SVC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1043e106",
   "metadata": {},
   "source": [
    "# C-Support Vector Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b186b534",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio.v3 as imageio\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "683f95c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get directories\n",
    "train_dir = \"C:/Users/bodin/School/MATH 5680/Projec/tumor_images/Training\"\n",
    "test_dir = \"C:/Users/bodin/School/MATH 5680/Projec/tumor_images/Testing\"\n",
    "\n",
    "labels = [\"glioma_tumor\",\"meningioma_tumor\",\"no_tumor\",\"pituitary_tumor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ea45daa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create list of data\n",
    "train_img_list = []\n",
    "test_img_list = []\n",
    "\n",
    "test_label_list = []\n",
    "train_label_list = []\n",
    "\n",
    "# Training dataset\n",
    "for label in labels:\n",
    "    for img_file in os.listdir(train_dir + '/' + label):\n",
    "        img = cv2.imread(train_dir + '/' + label + '/' + img_file)\n",
    "        img = cv2.resize(img, (200,200))\n",
    "        train_img_list.append(img)\n",
    "        train_label_list.append(label)\n",
    "\n",
    "# Testing dataset\n",
    "for label in labels:\n",
    "    for img_file in os.listdir(test_dir + '/' + label):\n",
    "        img = cv2.imread(test_dir + '/' + label + '/' + img_file)\n",
    "        img = cv2.resize(img, (200,200))\n",
    "        test_img_list.append(img)\n",
    "        test_label_list.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c64a4fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode labels\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "train_label_list = label_encoder.fit_transform(train_label_list)\n",
    "test_label_list = label_encoder.fit_transform(test_label_list)\n",
    "\n",
    "# Convert images to int64 array\n",
    "train_img_list = np.array(train_img_list).astype(np.int64)\n",
    "test_img_list = np.array(test_img_list).astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba1380f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    827\n",
       "0    826\n",
       "1    822\n",
       "2    395\n",
       "dtype: int64"
      ]
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     "execution_count": 5,
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   ],
   "source": [
    "# Value Counts for each type of tumor\n",
    "\"\"\"\n",
    "Pituitary = 3\n",
    "Glioma = 0\n",
    "Meningioma = 1\n",
    "None = 2\n",
    "\"\"\"\n",
    "pd.Series(train_label_list).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c705d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert images from (200, 200, 3) to column vector (120000)\n",
    "X_train = train_img_list.reshape(len(train_img_list), -1)\n",
    "X_test = test_img_list.reshape(len(test_img_list), -1)\n",
    "Y_train = train_label_list\n",
    "Y_test = test_label_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1df4ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize values between 0-1\n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dcb5352",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Not necessary in current state, may delete ###\n",
    "\n",
    "#from sklearn.decomposition import PCA\n",
    "#pca = PCA(.5)\n",
    "\n",
    "#pca_train = pca.fit_transform(X_train)\n",
    "#pca_test = pca.transform(X_test)\n",
    "\n",
    "#pca_train = X_train\n",
    "#pca_test = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da298a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nSVC parameters to alter:\\n\\nC: Regularization parameter. The strength of the regularization \\nis inversely proportional to C. Must be strictly positive. \\nThe penalty is a squared l2 penalty. [default=1.0]\\n\\nkernel: Specifies the kernel type to be used in the algorithm.\\n{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}\\n[default=rbf]\\n\\ndegree: Degree of the polynomial kernel function (‘poly’)\\n[default = 3]\\n\\ngamma: Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.\\n[default = 'scale']\\n\\ntol: Tolerance for stopping criterion.\\n[default = 1e-3]\\n\\n\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "\"\"\"\n",
    "SVC parameters to alter:\n",
    "\n",
    "C: Regularization parameter. The strength of the regularization \n",
    "is inversely proportional to C. Must be strictly positive. \n",
    "The penalty is a squared l2 penalty. [default=1.0]\n",
    "\n",
    "kernel: Specifies the kernel type to be used in the algorithm.\n",
    "{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}\n",
    "[default=rbf]\n",
    "\n",
    "degree: Degree of the polynomial kernel function (‘poly’)\n",
    "[default = 3]\n",
    "\n",
    "gamma: Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.\n",
    "[default = 'scale']\n",
    "\n",
    "tol: Tolerance for stopping criterion.\n",
    "[default = 1e-3]\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# Paramters to test\n",
    "param_grid = {'C': [0.1, 1, 10], \n",
    "              'kernel': ['linear', 'poly', 'rbf', 'sigmoid']}\n",
    "\n",
    "# Build model and search for best params\n",
    "sv = SVC(max_iter=100)\n",
    "grid_search = GridSearchCV(sv, param_grid, cv=5, scoring='accuracy', verbose=2)\n",
    "grid_search.fit(X_train, Y_train)\n",
    "\n",
    "# Print the best parameters and corresponding accuracy score\n",
    "print(\"Best Parameters: \", grid_search.best_params_)\n",
    "print(\"Best Accuracy Score: {:.4f}\".format(grid_search.best_score_))\n",
    "\n",
    "'''\n",
    "I ran the gridsearch for svc and got best results C=10 and kernel='rbf'.\n",
    "The output did not save & running it again would take another hour of waiting.\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27f34e9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bodin\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:299: ConvergenceWarning: Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "SVC(C=10, max_iter=500)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "sv = SVC(max_iter=500,\n",
    "        C=10,\n",
    "        kernel = 'rbf')\n",
    "sv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c8d8af8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7309644670050761\n"
     ]
    }
   ],
   "source": [
    "# Evaluation\n",
    "from sklearn.metrics import (accuracy_score,\n",
    "                             classification_report,\n",
    "                             confusion_matrix)\n",
    "\n",
    "y_pred = sv.predict(X_test)\n",
    "accuracy = accuracy_score(Y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "\n",
    "#print(\"Training Score: \", sv.score(X_train, Y_train))\n",
    "#print(\"testing Score: \", sv.score(X_test, Y_test))\n",
    "#print(f\"Confusion Matrix: \\n\", confusion_matrix(Y_test, y_pred), \"\\n\")\n",
    "#print(classification_report(Y_test, y_pred))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "62f411b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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