CervicalCancerImages.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "mTTKyNNBpWyU"
      ],
      "mount_file_id": "1xarRk2MfDcMIok1ntA610fS08yHjj7s8",
      "authorship_tag": "ABX9TyOzBvFhhOGgNS1XgPUDLunR",
      "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/erinmcmahon26/Cervical_Cancer_Risk_Assessment/blob/main/CervicalCancerImages.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The models in this notebook aren't outputted in the notebook itself. I wrote them to databricks."
      ],
      "metadata": {
        "id": "l2axWnByyCrt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRL-qxpGj1zO",
        "outputId": "e1147b52-bb96-4a5b-bcc0-72fcb51200c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.25.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (8.0.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Upload Kaggle API token"
      ],
      "metadata": {
        "id": "QPjlLdDCoBDQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "  \n",
        "# Then move kaggle.json into the folder where the API expects to find it.\n",
        "!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "xFGRjzJsoDIr",
        "outputId": "685d8d98-6063-4f5c-ca9d-cafbc6765f8f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8789ed95-37bf-44f3-971f-a32adf3851db\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8789ed95-37bf-44f3-971f-a32adf3851db\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "User uploaded file \"kaggle.json\" with length 66 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Data From Kaggle"
      ],
      "metadata": {
        "id": "6eihB8OGpURA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d prahladmehandiratta/cervical-cancer-largest-dataset-sipakmed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzxEy0eOoKzy",
        "outputId": "74d476ef-69d2-452a-8b28-f2b7e2dda880"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading cervical-cancer-largest-dataset-sipakmed.zip to /content\n",
            "100% 6.40G/6.40G [02:54<00:00, 41.7MB/s]\n",
            "100% 6.40G/6.40G [02:54<00:00, 39.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Unzip Kaggle Data"
      ],
      "metadata": {
        "id": "mTTKyNNBpWyU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip cervical-cancer-largest-dataset-sipakmed.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gp2tT7a5oT2G",
        "outputId": "db2873b3-5a1b-4270-9f37-ff7c05c7055f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: im_Parabasal/im_Parabasal/CROPPED/075_12_cyt.dat  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/060_04.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/061_04.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_02.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_03.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_04.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_05.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_06.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_07.bmp  \n",
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            "  inflating: im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/069_08.bmp  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Set up MLFlow"
      ],
      "metadata": {
        "id": "4f2ShEvpv2c1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://medium.com/the-point-collections/intro-to-mlflow-with-colab-part-1-2-beb80c960ad9"
      ],
      "metadata": {
        "id": "7gc65tAaw6PF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mlflow\n",
        "!databricks configure --host https://community.cloud.databricks.com/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZY0Tkgu1v03s",
        "outputId": "f7d9a1f3-92de-46b1-d0ca-a96f0bf64719"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mlflow\n",
            "  Downloading mlflow-2.1.1-py3-none-any.whl (16.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m65.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cloudpickle<3 in /usr/local/lib/python3.8/dist-packages (from mlflow) (2.2.1)\n",
            "Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.8/dist-packages (from mlflow) (7.1.2)\n",
            "Requirement already satisfied: pytz<2023 in /usr/local/lib/python3.8/dist-packages (from mlflow) (2022.7.1)\n",
            "Requirement already satisfied: requests<3,>=2.17.3 in /usr/local/lib/python3.8/dist-packages (from mlflow) (2.25.1)\n",
            "Requirement already satisfied: sqlparse<1,>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from mlflow) (0.4.3)\n",
            "Requirement already satisfied: markdown<4,>=3.3 in /usr/local/lib/python3.8/dist-packages (from mlflow) (3.4.1)\n",
            "Collecting querystring-parser<2\n",
            "  Downloading querystring_parser-1.2.4-py2.py3-none-any.whl (7.9 kB)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0m/bin/bash: databricks: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mlflow\n",
        "mlflow.set_tracking_uri(\"databricks\")\n",
        "mlflow.set_experiment(\"/Users/rachelsickler2024@u.northwestern.edu/cervical_cancer_img\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "37tA3bB8wKN3",
        "outputId": "a7eb7375-fb1e-445f-f2b3-4994f7506ffe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-6b304a996e15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmlflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmlflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_tracking_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"databricks\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmlflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_experiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Users/rachelsickler2024@u.northwestern.edu/cervical_cancer_img\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mlflow'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mlflow.tensorflow\n",
        "mlflow.tensorflow.autolog(every_n_iter=1)"
      ],
      "metadata": {
        "id": "XJNzsDk3FY4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Other Imports"
      ],
      "metadata": {
        "id": "bnJAkzyb2RY6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, make_scorer, f1_score\n",
        "from keras.applications.resnet import ResNet50\n",
        "from keras.applications.densenet import DenseNet121\n",
        "from keras.applications.efficientnet import EfficientNetB5\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten, Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow\n",
        "import cv2\n",
        "import joblib\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from keras.applications.efficientnet import EfficientNetB7"
      ],
      "metadata": {
        "id": "5WBdVT2M2PuW"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix_labeled(y_true, y_pred, CLASSES_LIST):\n",
        "    mtx = confusion_matrix(y_true, y_pred)\n",
        "    # define classes \n",
        "    classes = CLASSES_LIST\n",
        "    temp_df = pd.DataFrame(data=mtx,columns=classes)\n",
        "    temp_df.index = classes\n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    sns.heatmap(temp_df, annot=True, fmt='d', linewidths=.75,  cbar=False, ax=ax,cmap='Blues',linecolor='white')\n",
        "    #  square=True,\n",
        "    plt.ylabel('true label')\n",
        "    plt.xlabel('predicted label')\n",
        "    return plt"
      ],
      "metadata": {
        "id": "XZXBao1vH4LF"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CLASS_LIST = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediate']"
      ],
      "metadata": {
        "id": "SHC7v_hrH--3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "dSU30-9Dp2Vp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import modules\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# from matplotlib.image import imread\n",
        "# from os import makedirs\n",
        "# from os import listdir\n",
        "import os\n",
        "# from shutil import copyfile\n",
        "# from random import seed\n",
        "# from random import random\n",
