gangrene_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UWuDr-_0vtPS"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awDYKoYzxwDp",
        "outputId": "e8893b7f-adb2-43e7-e31c-027054c2d221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "def total_files(folder_path):\n",
        "    num_files = len([f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))])\n",
        "    return num_files\n",
        "\n",
        "train_dry_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/dry_gangrene\"\n",
        "train_wet_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/wet_gangrene\"\n",
        "train_gas_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/gas_gangrene\"\n",
        "train_normal_foot = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/normal_foot\"\n",
        "\n",
        "test_dry_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/dry_gangrene\"\n",
        "test_wet_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/wet_gangrene\"\n",
        "test_gas_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/gas_gangrene\"\n",
        "test_normal_foot = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/normal_foot\"\n",
        "\n",
        "valid_dry_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid/dry_gangrene\"\n",
        "valid_wet_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid/wet_gangrene\"\n",
        "valid_gas_gangrene = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid/gas_gangrene\"\n",
        "valid_normal_foot = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid/normal_foot\"\n",
        "\n",
        "\n",
        "print(\"Number of dry_gangrene images in training set\", total_files(train_dry_gangrene))\n",
        "print(\"Number of wet_gangrene images in training set\", total_files(train_wet_gangrene))\n",
        "print(\"Number of gas_gangrene images in training set\", total_files(train_gas_gangrene))\n",
        "print(\"Number of normal_foot images in training set\", total_files(train_normal_foot))\n",
        "\n",
        "print(\"========================================================\")\n",
        "\n",
        "print(\"Number of dry_gangrene images in test set\", total_files(test_dry_gangrene))\n",
        "print(\"Number of wet_gangrene images in test set\", total_files(test_wet_gangrene))\n",
        "print(\"Number of gas_gangrene images in test set\", total_files(test_gas_gangrene))\n",
        "print(\"Number of normal_foot images in test set\", total_files(test_normal_foot))\n",
        "\n",
        "print(\"========================================================\")\n",
        "\n",
        "print(\"Number of dry_gangrene images in validation set\", total_files(valid_dry_gangrene))\n",
        "print(\"Number of wet_gangrene images in validation set\", total_files(valid_wet_gangrene))\n",
        "print(\"Number of gas_gangrene images in validation set\", total_files(valid_gas_gangrene))\n",
        "print(\"Number of normal_foot images in validation set\", total_files(valid_normal_foot))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzY-qoI7x-wQ",
        "outputId": "387f953f-f193-4682-df10-1c917c4a0148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of dry_gangrene images in training set 42\n",
            "Number of wet_gangrene images in training set 23\n",
            "Number of gas_gangrene images in training set 22\n",
            "Number of normal_foot images in training set 8\n",
            "========================================================\n",
            "Number of dry_gangrene images in test set 42\n",
            "Number of wet_gangrene images in test set 23\n",
            "Number of gas_gangrene images in test set 22\n",
            "Number of normal_foot images in test set 8\n",
            "========================================================\n",
            "Number of dry_gangrene images in validation set 42\n",
            "Number of wet_gangrene images in validation set 23\n",
            "Number of gas_gangrene images in validation set 22\n",
            "Number of normal_foot images in validation set 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import IPython.display as display\n",
        "\n",
        "image_path = '/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/normal_foot/4821361372_06e20c245d_b.jpg'\n",
        "\n",
        "with open(image_path, 'rb') as f:\n",
        "    display.display(display.Image(data=f.read(), width=500))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "ZaAbswDmz6sT",
        "outputId": "e5393de8-158b-4857-ad77-627331fc0e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 500
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_path = '/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train/gas_gangrene/1-s2.0-S1067251613004882-gr1.jpg'\n",
        "\n",
        "with open(image_path, 'rb') as f:\n",
        "    display.display(display.Image(data=f.read(), width=500))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "BwPf-DTK0L4b",
        "outputId": "4ec47186-8b15-4e26-a550-a5eca1271161"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 500
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "jXGrxQZ80sFs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, brightness_range=[0.5, 1.5])\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory('/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPO6hct303Uz",
