='g')\n",
    "print('\\nConfusion Matrix: \\n', conf_mtx_nb_svm )\n",
    "\n",
    "\n",
    "print('classification Report')\n",
    "report=classification_report(y_test, y_pred_svm)\n",
    "print(report)\n",
    "\n",
    "accuracy=accuracy_score(y_test, y_pred_svm)\n",
    "precision, recall, fscore, support = score(y_test, y_pred_svm)\n",
    "print('classification Report END')\n",
    "\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_svm)\n",
    "fnr = 1 - tpr\n",
    "eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]\n",
    "EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))]\n",
    "\n",
    "print('Hand-crafted Features: ',feature)\n",
    "print('Score Report Start')\n",
    "print('EER: {}'.format(EER))\n",
    "print('accuracy: {}'.format(accuracy))\n",
    "print('precision: {}'.format(precision))\n",
    "print('recall: {}'.format(recall))\n",
    "print('fscore: {}'.format(fscore))\n",
    "print('support: {}'.format(support))\n",
    "print('Score Report End')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489d8be6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aec11ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20b603da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}