        "from PIL import Image\n",
        "import cv2\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "# import sys\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.models import Model\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "nTcDzZL5pNbB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=[]\n",
        "labels=[]\n",
        "\n",
        "height = 64\n",
        "width = 64\n",
        "channels = 3\n",
        "classes = 43\n",
        "n_inputs = height * width * channels\n",
        "\n",
        "path1 = \"im_Dyskeratotic/im_Dyskeratotic/CROPPED/\"\n",
        "path2 = \"im_Koilocytotic/im_Koilocytotic/CROPPED/\"\n",
        "path3 = \"im_Metaplastic/im_Metaplastic/CROPPED/\"\n",
        "path4 = \"im_Parabasal/im_Parabasal/CROPPED/\"\n",
        "path5 = \"im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/\"\n",
        "\n",
        "path = [path1, path2, path3, path4, path5]\n",
        "\n",
        "# print(Class)\n",
        "i = 0\n",
        "for p in path:\n",
        "    Class=os.listdir(p)\n",
        "    for a in Class:\n",
        "        if(a[-1] == 'p'):\n",
        "            labels.append(i)\n",
        "            try:\n",
        "                image=cv2.imread(p+a)\n",
        "                image_from_array = Image.fromarray(image, 'RGB')\n",
        "                size_image = image_from_array.resize((height, width))\n",
        "                data.append(np.array(size_image))\n",
        "            except AttributeError:\n",
        "                print(\" \")\n",
        "    i+=1\n",
        "\n",
        "# Converting data to ndarray - \n",
        "labels = np.array(labels)\n",
        "data = np.array(data)\n",
        "\n",
        "print(data.shape)\n",
        "print(labels.shape)\n",
        "print(labels)\n",
        "\n",
        "# Randomize the order of the input images\n",
        "s = np.arange(data.shape[0])\n",
        "np.random.seed(43)\n",
        "np.random.shuffle(s)\n",
        "data = data[s]\n",
        "labels = labels[s]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYLvCw6ht7xy",
        "outputId": "9ba8589c-6e9e-45d6-a8b6-a86c256a481d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4049, 64, 64, 3)\n",
            "(4049,)\n",
            "[0 0 0 ... 4 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = [\"Dyskeratotic\", \"Koilocytotic\", \"Metaplastic\", \"Parabasal\", \"Superficial-Intermediate\"]\n",
        "\n",
        "def show_the_image(i):\n",
        "    plt.imshow(data[i])\n",
        "    plt.xlabel(categories[labels[i]])\n",
        "    \n",
        "show_the_image(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JIxmwtE5vEcs",
        "outputId": "58e8a911-e2c7-4387-ba4a-63f344bbdb3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(10):\n",
        "  # plt.subplot(330 + 1 + i) # rows, cols, idx\n",
        "  plt.subplot(3, 5, i+1) # rows, cols, idx\n",
        "  plt.imshow(data[i])\n",
        "  plt.xlabel(categories[labels[i]])\n",
        "  plt.tight_layout()\n",
        "  plt.rcParams[\"figure.figsize\"] = (10, 15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "zEta5NnVvFn2",
        "outputId": "cba7a56c-7456-46a0-c68d-270579dc76d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for p in path:\n",
        "  print(p, len(os.listdir(p)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GPvl-QivuRn",
        "outputId": "66d1e740-71bd-4f33-cc06-f6e165f1fcf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "im_Dyskeratotic/im_Dyskeratotic/CROPPED/ 2439\n",
            "im_Koilocytotic/im_Koilocytotic/CROPPED/ 2475\n",
            "im_Metaplastic/im_Metaplastic/CROPPED/ 2379\n",
            "im_Parabasal/im_Parabasal/CROPPED/ 2361\n",
            "im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/ 2493\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 1"
      ],
      "metadata": {
        "id": "IAl5vNloqI01"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.kaggle.com/code/sohamgovande/low-res-cervical-cancer-classification"
      ],
      "metadata": {
        "id": "sPuiVpj7qpWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgdata = []\n",
        "labels = []\n",
        "###### FEEDBACK - how to determine the correct image size?\n",
        "compressed_img_size = 32\n",
        "\n",
        "celltypes = ['im_Dyskeratotic', 'im_Koilocytotic', 'im_Metaplastic', 'im_Parabasal', 'im_Superficial-Intermediate']\n",
        "path = [celltype  + \"/\" + celltype + \"/CROPPED/\" for celltype in celltypes]\n",
        "\n",
        "# Low pass filter\n",
        "LOW_PASS_FILTER = False\n",
        "# High pass filter\n",
        "HIGH_PASS_FILTER = False\n",
        "\n",
        "cur_label = -1\n",
        "for celltype in path:\n",
        "    cur_label += 1\n",
        "    # Iterate over each cell's individual image\n",
        "    cellimages = os.listdir(celltype)\n",
        "    for cellimage in cellimages:\n",
        "        if cellimage.lower().endswith(\".bmp\"):\n",
        "            # Mark each cell with a cell type\n",
        "            labels.append(cur_label)\n",
        "            img = cv2.imread(celltype+cellimage)\n",
        "            img = np.array(Image.fromarray(img, 'RGB').resize((compressed_img_size, compressed_img_size)))\n",
        "            if LOW_PASS_FILTER:\n",
        "                img = cv2.GaussianBlur(img, (3, 3), cv2.BORDER_DEFAULT)\n",
        "            elif HIGH_PASS_FILTER:\n",
        "                img = img - cv2.GaussianBlur(img, (3, 3), cv2.BORDER_DEFAULT) + 127\n",
        "            imgdata.append(img)\n",
        "\n",
        "labels = np.array(labels)\n",
        "imgdata = np.array(imgdata)\n",
        "\n",
        "# Shuffle/randomize order\n",
        "random_order = np.arange(imgdata.shape[0])\n",
        "np.random.shuffle(random_order)\n",
        "labels = labels[random_order]\n",
        "imgdata = imgdata[random_order]\n",
        "\n",
        "# Get # of each cell type\n",
        "unique, counts = np.unique(labels, return_counts=True)\n",
        "print(np.asarray((unique, counts)).T)\n",
        "\n",
        "# Used for generating figures (displaying cell images)\n",
        "def disp_img(i):\n",
        "    plt.imshow(imgdata[i])\n",
        "    plt.xlabel(celltypes[labels[i]][len('im_'):])\n",
        "    \n",
        "for i in range(0, len(labels)):\n",
        "    # Replace this with 0, 1, 2, 3, or 4 depending on the type of figure being generated\n",
        "    if labels[i] == 2:\n",
        "        disp_img(i)\n",
        "        break\n",
        "\n",
        "# Initialize neural networks\n",
        "# do not include the fully-connected layer at the top of the network\n",
        "model_efficientnet = EfficientNetB7(weights=None, include_top = False)\n",
        "features_efficientnet = model_efficientnet.predict(imgdata, batch_size=32)\n",
        "\n",
        "cur = features_efficientnet\n",
        "extracted_features = []\n",
        "zipped_data = zip(cur, labels)\n",
        "for cur, label in zipped_data:\n",
        "    cur = np.append(cur.reshape(cur.shape[0] * cur.shape[1] * cur.shape[2]), label)\n",
        "    extracted_features.append(cur)\n",
        "extracted_features = np.array(extracted_features)\n",
        "\n",
        "# Use min-max scaling to normalize the data\n",
        "X = extracted_features[:, 0:-1]\n",
        "y = extracted_features[:, -1]\n",
        "scaler = MinMaxScaler()\n",
        "scaled_X = scaler.fit_transform(X)\n",
        "\n",
        "# PCA\n",
        "pca = PCA(n_components = 0.95) # represent 95% of the variation through principal components\n",
        "pca.fit(scaled_X)\n",
        "pca_x = pca.transform(scaled_X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(pca_x, y, test_size = 0.2, random_state = 49)\n",
        "\n",
        "# Neural network for classification\n",
        "model = Sequential([\n",
        "    Flatten(input_shape=(X_train.shape[1], 1)),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(rate=0.8),\n",