        "outputId": "5d4fc2ec-4153-4f4d-ef62-8afcb8c9856d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 279 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory('/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train',\n",
        "                                                    target_size=(225, 225),\n",
        "                                                    batch_size=32,\n",
        "                                                    class_mode='categorical')\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory('/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid',\n",
        "                                                        target_size=(225, 225),\n",
        "                                                        batch_size=32,\n",
        "                                                        class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OLKlFlX0-au",
        "outputId": "1364ee43-7e57-4738-de6a-83b85d9be56b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 93 images belonging to 4 classes.\n",
            "Found 93 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "from keras.applications import VGG16\n",
        "\n",
        "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(225, 225, 3))\n",
        "\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "model = Sequential()\n",
        "model.add(Conv2D(64, (3, 3), input_shape=(225, 225, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(256, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(4, activation='softmax'))"
      ],
      "metadata": {
        "id": "76Rh7EeE104A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "FESBVIkt13zi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "model_checkpoint = ModelCheckpoint('best_model.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)\n",
        "\n",
        "callbacks = [early_stop, model_checkpoint]\n",
        "\n",
        "history = model.fit(train_generator,\n",
        "                    batch_size=16,\n",
        "                    epochs=100,\n",
        "                    validation_data=validation_generator,\n",
        "                    validation_batch_size=16,\n",
        "                    callbacks=callbacks\n",
        "                    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_x57nZowhoP2",
        "outputId": "282e3651-fa76-4f17-a7a4-dfec83d2d61d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7240 - accuracy: 0.6452 \n",
            "Epoch 1: val_accuracy improved from -inf to 0.83871, saving model to best_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r3/3 [==============================] - 55s 19s/step - loss: 0.7240 - accuracy: 0.6452 - val_loss: 0.5258 - val_accuracy: 0.8387\n",
            "Epoch 2/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6226 - accuracy: 0.7742 \n",
            "Epoch 2: val_accuracy did not improve from 0.83871\n",
            "3/3 [==============================] - 48s 17s/step - loss: 0.6226 - accuracy: 0.7742 - val_loss: 0.5708 - val_accuracy: 0.7849\n",
            "Epoch 3/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.8110 - accuracy: 0.6667\n",
            "Epoch 3: val_accuracy did not improve from 0.83871\n",
            "3/3 [==============================] - 45s 16s/step - loss: 0.8110 - accuracy: 0.6667 - val_loss: 0.4795 - val_accuracy: 0.8172\n",
            "Epoch 4/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7564 - accuracy: 0.7634 \n",
            "Epoch 4: val_accuracy improved from 0.83871 to 0.84946, saving model to best_model.h5\n",
            "3/3 [==============================] - 49s 17s/step - loss: 0.7564 - accuracy: 0.7634 - val_loss: 0.5329 - val_accuracy: 0.8495\n",
            "Epoch 5/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7851 - accuracy: 0.6129 \n",
            "Epoch 5: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 16s/step - loss: 0.7851 - accuracy: 0.6129 - val_loss: 0.5322 - val_accuracy: 0.7957\n",
            "Epoch 6/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7641 - accuracy: 0.6989 \n",
            "Epoch 6: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 16s/step - loss: 0.7641 - accuracy: 0.6989 - val_loss: 0.5821 - val_accuracy: 0.7097\n",
            "Epoch 7/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6642 - accuracy: 0.7419\n",
            "Epoch 7: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 57s 21s/step - loss: 0.6642 - accuracy: 0.7419 - val_loss: 0.4713 - val_accuracy: 0.7957\n",
            "Epoch 8/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7345 - accuracy: 0.6989 \n",
            "Epoch 8: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 16s/step - loss: 0.7345 - accuracy: 0.6989 - val_loss: 0.4960 - val_accuracy: 0.7957\n",
            "Epoch 9/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6578 - accuracy: 0.7204\n",
            "Epoch 9: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 47s 16s/step - loss: 0.6578 - accuracy: 0.7204 - val_loss: 0.5593 - val_accuracy: 0.7742\n",
            "Epoch 10/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6887 - accuracy: 0.6774 \n",
            "Epoch 10: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 48s 17s/step - loss: 0.6887 - accuracy: 0.6774 - val_loss: 0.5288 - val_accuracy: 0.7849\n",
            "Epoch 11/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7476 - accuracy: 0.6882 \n",
