604 lines (604 with data), 205.4 kB

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Souhib-khalbous/Quantitative-Analysis-of-T2-Coronal-MRI-Data-for-Treatment-Efficiency-in-Uterine-Fibroids-/blob/master/KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Mount The Data**"
      ],
      "metadata": {
        "id": "J8MaQSw7MN-v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOxOwSxrMMbV",
        "outputId": "9076e900-7d05-44b5-df7d-edf35e7955f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Libraries**"
      ],
      "metadata": {
        "id": "HLnH9PUkWGmN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bB9mCUA_VeBc"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "# sklearn for ML algorithms, model evaluation, and data preprocessing\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "# Metrics for evaluating model performance\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, auc\n",
        "from sklearn.metrics import precision_recall_curve, average_precision_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Read the Dataset**"
      ],
      "metadata": {
        "id": "LCoTBnd9Wh3O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the columns you want to use as features\n",
        "feature_columns = ['LB', 'AC', 'FM', 'UC', 'DL', 'DS', 'DP', 'ASTV', 'MSTV', 'ALTV', 'MLTV', 'Width', 'Min', 'Max', 'Nmax', 'Nzeros', 'Mode', 'Mean', 'Median', 'Variance', 'Tendency']\n",
        "\n",
        "# Read the 'Raw Data' sheet of the Excel file, selecting only the specified columns plus the 'CLASS' column\n",
        "dataset = pd.read_excel('/content/drive/MyDrive/Cardiac ECG/CTG.xls', sheet_name='Raw Data', usecols=feature_columns + ['CLASS'])\n",
        "\n",
        "#remove any row that has at least one NaN value\n",
        "dataset = dataset.dropna()\n",
        "\n",
        "#Reset the index of the DataFrame and drop the old index\n",
        "dataset = dataset.reset_index(drop=True)\n",
        "\n",
        "\n",
        "print(len(dataset))\n",
        "print(dataset.head())\n",
        "\n",
        "# Now you have a DataFrame 'dataset' with only the features you're interested in and the 'CLASS' target variable\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7svqWnXQbZF",
        "outputId": "03c42844-74f3-4c1d-f8a8-04521f02ab88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2126\n",
            "      LB   AC   FM   UC  ASTV  MSTV  ALTV  MLTV   DL   DS  ...   Min    Max  \\\n",
            "0  120.0  0.0  0.0  0.0  73.0   0.5  43.0   2.4  0.0  0.0  ...  62.0  126.0   \n",
            "1  132.0  4.0  0.0  4.0  17.0   2.1   0.0  10.4  2.0  0.0  ...  68.0  198.0   \n",
            "2  133.0  2.0  0.0  5.0  16.0   2.1   0.0  13.4  2.0  0.0  ...  68.0  198.0   \n",
            "3  134.0  2.0  0.0  6.0  16.0   2.4   0.0  23.0  2.0  0.0  ...  53.0  170.0   \n",
            "4  132.0  4.0  0.0  5.0  16.0   2.4   0.0  19.9  0.0  0.0  ...  53.0  170.0   \n",
            "\n",
            "   Nmax  Nzeros   Mode   Mean  Median  Variance  Tendency  CLASS  \n",
            "0   2.0     0.0  120.0  137.0   121.0      73.0       1.0    9.0  \n",
            "1   6.0     1.0  141.0  136.0   140.0      12.0       0.0    6.0  \n",
            "2   5.0     1.0  141.0  135.0   138.0      13.0       0.0    6.0  \n",
            "3  11.0     0.0  137.0  134.0   137.0      13.0       1.0    6.0  \n",
            "4   9.0     0.0  137.0  136.0   138.0      11.0       1.0    2.0  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Splitting the Data**\n",
        "\n",
        "\n",
        "*   Define X, y\n",
        "*   Scale the Features (-1 < Features < +1)\n",
        "\n",
        "*   Split the Dataset into *Training* and *Testing* Method\n"
      ],
      "metadata": {
        "id": "EIgfW8D6bpYZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define your features and target variable\n",
        "X = dataset[feature_columns]\n",
        "y = dataset['CLASS']\n",
        "\n",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the dataset into a training set and a test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42, stratify=y)\n",
        "\n",
        "# Now, X_train & y_train are ready for training your model, and X_test & y_test are ready for evaluating it.\n"