        "    Dense(5, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',  optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "oW-n3PM5qMZN",
        "outputId": "44ef512e-4564-4951-ba4b-edbea68ded92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0 825]\n",
            " [  1 793]\n",
            " [  2 813]\n",
            " [  3 787]\n",
            " [  4 831]]\n",
            "127/127 [==============================] - 52s 368ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the neural network\n",
        "nn = model.fit(X_train, y_train, batch_size=32, epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqAZbp2SqY5h",
        "outputId": "29562f2d-c78a-4b81-bcad-633c88a8a32c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "102/102 [==============================] - 1s 2ms/step - loss: 1.4550 - accuracy: 0.4310\n",
            "Epoch 2/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 1.0845 - accuracy: 0.5823\n",
            "Epoch 3/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.9518 - accuracy: 0.6422\n",
            "Epoch 4/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.8736 - accuracy: 0.6761\n",
            "Epoch 5/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.8451 - accuracy: 0.6934\n",
            "Epoch 6/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.8000 - accuracy: 0.7110\n",
            "Epoch 7/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7848 - accuracy: 0.7095\n",
            "Epoch 8/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7450 - accuracy: 0.7339\n",
            "Epoch 9/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7359 - accuracy: 0.7320\n",
            "Epoch 10/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7284 - accuracy: 0.7357\n",
            "Epoch 11/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6960 - accuracy: 0.7524\n",
            "Epoch 12/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7118 - accuracy: 0.7391\n",
            "Epoch 13/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6754 - accuracy: 0.7567\n",
            "Epoch 14/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6729 - accuracy: 0.7564\n",
            "Epoch 15/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6761 - accuracy: 0.7604\n",
            "Epoch 16/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6673 - accuracy: 0.7549\n",
            "Epoch 17/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6404 - accuracy: 0.7629\n",
            "Epoch 18/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6343 - accuracy: 0.7749\n",
            "Epoch 19/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6205 - accuracy: 0.7774\n",
            "Epoch 20/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6141 - accuracy: 0.7746\n",
            "Epoch 21/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6161 - accuracy: 0.7728\n",
            "Epoch 22/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6115 - accuracy: 0.7654\n",
            "Epoch 23/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6004 - accuracy: 0.7839\n",
            "Epoch 24/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6076 - accuracy: 0.7839\n",
            "Epoch 25/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5980 - accuracy: 0.7851\n",
            "Epoch 26/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5839 - accuracy: 0.7901\n",
            "Epoch 27/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5753 - accuracy: 0.7916\n",
            "Epoch 28/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5818 - accuracy: 0.7864\n",
            "Epoch 29/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5831 - accuracy: 0.7873\n",
            "Epoch 30/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5611 - accuracy: 0.7978\n",
            "Epoch 31/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5603 - accuracy: 0.7987\n",
            "Epoch 32/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5587 - accuracy: 0.7984\n",
            "Epoch 33/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5560 - accuracy: 0.7981\n",
            "Epoch 34/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5524 - accuracy: 0.8036\n",
            "Epoch 35/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5563 - accuracy: 0.7891\n",
            "Epoch 36/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5477 - accuracy: 0.8002\n",
            "Epoch 37/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5321 - accuracy: 0.7938\n",
            "Epoch 38/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5482 - accuracy: 0.7953\n",
            "Epoch 39/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5337 - accuracy: 0.8129\n",
            "Epoch 40/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5412 - accuracy: 0.7922\n",
            "Epoch 41/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5432 - accuracy: 0.8002\n",
            "Epoch 42/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5195 - accuracy: 0.8132\n",
            "Epoch 43/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5098 - accuracy: 0.8080\n",
            "Epoch 44/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5143 - accuracy: 0.8129\n",
            "Epoch 45/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5217 - accuracy: 0.8194\n",
            "Epoch 46/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5157 - accuracy: 0.8101\n",
            "Epoch 47/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.4961 - accuracy: 0.8160\n",
            "Epoch 48/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5200 - accuracy: 0.8104\n",
            "Epoch 49/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5053 - accuracy: 0.8077\n",
            "Epoch 50/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5095 - accuracy: 0.8058\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the test set and determine the accuracy\n",
        "y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "print(\"Accuracy is: \" + str(accuracy_score(y_test, y_pred)*100) + '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DY-9iCv_qnFI",
        "outputId": "5d93ec48-a844-49f6-96be-c5bb792c7c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 0s 2ms/step\n",
            "Accuracy is: 83.08641975308642%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict = nn.history\n",
        "history_dict.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjcX1rdz1FgV",
        "outputId": "ebe8b2d2-7cfe-4e49-eea2-d02a6c4e046e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_df=pd.DataFrame(history_dict)\n",
        "history_df.tail().round(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "otDrmSto1QHQ",
        "outputId": "85d27f3c-64a5-4d9b-9765-4553f075f284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     loss  accuracy\n",
              "45  0.516     0.810\n",
              "46  0.496     0.816\n",
              "47  0.520     0.810\n",
              "48  0.505     0.808\n",
              "49  0.509     0.806"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
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              "      <th>45</th>\n",
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              "  <style>\n",
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              "\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e473343b-9936-4e08-a4f5-4123e5b66d12 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e473343b-9936-4e08-a4f5-4123e5b66d12');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute and print classification report\n",
        "print(classification_report(y_test, y_pred, target_names=CLASS_LIST))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kHn3OxW2G4a",
        "outputId": "b81ec7bd-adcc-48ed-80cf-0b490e6b5251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                          precision    recall  f1-score   support\n",
            "\n",
            "            Koilocytotic       0.86      0.71      0.78       163\n",