            "Epoch 11: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 55s 17s/step - loss: 0.7476 - accuracy: 0.6882 - val_loss: 0.4886 - val_accuracy: 0.8065\n",
            "Epoch 12/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6793 - accuracy: 0.7742\n",
            "Epoch 12: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 48s 16s/step - loss: 0.6793 - accuracy: 0.7742 - val_loss: 0.4479 - val_accuracy: 0.8065\n",
            "Epoch 13/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7277 - accuracy: 0.6882 \n",
            "Epoch 13: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 17s/step - loss: 0.7277 - accuracy: 0.6882 - val_loss: 0.4050 - val_accuracy: 0.8495\n",
            "Epoch 14/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6350 - accuracy: 0.7312 \n",
            "Epoch 14: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 56s 22s/step - loss: 0.6350 - accuracy: 0.7312 - val_loss: 0.4273 - val_accuracy: 0.8495\n",
            "Epoch 15/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.7807 - accuracy: 0.6344 \n",
            "Epoch 15: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 16s/step - loss: 0.7807 - accuracy: 0.6344 - val_loss: 0.5544 - val_accuracy: 0.7742\n",
            "Epoch 16/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6647 - accuracy: 0.7204\n",
            "Epoch 16: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 46s 16s/step - loss: 0.6647 - accuracy: 0.7204 - val_loss: 0.5069 - val_accuracy: 0.8172\n",
            "Epoch 17/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6338 - accuracy: 0.7204\n",
            "Epoch 17: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 58s 21s/step - loss: 0.6338 - accuracy: 0.7204 - val_loss: 0.4497 - val_accuracy: 0.8280\n",
            "Epoch 18/100\n",
            "3/3 [==============================] - ETA: 0s - loss: 0.6026 - accuracy: 0.7097 \n",
            "Epoch 18: val_accuracy did not improve from 0.84946\n",
            "3/3 [==============================] - 47s 16s/step - loss: 0.6026 - accuracy: 0.7097 - val_loss: 0.4504 - val_accuracy: 0.7957\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''history = model.fit(train_generator,\n",
        "                    batch_size=16,\n",
        "                    epochs=50,\n",
        "                    validation_data=validation_generator,\n",
        "                    validation_batch_size=16\n",
        "                    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "95sD3Ogy2Ash",
        "outputId": "518fb7cc-069d-4bc7-ed85-692f33e023c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'train_generator_resampled' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-7640b773cfde>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mearly_stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m history = model.fit(train_generator_resampled,\n\u001b[0m\u001b[1;32m      9\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_generator_resampled' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "sns.set_context(\"poster\")\n",
        "\n",
        "figure(figsize=(25, 25), dpi=100)\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "id": "QhHJxB3y9FqT",
        "outputId": "273f1e0c-b24e-433d-ccff-ffb6731fac4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"model.h5\")"
      ],
      "metadata": {
        "id": "qrqppprBDdMj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2bdc095-5d05-4949-f263-11ab684a7c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv model.h5 /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "hBOKU0OmXy6x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "import numpy as np\n",
        "\n",
        "def preprocess_image(image_path, target_size=(225, 225)):\n",
        "    img = load_img(image_path, target_size=target_size)\n",
        "    x = img_to_array(img)\n",
        "    x = x.astype('float32') / 255.\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    return x\n",
        "\n",
        "x = preprocess_image('/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/gas_gangrene/492685_1_En_37_Fig5_HTML.jpg'\n",
        ")"
      ],
      "metadata": {
        "id": "p38Umao5ECzO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x)\n",
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGAaCMX7EWj7",
        "outputId": "f88bb1ff-5618-40ca-b7e9-adaa2298ab70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 263ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.6875834 , 0.17484504, 0.01740923, 0.12016242], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = train_generator.class_indices\n",
        "labels = {v: k for k, v in labels.items()}\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0yRD1Q-Egtk",
        "outputId": "c047e8c6-88af-4716-fdd6-10f3ace91ae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'dry_gangrene', 1: 'gas_gangrene', 2: 'normal_foot', 3: 'wet_gangrene'}"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "# Define class labels in the correct order\n",
        "class_labels = ['dry_gangrene', 'gas_gangrene', 'normal_foot', 'wet_gangrene']\n",
        "\n",
        "# Load the model\n",
        "model = load_model('/content/drive/MyDrive/model.h5')\n",
        "if model is not None:\n",
        "    print(\"Model loaded successfully!\")\n",
        "else:\n",