      ],
      "metadata": {
        "id": "LdFIZOncEiJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.unique(y_test, return_counts=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dm62x7MXbdE2",
        "outputId": "76dc4635-2844-4d71-9119-16743652d30c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]), array([115, 174,  16,  24,  21, 100,  76,  32,  21,  59]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.shape)\n",
        "# Check the shape of the dataframes\n",
        "print(\"\\nShape of X_train:\", X_train.shape)\n",
        "print(\"\\nShape of y_train:\", y_train.shape)\n",
        "print(\"\\nShape of X_test:\", X_test.shape)\n",
        "print(\"\\nShape of y_test:\", y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_XhJaUHG-Pv",
        "outputId": "0fcfe105-81c5-48e3-9e6e-cfcbcb912ea2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2126, 22)\n",
            "\n",
            "Shape of X_train: (1488, 21)\n",
            "\n",
            "Shape of y_train: (1488,)\n",
            "\n",
            "Shape of X_test: (638, 21)\n",
            "\n",
            "Shape of y_test: (638,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature Scaling**\n",
        "\n",
        "***Rule:*** Any alogorithm that computes distance or assumes normality, **scale your features!**\n",
        "\n",
        "i.g: when we'll apply *KNN*\n",
        "\n",
        "**Remember:**\n",
        "*standard scalar* means:\n",
        "instead of the data to be for example from 5 to 7000 in one column, and from 2 to 3004 in the other column.\n",
        "We'll set the data to be between -1 and +1"
      ],
      "metadata": {
        "id": "DrDTiL2QKZfl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sc_X= StandardScaler() # we import the standard scalar for this variable sc_X\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "metadata": {
        "id": "__irzd9TISmJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  **KNN Algorithm**\n",
        "\n",
        "Defining the model using KNeighborsClassifier and fit the train data in the model"
      ],
      "metadata": {
        "id": "To36ZOW-NUed"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "math.sqrt(len(y_test))\n",
        "# we use this block to know what is the suitable value that we can use\n",
        "# for the value of K.\n",
        "\n",
        "#==> I got 20 (after running this cell). which is an Even number. And since in the voting we want something\n",
        "# odd. So, we'll use 20 - 1 = 19"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dc0DosxVOxia",
        "outputId": "401e2390-d6c4-4020-8318-5b39967bed27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.25866188063018"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#define the model: Init K-NN\n",
        "\n",
        "classifier = KNeighborsClassifier(n_neighbors = 11, p=2, metric= 'euclidean') # n_neighbors = K number\n",
        "\n",
        "#Fit Model\n",
        "classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "alHz2dNGNglA",
        "outputId": "ec48a7cf-0cf8-418c-dcce-ef3a0b9426e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(metric='euclidean', n_neighbors=11)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict the test set results\n",
        "y_pred= classifier.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNX8WCl0UkLt",
        "outputId": "70b4fa5e-a29c-49fc-ea48-45df9e0b3346"
      },
      "execution_count": null,
      "outputs": [
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              "        1.,  8.,  6.,  6.,  8.,  2., 10.,  2.,  1.,  6.,  4.,  1.,  1.,\n",
              "        6.,  2.,  2.,  2.,  2.,  1.,  2., 10.,  2.,  9., 10.,  6.,  8.,\n",
              "        1.,  6.,  9.,  7.,  2.,  2.,  1.,  6.,  6.,  1., 10.,  6.,  6.,\n",
              "        2.,  7.,  1.,  8.,  2.,  2., 10.,  2., 10.,  6.,  7., 10.,  7.,\n",
              "        2.,  7.,  2.,  2.,  2.,  6.,  2., 10.,  6.,  6.,  1.,  2.,  1.,\n",
              "        7.,  6.,  2.,  2.,  2.,  7.,  2.,  2.,  7.,  6.,  8., 10.,  2.,\n",
              "        6.,  1.,  1.,  7.,  6.,  2.,  6.,  2.,  7.,  1.,  7.,  1.,  2.,\n",
              "        2.,  2.,  7.,  6.,  2.,  7.,  6.,  2.,  1.,  1.,  6.,  2.,  6.,\n",