            "             Metaplastic       0.87      0.87      0.87       166\n",
            "            Dyskeratotic       0.85      0.96      0.90       145\n",
            "               Parabasal       0.88      0.92      0.90       168\n",
            "Superficial-Intermediate       0.80      0.81      0.80       168\n",
            "\n",
            "                accuracy                           0.85       810\n",
            "               macro avg       0.85      0.85      0.85       810\n",
            "            weighted avg       0.85      0.85      0.85       810\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_test, y_pred, target_names=CLASS_LIST, output_dict=True)"
      ],
      "metadata": {
        "id": "peIM4l0C_BBm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4G02jhA7_GmC",
        "outputId": "c7731487-136a-489d-bb8c-456f8972d1f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Koilocytotic': {'precision': 0.8592592592592593,\n",
              "  'recall': 0.7116564417177914,\n",
              "  'f1-score': 0.7785234899328859,\n",
              "  'support': 163},\n",
              " 'Metaplastic': {'precision': 0.8682634730538922,\n",
              "  'recall': 0.8734939759036144,\n",
              "  'f1-score': 0.8708708708708709,\n",
              "  'support': 166},\n",
              " 'Dyskeratotic': {'precision': 0.852760736196319,\n",
              "  'recall': 0.9586206896551724,\n",
              "  'f1-score': 0.9025974025974026,\n",
              "  'support': 145},\n",
              " 'Parabasal': {'precision': 0.88,\n",
              "  'recall': 0.9166666666666666,\n",
              "  'f1-score': 0.8979591836734694,\n",
              "  'support': 168},\n",
              " 'Superficial-Intermediate': {'precision': 0.8,\n",
              "  'recall': 0.8095238095238095,\n",
              "  'f1-score': 0.8047337278106509,\n",
              "  'support': 168},\n",
              " 'accuracy': 0.8518518518518519,\n",
              " 'macro avg': {'precision': 0.8520566937018941,\n",
              "  'recall': 0.8539923166934109,\n",
              "  'f1-score': 0.8509369349770559,\n",
              "  'support': 810},\n",
              " 'weighted avg': {'precision': 0.8519522253514464,\n",
              "  'recall': 0.8518518518518519,\n",
              "  'f1-score': 0.8498678097896056,\n",
              "  'support': 810}}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report['accuracy']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be2c_LoN_uPM",
        "outputId": "7ad98809-22db-420a-cf04-2c60f8e0107b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8395061728395061"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = confusion_matrix(y_test, y_pred)\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsExoVM8CHuE",
        "outputId": "ad837fc5-bdc6-4427-e48a-21d36c994541"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[116,   9,  15,   5,  18],\n",
              "       [  7, 145,   0,   6,   8],\n",
              "       [  5,   0, 139,   0,   1],\n",
              "       [  0,   2,   5, 154,   7],\n",
              "       [  7,  11,   4,  10, 136]])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c[1][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKEcmuRrDso7",
        "outputId": "52bf7fcf-3c9c-4cbb-efa7-9a1d329d91a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "145"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot = plot_confusion_matrix_labeled(y_test, y_pred, CLASSES_LIST=CLASS_LIST)\n",
        "plot.savefig(\"confusionMatrix.png\")\n",
        "plot.show()\n",
        "plot.close()\n",
        "\n",
        "mlflow.log_artifact(\"confusionMatrix.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "QjjnOv-b22Aq",
        "outputId": "be136f48-a607-4032-b4ff-a78fc0504859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(model, 'model1.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1EtSnmCrg3x",
        "outputId": "a5ebc1c7-83d8-4e57-b1dc-2033e7350544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model1.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MLFlow"
      ],
      "metadata": {
        "id": "Ztzb53RtVTzU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# write to an existing experiment\n",
        "experiment = mlflow.get_experiment_by_name('/Users/rachelsickler2024@u.northwestern.edu/cervical_cancer_img')\n",
        "experiment_id = experiment.experiment_id\n",
        "run_name = 'model1'\n",
        "description = 'efficientnetb7, cnn'"
      ],
      "metadata": {
        "id": "slen9gCW48_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with mlflow.start_run(\n",
        "        experiment_id=experiment_id,\n",
        "        run_name=run_name,\n",
        "        description=description,\n",
        ") as run:\n",
        "\n",
        "  # Low pass filter\n",
        "  LOW_PASS_FILTER = False\n",
        "  # High pass filter\n",
        "  HIGH_PASS_FILTER = False\n",
        "\n",
        "  # Initialize neural networks\n",
        "  # do not include the fully-connected layer at the top of the network\n",
        "  model_efficientnet = EfficientNetB7(weights=None, include_top = False)\n",
        "  features_efficientnet = model_efficientnet.predict(imgdata, batch_size=32)\n",
        "\n",
        "  cur = features_efficientnet\n",
        "  extracted_features = []\n",
        "  zipped_data = zip(cur, labels)\n",
        "  for cur, label in zipped_data:\n",
        "      cur = np.append(cur.reshape(cur.shape[0] * cur.shape[1] * cur.shape[2]), label)\n",
        "      extracted_features.append(cur)\n",
        "  extracted_features = np.array(extracted_features)\n",
        "\n",
        "  # Use min-max scaling to normalize the data\n",
        "  X = extracted_features[:, 0:-1]\n",
        "  y = extracted_features[:, -1]\n",
        "  scaler = MinMaxScaler()\n",
        "  scaled_X = scaler.fit_transform(X)\n",
        "\n",
        "  # PCA\n",
        "  pca = PCA(n_components = 0.95) # represent 95% of the variation through principal components\n",
        "  pca.fit(scaled_X)\n",
        "  pca_x = pca.transform(scaled_X)\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(pca_x, y, test_size = 0.2, random_state = 49)\n",
        "\n",
        "  # Neural network for classification\n",
        "  model = Sequential([\n",
        "      Flatten(input_shape=(X_train.shape[1], 1)),\n",
        "      Dense(256, activation='relu'),\n",
        "      Dropout(rate=0.8),\n",
        "      Dense(5, activation='softmax')\n",
        "  ])\n",
        "\n",
        "  model.compile(loss='sparse_categorical_crossentropy',  optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "  # Train the neural network\n",
        "  nn = model.fit(X_train, y_train, batch_size=32, epochs=50)\n",
        "\n",
        "  # Predict on the test set and determine the accuracy\n",
        "  y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "  print(\"Accuracy is: \" + str(accuracy_score(y_test, y_pred)*100) + '%')\n",
        "\n",
        "  # compute and print classification report\n",
        "  confusion = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "  # compute and print classification report\n",
        "  # print(classification_report(y_test, y_pred))\n",
        "  plot = plot_confusion_matrix_labeled(y_test, y_pred, CLASSES_LIST=CLASS_LIST)\n",
        "  plot.savefig(\"confusionMatrix.png\")\n",
        "  # plot.show()\n",
        "  plot.close()\n",
        "  mlflow.log_artifact(\"confusionMatrix.png\")\n",
        "\n",
        "  report = classification_report(y_test, y_pred, target_names=CLASS_LIST, output_dict=True)\n",
        "\n",
        "  metrics = {}\n",
        "  metrics['Koilocytotic_precision'] = report['Koilocytotic']['precision']\n",
        "  metrics['Koilocytotic_recall'] = report['Koilocytotic']['recall']\n",
        "  metrics['Koilocytotic_f1'] = report['Koilocytotic']['f1-score']\n",
        "  metrics['Koilocytotic_support'] = report['Koilocytotic']['support']\n",