        "    print(\"Error loading the model. Please check the file path and try again.\")\n",
        "\n",
        "# Preprocess the image\n",
        "x = preprocess_image('/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/test/wet_gangrene/1-Figure1-1.png')\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(x)\n",
        "\n",
        "# Get the predicted class index\n",
        "predicted_class_index = np.argmax(predictions[0])\n",
        "\n",
        "# Get the predicted class label\n",
        "predicted_label = class_labels[predicted_class_index]\n",
        "\n",
        "print(predicted_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zY_Byb1jEkyS",
        "outputId": "606edd64-6d4e-43da-ad41-6720cc9f1523"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded successfully!\n",
            "1/1 [==============================] - 0s 192ms/step\n",
            "wet_gangrene\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''# For pre-trained model\n",
        "from keras.applications.resnet50 import ResNet50, preprocess_input\n",
        "\n",
        "# For SVM\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# For Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# For Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# For F1 score calculation\n",
        "from sklearn.metrics import f1_score"
      ],
      "metadata": {
        "id": "uOJXwjKWVT3F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''# Load the pre-trained ResNet50 model without the top classification layer\n",
        "resnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(225, 225, 3))\n",
        "\n",
        "# Extract features from the training and validation sets\n",
        "train_features = resnet50_model.predict_generator(train_generator, verbose=1)\n",
        "validation_features = resnet50_model.predict_generator(validation_generator, verbose=1)\n",
        "\n",
        "# Get the labels for training and validation sets\n",
        "train_labels = train_generator.classes\n",
        "validation_labels = validation_generator.classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDNx1UzfXGGP",
        "outputId": "cab410de-b559-4498-b6fa-52adf33aef10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 4s 0us/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-107-6b8e3a84f77c>:5: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  train_features = resnet50_model.predict_generator(train_generator, verbose=1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 26s 7s/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-107-6b8e3a84f77c>:6: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  validation_features = resnet50_model.predict_generator(validation_generator, verbose=1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 25s 7s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''# SVM Hyperparameter Tuning\n",
        "svm_param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': [0.001, 0.01, 0.1], 'probability': [True]}\n",
        "svm_grid_search = GridSearchCV(SVC(), svm_param_grid, cv=5, scoring='f1_macro')\n",
        "# Reshape the training features\n",
        "train_features_reshaped = train_features.reshape(train_features.shape[0], -1)\n",
        "# Fit the SVM model\n",
        "svm_grid_search.fit(train_features_reshaped, train_labels)\n",
        "svm_model = svm_grid_search.best_estimator_\n",
        "# Reshape the validation features to have a maximum of two dimensions\n",
        "validation_features_reshaped = validation_features.reshape(validation_features.shape[0], -1)\n",
        "# Make predictions using the SVC model\n",
        "svm_predictions = svm_model.predict(validation_features_reshaped)\n",
        "# Calculate the F1 score\n",
        "svm_f1 = f1_score(validation_labels, svm_predictions, average='macro')\n",
        "# Print the F1 score\n",
        "print(f\"SVM F1 Score: {svm_f1}\")\n",
        "\n",
        "\n",
        "# Random Forest Hyperparameter Tuning\n",
        "rf_param_dist = {'n_estimators': [100, 200, 300], 'max_depth': [10, 20, None], 'max_features': ['sqrt', 'log2']}\n",
        "rf_random_search = RandomizedSearchCV(RandomForestClassifier(), rf_param_dist, cv=5, scoring='f1_macro', n_iter=10)\n",
        "# Reshape the training features to have a maximum of two dimensions\n",
        "train_features_reshaped = train_features.reshape(train_features.shape[0], -1)\n",
        "rf_random_search.fit(train_features_reshaped, train_labels)\n",
        "# Reshape the validation features to have a maximum of two dimensions\n",
        "validation_features_reshaped = validation_features.reshape(validation_features.shape[0], -1)\n",
        "# Make predictions using the Random Forest model\n",
        "rf_predictions = rf_model.predict(validation_features_reshaped)\n",
        "# Calculate the F1 score\n",
        "rf_f1 = f1_score(validation_labels, rf_predictions, average='macro')\n",
        "# Print the F1 score\n",
        "print(f\"Random Forest F1 Score: {rf_f1}\")\n",
        "\n",
        "# Logistic Regression Hyperparameter Tuning\n",
        "lr_param_grid = {'C': [0.1, 1, 10], 'solver': ['liblinear', 'lbfgs']}\n",
        "lr_grid_search = GridSearchCV(LogisticRegression(), lr_param_grid, cv=5, scoring='f1_macro')\n",
        "train_features_reshaped = train_features.reshape(train_features.shape[0], -1)\n",