              "        1., 10.,  8., 10.,  1.,  1., 10.,  2.,  1.,  1.,  2.,  6.,  2.,\n",
              "        2.,  2.,  8.,  6.,  1.,  6.,  7.,  8.,  2.,  2., 10.,  1.,  2.,\n",
              "        2.,  2.,  2., 10.,  1.,  7.,  2.,  4.,  8.,  1.,  1.,  6.,  2.,\n",
              "        1.])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's Important to **evaluate** the model.\n",
        "Let's use the confusion matrix"
      ],
      "metadata": {
        "id": "L6QPG-DaT3Gz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate Model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "#F1_score = f1_score(y_test, y_pred)\n",
        "print(cm)\n",
        "#print(F1_score)\n",
        "\n",
        "\n",
        "# Calculate the accuracy\n",
        "accuracy = np.trace(cm) / np.sum(cm)\n",
        "accuracy_percent = accuracy * 100\n",
        "\n",
        "print(f'Accuracy of the model: {accuracy_percent:.2f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5T-5JoFT1U4",
        "outputId": "12d82e1a-1ee8-4d67-ce90-9dec3b073fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 89  14   1   0   1   0   3   0   0   7]\n",
            " [ 17 148   1   1   0   4   2   0   0   1]\n",
            " [  8   4   3   0   0   0   1   0   0   0]\n",
            " [  0  15   0   8   0   1   0   0   0   0]\n",
            " [ 10   6   0   0   1   0   0   0   0   4]\n",
            " [  0  11   0   0   0  80   7   2   0   0]\n",
            " [  4   0   1   0   0  18  50   2   0   1]\n",
            " [  0   0   0   0   0   2   3  27   0   0]\n",
            " [  4   0   0   0   0   0   0   0  10   7]\n",
            " [ 14   2   0   0   0   0   0   0   3  40]]\n",
            "Accuracy of the model: 71.47%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "6eC9LVnZJuH6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score, accuracy_score, roc_curve, auc\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from itertools import cycle\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Binarizing the output\n",
        "y_bin = label_binarize(y_test, classes=[i for i in range(1, 11)])\n",
        "n_classes = y_bin.shape[1]\n",
        "\n",
        "# Predict probabilities\n",
        "probas_ = classifier.predict_proba(X_test)\n",
        "\n",
        "# Compute ROC curve and ROC AUC for each class\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_bin[:, i], probas_[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# Compute micro-average ROC curve and ROC AUC\n",
        "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_bin.ravel(), probas_.ravel())\n",
        "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
        "\n",
        "# Compute macro-average ROC curve and ROC AUC\n",
        "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
        "mean_tpr = np.zeros_like(all_fpr)\n",
        "for i in range(n_classes):\n",
        "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
        "mean_tpr /= n_classes\n",
        "fpr[\"macro\"] = all_fpr\n",
        "tpr[\"macro\"] = mean_tpr\n",
        "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
        "\n",
        "# Plot all ROC curves\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
        "         label='Micro-average ROC curve (area = {0:0.2f})'\n",
        "               ''.format(roc_auc[\"micro\"]),\n",
        "         color='deeppink', linestyle=':', linewidth=4)\n",
        "\n",
        "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
        "         label='Macro-average ROC curve (area = {0:0.2f})'\n",
        "               ''.format(roc_auc[\"macro\"]),\n",
        "         color='navy', linestyle=':', linewidth=4)\n",
        "\n",
        "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=2,\n",
        "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
        "             ''.format(i+1, roc_auc[i]))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n",
        "# Accuracy\n",
        "accuracy = accuracy_score(y_test, classifier.predict(X_test))\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "1S77SOOGZwmU",
        "outputId": "917f3269-7988-4001-a927-fd86136defce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 71.47%\n"
          ]
        }
      ]
    }
  ]
}