        "  metrics['Metaplastic_precision'] = report['Metaplastic']['precision']\n",
        "  metrics['Metaplastic_recall'] = report['Metaplastic']['recall']\n",
        "  metrics['Metaplastic_f1'] = report['Metaplastic']['f1-score']\n",
        "  metrics['Metaplastic_support'] = report['Metaplastic']['support']\n",
        "  metrics['Dyskeratotic_precision'] = report['Dyskeratotic']['precision']\n",
        "  metrics['Dyskeratotic_recall'] = report['Dyskeratotic']['recall']\n",
        "  metrics['Dyskeratotic_f1'] = report['Dyskeratotic']['f1-score']\n",
        "  metrics['Dyskeratotic_support'] = report['Dyskeratotic']['support']\n",
        "  metrics['Parabasal_precision'] = report['Parabasal']['precision']\n",
        "  metrics['Parabasal_recall'] = report['Parabasal']['recall']\n",
        "  metrics['Parabasal_f1'] = report['Parabasal']['f1-score']\n",
        "  metrics['Parabasal_support'] = report['Parabasal']['support']\n",
        "  metrics['SI_precision'] = report['Superficial-Intermediate']['precision']\n",
        "  metrics['SI_recall'] = report['Superficial-Intermediate']['recall']\n",
        "  metrics['SI_f1'] = report['Superficial-Intermediate']['f1-score']\n",
        "  metrics['SI_support'] = report['Superficial-Intermediate']['support']\n",
        "  metrics['accuracy'] = report['accuracy']\n",
        "  metrics['tp_Koilocytotic'] = confusion[0][0]\n",
        "  metrics['tp_Metaplastic'] = confusion[1][1]\n",
        "  metrics['tp_Dyskeratotic'] = confusion[2][2]\n",
        "  metrics['tp_Parabasal'] = confusion[3][3]\n",
        "  metrics['tp_SI'] = confusion[4][4]\n",
        "  metrics['f1_score'] = f1_score(y_test, y_pred, average='weighted')\n",
        "  metrics['low_pass_filer'] = LOW_PASS_FILTER\n",
        "  metrics['high_pass_filter'] = HIGH_PASS_FILTER\n",
        "\n",
        "  mlflow.log_metrics(metrics)\n",
        "\n",
        "  joblib.dump(model, 'model1.pkl')\n",
        "  mlflow.log_artifact('model1.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcOjfSKV41I6",
        "outputId": "c633d09c-5e28-444f-9b71-df3676fd32d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127/127 [==============================] - 54s 373ms/step\n",
            "Epoch 1/50\n",
            "102/102 [==============================] - 1s 4ms/step - loss: 1.5256 - accuracy: 0.4159\n",
            "Epoch 2/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 1.0873 - accuracy: 0.5783\n",
            "Epoch 3/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.9577 - accuracy: 0.6406\n",
            "Epoch 4/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.8837 - accuracy: 0.6761\n",
            "Epoch 5/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.8335 - accuracy: 0.6974\n",
            "Epoch 6/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7962 - accuracy: 0.7061\n",
            "Epoch 7/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.7768 - accuracy: 0.7228\n",
            "Epoch 8/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7353 - accuracy: 0.7311\n",
            "Epoch 9/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7166 - accuracy: 0.7410\n",
            "Epoch 10/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7181 - accuracy: 0.7404\n",
            "Epoch 11/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.7059 - accuracy: 0.7397\n",
            "Epoch 12/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6876 - accuracy: 0.7505\n",
            "Epoch 13/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.6898 - accuracy: 0.7499\n",
            "Epoch 14/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6698 - accuracy: 0.7567\n",
            "Epoch 15/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6509 - accuracy: 0.7666\n",
            "Epoch 16/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6493 - accuracy: 0.7613\n",
            "Epoch 17/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6429 - accuracy: 0.7657\n",
            "Epoch 18/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6358 - accuracy: 0.7678\n",
            "Epoch 19/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6302 - accuracy: 0.7632\n",
            "Epoch 20/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.6336 - accuracy: 0.7669\n",
            "Epoch 21/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6163 - accuracy: 0.7666\n",
            "Epoch 22/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6300 - accuracy: 0.7660\n",
            "Epoch 23/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.6043 - accuracy: 0.7762\n",
            "Epoch 24/50\n",
            "102/102 [==============================] - 0s 3ms/step - loss: 0.6142 - accuracy: 0.7811\n",
            "Epoch 25/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5877 - accuracy: 0.7777\n",
            "Epoch 26/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5818 - accuracy: 0.7854\n",
            "Epoch 27/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.5908 - accuracy: 0.7854\n",
            "Epoch 28/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5748 - accuracy: 0.7873\n",
            "Epoch 29/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5744 - accuracy: 0.7894\n",
            "Epoch 30/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5861 - accuracy: 0.7860\n",
            "Epoch 31/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5573 - accuracy: 0.7916\n",
            "Epoch 32/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5631 - accuracy: 0.7882\n",
            "Epoch 33/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.5423 - accuracy: 0.8089\n",
            "Epoch 34/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5640 - accuracy: 0.7916\n",
            "Epoch 35/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5533 - accuracy: 0.7972\n",
            "Epoch 36/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5545 - accuracy: 0.7897\n",
            "Epoch 37/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5500 - accuracy: 0.7999\n",
            "Epoch 38/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5429 - accuracy: 0.8006\n",
            "Epoch 39/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.5378 - accuracy: 0.7941\n",
            "Epoch 40/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5534 - accuracy: 0.7950\n",
            "Epoch 41/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5505 - accuracy: 0.7999\n",
            "Epoch 42/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5313 - accuracy: 0.8018\n",
            "Epoch 43/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5301 - accuracy: 0.8012\n",
            "Epoch 44/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5196 - accuracy: 0.8040\n",
            "Epoch 45/50\n",
            "102/102 [==============================] - 0s 4ms/step - loss: 0.5371 - accuracy: 0.8012\n",
            "Epoch 46/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5204 - accuracy: 0.8030\n",
            "Epoch 47/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5429 - accuracy: 0.7969\n",
            "Epoch 48/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5156 - accuracy: 0.8043\n",
            "Epoch 49/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5151 - accuracy: 0.8157\n",
            "Epoch 50/50\n",
            "102/102 [==============================] - 0s 2ms/step - loss: 0.5087 - accuracy: 0.8114\n",
            "1/1 [==============================] - 0s 50ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023/01/28 21:48:52 WARNING mlflow.utils.requirements_utils: Found jaxlib version (0.3.25+cuda11.cudnn805) contains a local version label (+cuda11.cudnn805). MLflow logged a pip requirement for this package as 'jaxlib==0.3.25' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 0s 2ms/step\n",
            "Accuracy is: 83.08641975308642%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 2"
      ],
      "metadata": {