        "lr_grid_search.fit(train_features_reshaped, train_labels)\n",
        "lr_model = lr_grid_search.best_estimator_\n",
        "lr_predictions = lr_model.predict(validation_features_reshaped)\n",
        "lr_f1 = f1_score(validation_labels, lr_predictions, average='macro')\n",
        "print(f\"Logistic Regression F1 Score: {lr_f1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "YHjpMiuJXsM0",
        "outputId": "7757a35f-ec62-4ced-d444-d3c6ce1731c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM F1 Score: 0.3028586045468562\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'rf_model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-108-098f1162faf1>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mvalidation_features_reshaped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m# Make predictions using the Random Forest model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mrf_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_features_reshaped\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;31m# Calculate the F1 score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mrf_f1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf_predictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'macro'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'rf_model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Define class labels\n",
        "class_labels = ['dry_gangrene', 'gas_gangrene', 'normal_foot', 'wet_gangrene']\n",
        "\n",
        "# Load pre-trained ResNet50 model\n",
        "resnet50_model = ResNet50(weights='imagenet', include_top=False, input_shape=(225, 225, 3))\n",
        "\n",
        "# Function to extract features\n",
        "def extract_features(generator, model):\n",
        "    features = model.predict(generator, verbose=1)\n",
        "    return features.reshape(features.shape[0], -1)\n",
        "\n",
        "# Data generators\n",
        "train_data_dir = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/train\"\n",
        "validation_data_dir = \"/content/drive/MyDrive/Laptop_backup/Gangrene_Detection/model/dataset/valid\"\n",
        "train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, brightness_range=[0.5, 1.5])\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, brightness_range=[0.5, 1.5])\n",
        "train_generator = train_datagen.flow_from_directory(train_data_dir, target_size=(225, 225), batch_size=32, class_mode='categorical')\n",
        "validation_generator = validation_datagen.flow_from_directory(validation_data_dir, target_size=(225, 225), batch_size=32, class_mode='categorical')\n",
        "\n",
        "# Extract features\n",
        "train_features = extract_features(train_generator, resnet50_model)\n",
        "validation_features = extract_features(validation_generator, resnet50_model)\n",
        "train_labels = train_generator.classes\n",
        "validation_labels = validation_generator.classes\n",
        "\n",
        "# Hyperparameter tuning for SVM\n",
        "svm_param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': [0.001, 0.01, 0.1]}\n",
        "svm_grid_search = GridSearchCV(SVC(), svm_param_grid, cv=5, scoring='f1_macro')\n",
        "svm_grid_search.fit(train_features, train_labels)\n",
        "svm_model = svm_grid_search.best_estimator_\n",
        "\n",
        "# Hyperparameter tuning for Random Forest\n",
        "rf_param_grid = {'n_estimators': [100, 200, 300], 'max_depth': [10, 20, None], 'max_features': ['sqrt', 'log2']}\n",
        "rf_grid_search = GridSearchCV(RandomForestClassifier(), rf_param_grid, cv=5, scoring='f1_macro')\n",
        "rf_grid_search.fit(train_features, train_labels)\n",
        "rf_model = rf_grid_search.best_estimator_\n",
        "\n",
        "# Hyperparameter tuning for Logistic Regression\n",
        "lr_param_grid = {'C': [0.1, 1, 10], 'solver': ['liblinear', 'lbfgs']}\n",
        "lr_grid_search = GridSearchCV(LogisticRegression(), lr_param_grid, cv=5, scoring='f1_macro')\n",
        "lr_grid_search.fit(train_features, train_labels)\n",
        "lr_model = lr_grid_search.best_estimator_\n",
        "\n",
        "# Evaluate models\n",
        "svm_predictions = svm_model.predict(validation_features)\n",
        "svm_f1 = f1_score(validation_labels, svm_predictions, average='macro')\n",
        "rf_predictions = rf_model.predict(validation_features)\n",
        "rf_f1 = f1_score(validation_labels, rf_predictions, average='macro')\n",
        "lr_predictions = lr_model.predict(validation_features)\n",
        "lr_f1 = f1_score(validation_labels, lr_predictions, average='macro')\n",
        "\n",
        "print(f\"SVM F1 Score: {svm_f1}\")\n",
        "print(f\"Random Forest F1 Score: {rf_f1}\")\n",
        "print(f\"Logistic Regression F1 Score: {lr_f1}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8moHfNjuqSw",
        "outputId": "95922fce-06a4-4519-a925-97336e5164bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 93 images belonging to 4 classes.\n",
            "Found 93 images belonging to 4 classes.\n",
            "3/3 [==============================] - 26s 7s/step\n",
            "3/3 [==============================] - 28s 8s/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM F1 Score: 0.25448924509074883\n",
            "Random Forest F1 Score: 0.2565476190476191\n",
            "Logistic Regression F1 Score: 0.22179432320638995\n"
          ]
        }
      ]
    }
  ]
}