        "id": "8sU_NsJiSTiM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.kaggle.com/code/kanishk26/sipkamed-dataset"
      ],
      "metadata": {
        "id": "p3MPeNSHYVNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=[]\n",
        "labels=[]\n",
        "\n",
        "height = 64\n",
        "width = 64\n",
        "channels = 3\n",
        "classes = 43\n",
        "n_inputs = height * width * channels\n",
        "\n",
        "path1 = \"im_Dyskeratotic/im_Dyskeratotic/CROPPED/\"\n",
        "path2 = \"im_Koilocytotic/im_Koilocytotic/CROPPED/\"\n",
        "path3 = \"im_Metaplastic/im_Metaplastic/CROPPED/\"\n",
        "path4 = \"im_Parabasal/im_Parabasal/CROPPED/\"\n",
        "path5 = \"im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/\"\n",
        "\n",
        "path = [path1, path2, path3, path4, path5]\n",
        "\n",
        "# print(Class)\n",
        "i = 0\n",
        "for p in path:\n",
        "    Class=os.listdir(p)\n",
        "    for a in Class:\n",
        "        if(a[-1] == 'p'):\n",
        "            labels.append(i)\n",
        "            try:\n",
        "                image=cv2.imread(p+a)\n",
        "                image_from_array = Image.fromarray(image, 'RGB')\n",
        "                size_image = image_from_array.resize((height, width))\n",
        "                data.append(np.array(size_image))\n",
        "            except AttributeError:\n",
        "                print(\" \")\n",
        "    i+=1\n",
        "\n",
        "# Converting data to ndarray - \n",
        "labels = np.array(labels)\n",
        "data = np.array(data)\n",
        "\n",
        "print(data.shape)\n",
        "print(labels.shape)\n",
        "print(labels)\n",
        "\n",
        "# Randomize the order of the input images\n",
        "s = np.arange(data.shape[0])\n",
        "np.random.seed(43)\n",
        "np.random.shuffle(s)\n",
        "data = data[s]\n",
        "labels = labels[s]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVsOjeMZYGKy",
        "outputId": "37c9e2a6-2621-45f1-fa93-15a35d4d1834"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4049, 64, 64, 3)\n",
            "(4049,)\n",
            "[0 0 0 ... 4 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', MinMaxScaler()),\n",
        "    ('pca', PCA(n_components = 0.95)),\n",
        "\n",
        "])\n",
        "# pipeline.fit(X_train, y_train)\n",
        "\n",
        "# predictions = pipeline.predict(X_test)"
      ],
      "metadata": {
        "id": "zRycHrnhWbtl"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_resnet       = ResNet50(weights = 'imagenet', include_top = False)\n",
        "# model_densenet     = DenseNet121(weights = 'imagenet', include_top = False)\n",
        "# model_efficientnet = EfficientNetB5(weights = 'imagenet', include_top = False)\n",
        "\n",
        "features_resnet       = model_resnet.predict(data, batch_size=32)\n",
        "# features_densenet     = model_densenet.predict(data, batch_size=32)\n",
        "# features_efficientnet = model_densenet.predict(data, batch_size=32)\n",
        "\n",
        "extracted_features = []\n",
        "# for f_r, f_d, f_e, label in zip(features_resnet, features_densenet, features_efficientnet, labels):\n",
        "for f_r,  label in zip(features_resnet, labels):\n",
        "  f_r = f_r.reshape(f_r.shape[0] * f_r.shape[1] * f_r.shape[2])\n",
        "  # f_d = f_d.reshape(f_d.shape[0] * f_d.shape[1] * f_d.shape[2])\n",
        "  # f_e = f_e.reshape(f_e.shape[0] * f_e.shape[1] * f_e.shape[2])\n",
        "  \n",
        "  # f_r = np.append(f_r, f_d)\n",
        "  # f_r = np.append(f_r, f_e)\n",
        "  f_r = np.append(f_r, label)\n",
        "  extracted_features.append(f_r)\n",
        "\n",
        "extracted_features = np.array(extracted_features)\n",
        "\n",
        "# Normalization\n",
        "X = extracted_features[:, 0:-1]\n",
        "y = extracted_features[:, -1]\n",
        "\n",
        "# scaler = MinMaxScaler()\n",
        "# scaled_X = scaler.fit_transform(X)\n",
        "    \n",
        "# # extracted_features = np.array(extracted_features)\n",
        "\n",
        "# # PCA\n",
        "# pca = PCA(n_components = 0.95)\n",
        "# pca.fit(scaled_X)\n",
        "# principal_X = pca.transform(scaled_X)\n",
        "\n",
        "# output this baby\n",
        "pipeline.fit(X)\n",
        "\n",
        "principal_X = pipeline.transform(X)\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(principal_X, y, test_size = 0.2, random_state = 6140)\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(X_train.shape[1], 1)))\n",
        "# model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(rate=0.5))\n",
        "model.add(Dense(5, activation='softmax'))\n",
        "\n",
        "#Compilation of the model\n",
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy', \n",
        "    optimizer='adam', \n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "epochs = 15\n",
        "\n",
        "# Train the neural network\n",
        "nn = model.fit(X_train, y_train, batch_size=32, epochs=epochs)\n",
        "\n",
        "# Predict on the test set and determine the accuracy\n",
        "y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "print(\"Accuracy is: \" + str(accuracy_score(y_test, y_pred)*100) + '%')\n",
        "\n",
        "#########################################\n",
        "# compute and print classification report\n",
        "confusion = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# compute and print classification report\n",
        "# print(classification_report(y_test, y_pred))\n",
        "plot = plot_confusion_matrix_labeled(y_test, y_pred, CLASSES_LIST=CLASS_LIST)\n",
        "# plot.savefig(\"confusionMatrix.png\", bbox_inches=\"tight\")\n",
        "plot.show()\n",
        "plot.close()\n",
        "# mlflow.log_artifact(\"confusionMatrix.png\")\n",
        "\n",
        "report = classification_report(y_test, y_pred, target_names=CLASS_LIST, output_dict=True)\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ks4KSpYJXaF3",
        "outputId": "2db38eed-37f7-46a7-8ca0-a42ae839e3c5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 30 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fa335b48040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127/127 [==============================] - 72s 550ms/step\n",
            "Epoch 1/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.9219 - accuracy: 0.7348\n",
            "Epoch 2/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.2530 - accuracy: 0.9435\n",
            "Epoch 3/15\n",
            "102/102 [==============================] - 1s 9ms/step - loss: 0.1087 - accuracy: 0.9812\n",
            "Epoch 4/15\n",
            "102/102 [==============================] - 1s 11ms/step - loss: 0.0614 - accuracy: 0.9914\n",
            "Epoch 5/15\n",
            "102/102 [==============================] - 1s 12ms/step - loss: 0.0369 - accuracy: 0.9969\n",
            "Epoch 6/15\n",
            "102/102 [==============================] - 1s 12ms/step - loss: 0.0252 - accuracy: 0.9988\n",
            "Epoch 7/15\n",
            "102/102 [==============================] - 1s 12ms/step - loss: 0.0177 - accuracy: 0.9994\n",
            "Epoch 8/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0124 - accuracy: 0.9997\n",
            "Epoch 9/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0090 - accuracy: 1.0000\n",
            "Epoch 10/15\n",
            "102/102 [==============================] - 1s 9ms/step - loss: 0.0071 - accuracy: 1.0000\n",
            "Epoch 11/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0057 - accuracy: 1.0000\n",
            "Epoch 12/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0050 - accuracy: 1.0000\n",
            "Epoch 13/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0041 - accuracy: 1.0000\n",
            "Epoch 14/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0033 - accuracy: 1.0000\n",
            "Epoch 15/15\n",
            "102/102 [==============================] - 1s 8ms/step - loss: 0.0029 - accuracy: 1.0000\n",
            "26/26 [==============================] - 0s 5ms/step\n",
            "Accuracy is: 91.60493827160494%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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/zqabbc5Fl16edayie+qJ+rGLEXzMrOQiYqKkXYAhwInArcDwdPWNCzheBnAt8B9JY4HZwCER8ZOkG0l2N46RNItkkMk16WMGAstExP8Knqd/uu0bEdFHUrUZJA1LB308Dvy74PELer06dcHZpzH69RF8P3kyvXvsyKF9j+XVYUP57JOPUQPRYdnl+eOZ52YRbZHcdtEBdNtkNZZeoiXvP3I2F/V/im26rMaGayxPBHzy5UT6XXb/3O233rgTn389mY/H17s9vQt13lmnMvr1EUyePJm9dtuBw/seR+u2bfnnFZcwedJETjvpWNZYcy3+cc0NWUe1Gpg+fRrDX3uZM845P+soACg51GLlQtI1wKiIuCnDGPHVD7MyfPnSWbZN0sFtvvlpGScpjemvXQHAN1PqbA91nVqmdSOm/DQn6xgl07ppAyZNq8g6Rkks2aIhJIPWquWeWRmR9DowFfjjwrY1MysnLmZlJCK6ZJ3BzCwLHgBiZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma552JmZma5p4jIOoOVH/9SmVkpaH4r3DMzM7Pca5R1ACtP02dlnaA0mjdO/v1++pxsg5RI2+bJ99sl+tyRcZLSmDzwQKbOLN8dBy2bqOz/782Pe2ZmZpZ7LmZmZpZ7LmZmZpZ78z1mJmkKP49KqxxBEun9iIg2Jc5mZmZWI/MtZhHRui6DmJmZLaoa7WaUtLWkQ9P7S0vqVNpYZmZmNbfQYibpPOBPwJlpUxOgPMftmplZLtWkZ7YXsAcwFSAixgPeBWlmZvVGTYrZzEjmvAoASS1LG8nMzKx2alLM/ivpemAJSUcCzwA3lDaWmZlZzS10OquI+Juk7sAPwJrAuRHxdMmTmZmZ1VBN52YcCzQn2dU4tnRxzMzMaq8moxmPAIYDewO9gVclHVbqYGZmZjVVk57ZacDGEfEdgKSlgJeBm0sZzMzMrKZqMgDkO2BKwfKUtM3MzKxeWNDcjKekd98HXpP0MMkxsz2BMXWQzczMrEYWtJux8sToD9JbpYdLF8fMzKz2FjTR8AV1GcTMzGxRLXQAiKRlgNOB9YBmle0RsUMJc5mZmdVYTQaADATGAZ2AC4CPgRElzGRmZlYrNRmav1RE3CTpxIh4EXhRkovZQkgKYGBEHJguNwK+BF6LiJ4LeFxnYPmIeGwxXvvHiGi1CI87CegfEdPS5ceAAyJi8qJmKaXzzjmTIUNeoF27pbj/oUezjlN0U374gYsv/DMfvP8ekjjn/L+w4UYbZx2rVq45cgt+u/GKfPPDDLY6I/mMzu69Ebt1WZE5EXzzwwyOve4Vvpo8na3X6cDAU7bl029+BOCREZ9x+YP5nKPhp59+4ohDDmTmzJlUVFSwY/edOea4E7KOVTT18f9eTXpms9J/v5TUQ9LGQLsSZioXU4H1JTVPl7sDX9TgcZ2B3UqWasFOAlpULkTEbvW1kAHs0Wtvrr3uxqxjlMzfL7+ELbbamnsfeoyB/32QTp1WyzpSrd059EN6X/7cPG1XDX6H35w5mG5nPcaTo77g9L03mLvulXe/pttZj9HtrMdyW8gAmjRpwvU33co99z/MXfc+yCvDXmLMm6OzjlU09fH/Xk2K2V8ktQX+CJwK3AicXNJU5eMxoEd6//fAXZUrJLWUdLOk4ZJGSdpTUhPgQmB/SaMl7S9pM0mvpNu8LGmt9PGHSHpY0guS3kuvOzcPSa0kPSvpDUljJe1Z8NqDJb0p6a30dU4Algeel/R8ut3HkpZO7/9B0pj0MQNK+DOrsS5dN6VN27ZZxyiJH6dMYdQbI9lzr94ANG7chNZt2mScqvZeHvc1k378aZ62KdNnzb3fomkjIuo6VelJokWL5AIjs2fPZvbs2UjKOFXx1Mf/ezWZaLiyD/k9sH1p45Sdu4FzJT0KbEgya0q3dN3ZwHMRcZikJUimDHsGOBfoGhHHA0hqA3SLiNmSdgIuAfZJn2MzYH1gGjBC0uCIGFnw+jOAvSLih7QovSppELALMD4ieqSv0TYivk/PLdw+Ir4tfBOS1gPOAbaKiG8luWdeYuO/+Jwll2zHheeexXv/9y5rr7sufzz9LJo3b7HwB+fAOftuxO+6rcoP02ax+8U/z1u+2erL8NIlPfhy8jT+PPANxn3xfYYpF09FRQV99t+Hzz79lP1+dwAbbLhR1pHK2nx7ZpKulnTV/G51GTKvImIM0JGkV1b1GNjOwBmSRgMvkIwUXbmap2kL3CvpLeBKklGllZ6OiO8iYjrwALB1lccKuETSGJJCuQLQgWSy6O6S/iqpW0Qs7C/GDsC9lUUuIiYuZHtbTLMrKnh33Dvss9/vuOOeB2jerAW33Vw+V176y71vsv4JD3Lvyx/Rd+e1AHjz44lscOKDbH3WYPo/+S4DT9k245SLp2HDhtx930M88cwLvP3WGN5/7/+yjlTWFrSbcSTw+gJuVjODgL9RsIsxJWCfiOic3laOiP9V8/iLgOcjYn1gdwpOjyC9YOoClvsAywBdIqIzMAFoFhH/B2xCUtT+IuncRXlj87wZqa+kkZJG9u/ff3Gf7levfYcOtG/fgfU3SL7N79B9Z9793zsZpyq+e4d9xO6bJt/hpkyfxdSfZgPw9JvjadywAe1aNc0yXlG0btOGrptuzsvDhmYdpawt6KTp2+oySBm7GZgcEWMlbVfQ/iTQT1K/iAhJG0fEKJK5L1sXbNeWnweOHFLlubunu/ymA72AqlczaAt8HRGzJG0PrAIgaXlgYkTcIWkycES6feVrf1vleZ4DHpT0j4j4TlK7qr2ziOgPVFaxKDgsYotg6aWXof2yy/HJxx+xSsdOjHjtVTqtunrWsYpi1Q6t+XBCMt3rbl1W4r0vkx0D7ds24+vvZwCwyapLIYmJVY635cWkiRNp1KgRrdu0YcaMGbz66sscctgRC3+gLbKaXs/MFlFEfA5Ut1v2IuCfwBhJDYCPgJ7A8/y8+/FS4HLgNknnAIOrPMdw4H5gReCOKsfLIDlH8BFJY0l62uPS9g2AKyTNIRmtekza3h94QtL4iJh7fDQi3pZ0MclpGRXAKH5ZWOvcGaedwsgRw5k8eRI777gNxxzbj7322TfrWEVz2p/O5s9nncbsWbNYfoWVOPfCi7OOVGs3Hrc1W6/TgaVaN+Xtq/fisvvG0L3zCqy+XBsigs++ncrJN78GwJ6brcxhO61JRUUwfdZsDr8mvz2Zb775hvPOOYOKigoigu4778I225bPkIP6+H9PUY5DiX4FJB1CwUCReqZse2bNGyf/fj99TrZBSqRt8+TIwxJ97sg4SWlMHnggU2eW79+8lk1Emf/fm++Q0JoMzTczM6vXanKl6TXTc5XeSpc3THd5WYYi4tZ62iszM6tzNemZ3QCcSToTSDrc/HelDGVmZlYbNSlmLSJieJW22aUIY2ZmtihqUsy+lbQa6TlMknqTTJhrZmZWL9RkaP5xJEO215b0BckQ8gNLmsrMzKwWajI344fATpJaAg0iYkrpY5mZmdVcTa40fW6VZQAi4sISZTIzM6uVmuxmnFpwvxnJLBXVzSFoZmaWiZrsZvx74bKkv5HMK2hmZlYvLMoMIC1I5gI0MzOrF2pyzGwsP19apCHJJUV8vMzMzOqNmhwz61lwfzYwISJ80rSZmdUbCyxmkhoCT0bE2nWUx8zMrNYWeMwsIiqAdyWtXEd5zMzMaq0muxmXBN6WNJyCYfoRsUfJUpmZmdVCTYrZn0uewszMbDHUpJjtFhF/KmyQ9FfgxdJEMjMzq52anGfWvZq2XYsdxMzMbFHNt2cm6RjgWGBVSWMKVrUGhpU6mJmZWU0taDfjncDjwKXAGQXtUyJiYklTmZmZ1cJ8i1lEfA98D/y+7uKYmZnVniJi4VuZ1Y5/qcysFDS/FYsy0bCZmVm9UpOh+Wa1Nm1meXbOWjRJvhjOKNPZSZulfxGm/DQn2yAl0rppA5pvfHzWMUpm+qhrmD4r6xSl0bzxgte7Z2ZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYmZmZrnnYpYhSRWSRkt6S9K9kloU4Tm3k/RoMfLN5/kPkXRNqZ6/Nr766kuOPOwP7L1nD/bp1ZM777g960hFNWzoEPbo8Vt67tKdm27on3WckqioqOCA/fbmpOOPzjrKIrnuvD588uyljLz3rHnaj/ndtox+4Bxev+9sLj5xTwDatW3JE/1P4Jthf+fKP+2bRdyiOe+cM9l+my3Zp1fPrKPM5WKWrekR0Tki1gdmAjX6Hy2pUWlj5UPDhg055dQ/8cDDg7l94N3cc/dAPvjg/axjFUVFRQWXXHwh1153Iw8OGswTjz3KB++Xx3srdNfAAXTqtGrWMRbZgEdeZc/j/j1P2zZd16Dndhuw2f6X0aX3xfzz9mcBmPHTLC689lHOvPLBLKIW1R699uba627MOsY8XMzqj6HA6pJ2l/SapFGSnpHUAUDS+ZIGSBoGDJDUUdJQSW+kt60KnquNpMGS3pV0naQG6XP8R9JISW9LuqByY0mXSXpH0hhJf0vbqs1RnyyzTHvWWXc9AFq2bEWnTqvxzYQJGacqjrfGjmGllVZhxZVWonGTJuyyWw9eeP7ZrGMV1YSvvmLYkBfptXfvrKMssmFvfMDE76fN09Z332787ZanmTlrNgDfTPoRgGkzZvLy6A+Z8dOsOs9ZbF26bkqbtm2zjjEPF7N6IO1p7QqMBV4CtoiIjYG7gdMLNl0X2Ckifg98DXSPiE2A/YGrCrbbDOiXbr8asHfafnZEdAU2BLaVtKGkpYC9gPUiYkPgL+m2C8pR74z/4nPeHfc/1t9wo6yjFMXXEyaw7HLLzl1u36EDE8qkUFf6++WXcsIpp6IG5fVnaPVV2vObjVdjyO2n8tSNJ9Jl3ZWzjvSrUF6/RfnTXNJoYCTwKXATsCLwpKSxwGnAegXbD4qI6en9xsAN6Xb3khSuSsMj4sOIqADuArZO2/eT9AYwKn3edYHvgRnATZL2Biq/Zi4oR70ybdpUTj35BE7905m0atUq6zhWA0NffJ527drN7VmXk0YNG9CubUu2+cPfOOvKh7jj8sOyjvSr4GKWrcpjZp0jol9EzASuBq6JiA2Ao4BmBdtPLbh/MjAB2AjoCjQpWBdVXickdQJOBXZMe2CDgWYRMZukJ3cf0BN4In3MgnL8gqS+6S7Mkf37191ghVmzZnHqySewa4/d2XGnnevsdUutfYcOfPXlV3OXv54wgQ4d6t2e3kX25uhRDHnheXbfZUfOPv2PjBj+Gn8+s153/mvsiwmTeejZ0QCMfPsT5swJll7SX7JKzQMJ6p+2wBfp/YMXst3nETFH0sFAw4J1m6XF6xOSXZD9gTYkxfD79PjXrsALkloBLSLisfR43Ie1zAFARPRPXwcgps2sWk+LLyK44Lxz6LTqahx08KElf726tN76G/Dppx/z+eef0aF9B554bDCXXvH3rGMVzfEnnsLxJ54CwMgRw7njtpu56NLLM05VHI+8MIZtN12TISPfY/WV29OkcSO+TY+bWem4mNU/5wP3SpoEPAd0ms921wL3S/oDSW+qsNc2ArgGWB14HngwLXqjgHHAZ8CwdNvWwMOSmgECTqlljsyMHvUGgx95mDXWWJP9e/cC4PgTTqbbNttmnGzxNWrUiDPPPpdj+h7BnDkV9NprH1ZffY2sY1kVt116CN26rMHSS7Ti/Scu4qLrHuO2h17h+vP7MPLes5g5q4Ijzh0wd/txgy+gdctmNGnciN2335Cex/6bcR9+tYBXqJ/OOO0URo4YzuTJk9h5x2045th+7LVPtqcbKKL036DtV6dOemZZaNFEAMyYnXGQEmmWfr2d8tOcbIOUSOumDWi+8fFZxyiZ6aOuYXr+B0tWq/rGO14AACAASURBVHljIPnCXS0fMzMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xzMTMzs9xTRGSdwcqPf6nMrBQ0vxWN6jKF/XrMmJ11gtJolv6PKff3N31WtjlKpXnj8v3sIPn8mu96ZdYxSmL64ycvcL13M5qZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe65mJmZWe6VtJhJOlvS25LGSBotafMSv96+kv4n6XlJXSVdtZDtH5O0xEK2+VjS0tW0HyLpmoU8tqOkA2qWvnQK34Oklxey7RKSjq2bZItv2NAh7NHjt/TcpTs33dA/6zhFVc7vDeC8c85k+222ZJ9ePbOOUhLl8Pldd3J3PrnrKEb+56BfrDtx702Y/vjJLNWm2dy2bhusyKvX9OH16/7AU5fvW5dRS1fMJG0J9AQ2iYgNgZ2Az0r0WpLUADgcODIito+IkRFxwoIeFxG7RcTkUmRKdQRqVcwkNSpNlEREbLWQTZYAclHMKioquOTiC7n2uht5cNBgnnjsUT54//2sYxVFOb+3Snv02ptrr7sx6xglUS6f34Cn32HPcx78RfuKS7dix01W4dMJP8xta9uyKf86fgf2vWAQXY6+nT4XP1qXUUvaM1sO+DYifgKIiG8jYnyVXkJXSS+k98+XNEDSK5Lek3Rk5RNJOk3SiLSHd0Ha1lHSu5JuB94C/gxsDdwk6QpJ20l6NN22laRbJI1Nn2OftL0wy0OSXk97kn1r80Yl3SrpKkkvS/pQUu901WVAt7RXerKkhmm2yvdyVPr47SQNlTQIeCddflHSw+nzXSapj6Th6XtYLX3cMpLuT59vhKTfpO1LSXoqfS83AirI+mPBz+RZSW+kz7lnQebV0sxXzO/nXx+8NXYMK620CiuutBKNmzRhl9168MLzz2YdqyjK+b1V6tJ1U9q0bZt1jJIol89v2FtfMHHKjF+0X37Udpx901CCmNu2/3Zr8fCw9/nsmykAfPP99DrLCVDKXsBTwLmS/g94BrgnIl5cyGM2BLYAWgKjJA0G1gfWADYj+aM8SNI2wKdp+8ER8SqApO2BUyNipKTtCp73z8D3EbFBut2S1bz2YRExUVJzYISk+yPiu1q83+VIiunawCDgPuCMNE/P9HX7pjk2ldQUGCbpqfTxmwDrR8RHafaNgHWAicCHwI0RsZmkE4F+wEnAv4ArI+IlSSsDT6aPOQ94KSIulNSDpMda1Qxgr4j4IS3or6bF9Iw0R+c0885U8/OPiCG1+NmUxNcTJrDscsvOXW7foQNjx4zJMFHxlPN7+zUo58+v5xarMv7bHxn70bfztK+x4pI0atiAJ//am1bNm/Dvh0dx57P/q7NcJStmEfGjpC5AN2B74B5JZyzkYQ9HxHRguqTnSf6Abg3sDIxKt2lF8sf1U+CTykK2EDsBvyvINqmabU6QtFd6f6X0NWpTzB6KiDkkPasO89lmZ2DDgp5b2/R1ZgLDI+Kjgm1HRMSXAJI+IPlyADCW5OdZ+b7WleZ2vNpIagVsA+wNEBGDJVX3fgVckn4xmAOsAFSXe2eq//lnXszMrG41b9qI0/ffjJ5nP/CLdY0aNGCTNTqw6xn30bxpI174x+8YPu5L3v+ilEdyCl6/lE8eERXAC8ALksYCBwOz+Xn3ZrOqD6lmWcClEXF94QpJHYGpxciZ9oR2AraMiGnprs9mVbY5Dqjc9blbNU/zU+Hm83spoF9EPFnN61d9L4XPN6dgeQ4/f24NgC0iYp79AAXFbUH6AMsAXSJilqSP+eXnUZn5Fz//X2yU9Dr7Alx//fX84bBa7aldJO07dOCrL7+au/z1hAl06DC/7xH5Us7v7degXD+/VZdryyrLtmX4tQcCsMLSrXnl6j50O+kuvvj2R76bMoNpP81m2k+zeemtL9iw0zJ1VsxKOQBkLUlrFDR1Bj4BPga6pG37VHnYnpKaSVoK2A4YQbLr7LC0x4GkFSS1r2Wcp4HjCrJV3c3YFpiUFrK1SXZ1ziMi/h0RndPb+Bq+7hSgdcHyk8AxkhqnOdaU1LI2b6SKp0h2OZI+X+f07hDSgSeSdgWq263aFvg6LWTbA6ssIPNCf/4R0T8iukZE1759S1/IANZbfwM+/fRjPv/8M2bNnMkTjw1m2+13qJPXLrVyfm+/BuX6+b398Xes8vvrWfuQm1n7kJv54tspbNlvIBMmTeORVz9gq/WWp2ED0bxpIzZda1nGfTaxzrKVsmfWCrhaydD32cD7JN/c1yEZpHERSa+t0BjgeWBp4KK0aIyXtA7wStrj+BE4EKioRZa/AP+W9Fb6uAuAwn7yE8DRkv4HvAvUZNdlTYwBKiS9CdxKcoyrI/CGkjfzDdBrMZ7/BJL3NYbksxwCHE3y/u6S9DbwMsku2aoGAo+kPeaRwDiAiPhO0rD0Z/V4RJw2n5//14uRuygaNWrEmWefyzF9j2DOnAp67bUPq6++xsIfmAPl/N4qnXHaKYwcMZzJkyex847bcMyx/dhrn7odzl0q5fL53fanXem24Uos3aYZ7w84gosGvMJtT71d7bbvfjaRp0d+zIj/HMScOcGtT77FO5/U5kjN4lFE1T172ZB0PvBjRPwt6yy22GLG7KwjlEaz9Otfub+/6bOyzVEqzRuX72cHyefXfNcrs45REtMfPxnmfwjHM4CYmVn+lXQASG1ExPlZZzAzs3xyz8zMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHLPxczMzHJPEZF1Bis//qUys1LQ/Fa4Z2aloLq8STqqrl/T78/vz+8vk/c2Xy5mVg76Zh2gxPz+8q2c31+9eW8uZmZmlnsuZmZmlnsuZlYO+mcdoMT8/vKtnN9fvXlvHs1oZma5556ZmZnlnouZmZnlnouZmZnlXqOsA5gtCkktgekRMSddbgA0i4hp2SYrDknHAQMjYnK6vCTw+4i4NttkNj+SrmYBs99ExAl1GKekJG0NrBERt0haBmgVER9lmck9M8urZ4EWBcstgGcyylIKR1YWMoCImAQcmWGeopJ0m6QlCpaXlHRzlpmKYCTw+gJuZUHSecCfgDPTpsbAHdklSrhnZnnVLCJ+rFyIiB8ltVjQA3KmoSRFOtxYUkOgScaZimnDqsVa0sZZBlpcEXFb1hnqyF7AxsAbABExXlLrbCO5mFl+TZW0SUS8ASCpCzA940zF9ARwj6Tr0+Wj0rZy0UDSkmmPE0ntKJO/R+lutz8B6wLNKtsjYofMQhXXzIgISZVftFpmHQjK5JfHfpVOAu6VNJ5kAtJlgf2zjVRUfyIpYMeky08DN2YXp+j+Drwi6V6Sz683cHG2kYpmIHAP0AM4GjgY+CbTRMX13/RL1hKSjgQOox78bvqkacstSY2BtdLFdyNiVpZ5rHYkrQtU9laei4h3ssxTLJJej4guksZExIZp24iI2DTrbMUiqTuwM8kXkScj4umMI7lnZvkiaYeIeE7S3lVWrSmJiHggk2BFIum/EbGfpLFUMzKu8o9jXklqExE/pLsVvwLuLFjXLiImZpeuaCq/VH0pqQcwHmiXYZ6ikvTXiPgTyd6Cqm2Zcc/MckXSBRFxnqRbqlkdEXFYnYcqIknLRcSXklapbn1EfFLXmYpJ0qMR0VPSR8xbrEXy+a2aUbSikdQTGAqsBFwNtAEuiIhBmQYrEklvRMQmVdrGZP1Fy8XMcklSp6rntVTXllfVfdOtD99+7ddL0jHAscCqwAcFq1oDwyLiwEyCpXyemeXV/dW03VfnKUqnezVtu9Z5ihKR9GxN2vJI0uWS2khqLOlZSd9IyvQPfZHcCewODEr/rbx1ybqQgY+ZWc5IWhtYD2hb5bhZGwqGQedV4bdfSWMKVrUGhmWTqngkNSM5wX3pdFYTpavaACtkFqy4do6I0yXtBXwM7A0MoR6cWLw4IuJ74Hvg9wCS2pP8n2slqVVEfJplPhczy5u1gJ7AEiTfCitNoTxmyLgTeBy4FDijoH1KmQyOOIrktIrlSWbFqCxmPwDXZBWqyCr/rvYA7o2I7yUtaPtckbQ78A+Sz/BrYBXgfyRfMrPL5WNmlkeStoyIV7LOUUqSNgK6pYtDI+LNLPMUk6R+EXF11jlKQdJlQC+Sk/g3I/ni9WhEbJ5psCKR9CbJKRXPRMTGkrYHDoyIw7PM5WNmllefSXpQ0tfp7X5JK2YdqlgknUBy8m379HaHpH7ZpiqqryqnQJJ0jqQHJG2ysAflQUScAWwFdE3PfZwK7JltqqKaFRHfkczi0iAinge6Zh3KPTPLJUlPk+ySG5A2HQj0iYjqBk7kTnq8bMuImJoutwReyXr4c7FUDuVOZ1//C3AFcG4Z9V7W55fTWd2eXaLikfQMSc/zUmBpkl2Nm0bEVlnmcs/M8qp9RNwSEbPT263AMlmHKiIBFQXLFfx8fKkcVL63HkD/iBhMmUyknM4qf3V62x64HNgj01DFtSfJLtSTSeYL/YB5j19nwgNALK++TYc735Uu/x74LsM8xXYL8JqkB9PlXkDeL5FS6It0fr/uwF8lNaV8vlz3BjYCRkXEoZI6kPORjIUq9xak6s2VAryb0XIpnSHjamBLkpkkXgb6RcRnmQYrovQY0tbp4tCIGJVlnmJKL9ezCzA2It6TtBywQUQ8lXG0xSZpeERsJul1kp7ZFOB/EbF2xtEWi6SXImJrSVOofvaWNhlFA9wzs/xaMSLm2XUj6TdAWRQzSQMi4iDSa0ZVacu99IrgD0hqL2nltHlclpmKaGR64dEbSE4/+BHI/cjbiNg6/Tfza5dVxz0zy6X5zA/3i7a8qvpe0otzjo2IdTOMVTSS9iC5DEzluUorA+MiItNzlYpNUkegTUSMWcim9V46OfR8ZX0epHtmliuStiQZ9ryMpFMKVrUBGmaTqngknQmcBTSX9ENlMzAT6J9ZsOK7CNiCKucqZZypaNLZabYm2R33EpD7YkbSywyS38eVgUnp/SWAT4FO2UUrnwOu9uvRBGhF8kWsdcHtB5ID77kWEZemu3GuiIg26a11RCwVEWdmna+I6uW5SsUg6VqSi3KOBd4CjpL072xTLb6I6JRe1eAZYPeIWDoiliKZkSfzY53ezWi5JGmVvF8OZUEkPUBy9d4nImJO1nmKrb6eq1QMksYB60T6x1VSA+DtiFgn22TFIWlsRGywsLa65p6Z5dU0SVdIekzSc5W3rEMV0bVAH+A9SZdJWmthD8iZenmuUpG8T7IbrtJKaVu5GJ/O2tIxvZ1NcgHSTLlnZrkk6SngHuBUkl06BwPflNv1viS1JTmH7mySkZo3AHek0yRZPSLpEZJjSm2BTYHh6fLmwPCI2C67dMWTDgQ5D9iG5P0NAS7MegCIi5nlkqTXI6JL4RVuJY2IiE2zzlYskpYiGRRxEMk334Ekgwo2yOsfxvmdo1T5b9bnKi0OSdsuaH1EvFhXWeqCpJZVTqDOlEczWl5V9ky+lNSD5I/9AocO50k688daJHNP7h4RX6ar7pE0Mrtki6e+nqNUDOVWrOZH0lYkx3NbASunV3c4KiKOzTSXe2aWR5J6AkNJjkdcTTI0/4KIGJRpsCKRtH06wq9sFcxwEsBL5TLDiaQtSH4n1yEZfdsQmJrnXmchSa+RjBweFBEbp21vRcT6WebyABDLnfQE4jUi4vuIeCsito+ILuVSyFLrprNIACBpSUmZfvMtJknnkszrtxTJaMZbJZ2TbaqiuYbkOOd7QHPgCCD3Q/MLVTNtXEW1G9YhFzPLnYioIL10exk7MiImVy5ExCTK40ralfqQDMU/LyLOIzmBuiym6gKIiPeBhhFRERG3kMxDWS4+S3c1hqTGkk4ludJ0pnzMzPJqmKRrSEY0zj0IHRFvzP8hudJQkgrOVWpImVwiJTWe5FpfM9LlpsAX2cUpqmmSmgCjJV0OfEl5dRyOBv4FrEDymT0FHJdpInzMzHJKUnXHkyIidqjzMCUg6QpgFeD6tOko4LOI+GN2qYpH0kMkw9efJjlm1p1kKPvnABFxQnbpFk96RYcJJF8+TiYZqn9t2luzEnExM6uH0lkjjgJ2TJueBm5Md7HmnqSDF7Q+IurNdbJqI+1B3x4RfbLOUiqSOgH9gI4U7N2rehWLuuZiZrmUXvDwEmD5iNhV0rrAlhFxU8bRiibdVbUWSc/lXZ8onQ+SXgJ2iIiZWWcpBUlvAjeRzD05d6q1rE9NcDGzXJL0OMnVmM+OiI0kNSK5sm+m88MVi6TtSEb7fUxyQvFKwMERMSTDWEUjaQ2SeRnXJTl2BkA6kW2uSbqdZFj+IOY9nvuPzEIVkaTXImLzrHNU5QEglldLR8R/00umEBGzJZXFLrjU34GdI+JdAElrAncBXTJNVTy3kEyJdCXJ1ZgPpXwGSXyQ3hqQXNGh3PxL0nkkAz9+qmzMevCVi5nl1dR0uqfK0X5bAN9nG6moGlcWMoCI+D9JjbMMVGTNI+LZdMTmJ8D5kl4Hzs062OKKiAuyzlBiG5CcRrEDP+9mjHQ5My5mllenkOzGWU3SMGAZyuB6ZgVGSroRuCNd7gPkdhqravyUDnJ5T9LxJEO8W2WcqSgkLQOcDqzHvLtQy2KkLbAvsGp9OyZYLt16+/WZBGxLctXpo0j+cDTNNFFxHQO8A5yQ3t5J28rFiUALkvfWhWRC5T9kmqh4BgLjSK68fAHJcc8RWQYqsrdIri5dr3gAiOVSuktqj4j4Il3eBvh3uQwAKXeS9o2IexfWlkflfkUHSS8AG5IU6MJjZpkOzfduRsuro4GHJO0ObEIyMm63bCMtPkljmfcSKfOo/ONYBs4Eqhau6tryqKyv6EAycKfecTGzXIqIEZJOIBlRNQPYKSK+yThWMfTMOkApSdqV5EvHCpKuKljVBpidTaqi+0t6UdU/8vMVHU7ONlJxpCeFXx8Ra2edpSoXM8uVgqv5VmpBMorxJkmZ7+pYXOnIvnI2nmQgyx7A6wXtU8j5H3xJzUj2GKxOMm/hTRGxfbapiisiKiS9K2nliPg06zyFfMzMcqXcr+Yr6aWI2LrgisxlcyXmQulpBo2AlQtPQcgzSfeQ7GIcCuwKfBIRJ2abqvgkDQE2JplLs/CkcE9nZVZbkloC0yNiTnpC8drA457yKR/SY51/A5pERCdJnYELs/6DuDgkja0cgJTOSDM8IjbJOFbRze8LZdZfJL2b0fJqCNBN0pIkx81GAPuTnI9VFtLL0XdLF4dExJgs8xTZ+cBmwAsAETE6ncA2z+Z+kUpnpMkyS8lExIvplQHWiIhnJLUguZp2pnyemeWVImIasDfJ5TX2BTK9bHsxSTqR5Hyl9ultoKR+2aYqqlkRUXXGlrzvJtpI0g/pbQqwYeV9ST9kHa5YJB0J3MfPlydaAXgou0QJ98wsryRpS5Ke2OFpWzl9OTsc2DwipgJI+ivwCsnouHLwtqQDSC5CugbJydMvZ5xpsURE5r2TOnIcSa/6NYCIeE9S+2wjldd/fvt1OZHkvKQHI+JtSasC1V2wM68EFE6cXJG2lYt+JLO2/EQygfIPwEmZJrKa+qlwKqv0+GDmvWoPALFckrRBRIzNOkepSDoFOBh4MG3qBdwaEf/MLpUZSLocmEwy/Vg/4FjgnYg4O9NcLmaWR5KGkszFeCswsJrjL7kkaaWI+Cy9vwmwdbpqKLBCRDyaWbgikDRoQevzPJrx1yKdIPpwYGeSvQVPRsQN2aZyMbMcS4fkH0oyi/dwkp7LU9mmWjySxgG7RMTHVdoPI7kQ6WqZBCsSSd8An5HsWnyNKrtOsx7ebQsn6cSI+NfC2uqai5nlWjq9Ti/gKpLjLgLOiogHMg22iCTtBvwT6BER76VtZ5AMdNk1Ij7PMt/iSj+v7sDvSSarHQzcFRFvZxrMakzSG1XPn5M0KiI2zioTuJhZTknakKRX1gN4mmTqoDckLQ+8EhGrZBpwMUjakWTYcy/gCJKRYz0iYlKmwYpMUlOSonYFcEFEXJNxJFsASb8HDiDZ9T20YFVrYE5E7JhJsJSH5lteXQ3cSNILm17ZGBHjJZ2TXazFl16B+VCSE4pfBnaIiBnZpiqetIj1IClkHUl61Q8u6DFWL7wMfAksDfy9oH0KkPkJ/e6ZWW6lV/SlTGbLB6DKnIxNSWaVqByWn/u5GSXdTnJy+2PA3RHxVsaRrEy4mFmuKJkj6DySEzcbkvyRnw1cHREXZpnNFk7SHH6enLbwj09ZFOtfA0l7A38lmZlG1JPPzsXMciU9/2pXoG9EfJS2rQr8B3giIq7MMp9ZuZP0PrB7RPwv6yyFXMwsVySNArpHxLdV2pcBnsp6RJVZuZM0LCJ+k3WOqjwAxPKmcdVCBslxs/QaWWZWWiPTa7c9RDIdGQBZnw7jYmZ5M3MR15lZcbQBppHMAFIpgEyLmXczWq5IqqDg6raFq4BmEeHemdmvkIuZmZktlKSrWcDs+BFxQh3G+QXvZjQzs5oYmXWABXHPzMzMFomkZSPiq6xzgC/OaWZmi+6xrANUcjEzM7NFVW+ufu5iZmZmiyrzi3JW8jEzMzNbKEntFrQ+IibWVZbquJiZmdlCSfqIn6/oUKlyOSJi1UyCpVzMzMws93yemZmZ1YqkJYE1gGaVbRExJLtELmZmZlYLko4ATgRWBEYDWwCvADtkmcujGc3MrDZOBDYFPomI7YGNgcnZRnIxMzOz2pkRETMAJDWNiHHAWhln8m5GMzOrlc8lLUFyPbOnJU0CPsk4k0czmpnZopG0LdAWeCIiMr2eoIuZmZktlKQ2EfHD/E6e9knTZmZW70l6NCJ6Vjl52idNm5mZFYtHM5qZWY1J2ktS24LlJST1yjITuGdmZma1IGl0RHSu0jYqIjbOKhO4Z2ZmZrVTXd3I/DQvFzMzM6uNkZL+IWm19PYP4PWsQ7mYmZlZbfQDZgL3pLefgOMyTYSPmZmZWRnIfD+nmZnVf5L+GREnSXqE5PyyeUTEHhnEmsvFzMzMauL29N+/ZZpiPlzMzMysJq4AdgR2i4g/ZR2mKhczMzOrieUkbQXsIelukmms5oqIN7KJlfAAEDMzWyhJvYHDga2BEcxbzCIiMr3StIuZmZnViCQBf46IC7POUpXPMzMzsxqJpPezb9Y5quNiZmZmtfGGpE2zDlGVdzOamVmNSRoHrA58Akzl5+uZbZhpLhczMzOrKUmrVNceEZ/UdZZC3s1oZmY1lhatlYAd0vvTqAe1xD0zMzOrMUnnAV2BtSJiTUnLA/dGxG+yzJV5NTUzs1zZC9iD5HgZETEeaJ1pIlzMzMysdmamQ/QDQFLLjPMALmZmZlY7/5V0PbCEpCOBZ4AbMs7kY2ZmZlY7kroDO6eLT0XE01nmAU80bGZmtTcWaE6yq3FsxlkA72Y0M7NakHQEMBzYG+gNvCrpsGxTeTejmZnVgqR3ga0i4rt0eSng5YhYK8tc7pmZmVltfAdMKViekrZlyj0zMzOrMUm3AxsAD5McM9sTGJPeiIh/ZJHLA0DMzKw2PkhvlR5O/830xGn3zMzMLPfcMzMzsxqT9Dzp7B+FImKHDOLM5WJmZma1cWrB/WbAPsDsjLLM5d2MZma2WCQNj4jNsszgnpmZmdWYpHYFiw1ILgfTNqM4c7mYmZlZbbzOz8fMZgMfA4dnliblYmZmZgslaVPgs4jolC4fTHK87GPgnQyjAZ4BxMzMauZ6YCaApG2AS4HbgO+B/hnmAtwzMzOzmmkYERPT+/sD/SPifuB+SaMzzAW4Z2ZmZjXTUFJlB2hH4LmCdZl3jDIPYGZmuXAX8KKkb4HpwFAASf/f3r2EWlXFcRz/fpOiUlEnPQjilqUlUqIYVhAWItELiYQsCCmsLHoQCUIFBUGGjSIqqYEDKyxLECPsMSgUK+WWZviiBzUICgcWRpP6NzhLOF7UjiZ4D/f3gcPZj/9e/73O5M9a++y9L6Iz1XhS5T6ziIjoiToLOJfO26UPtG2TgDFVNXhSzy3FLCIi+l2umUVERN9LMYuIiL6XYhYRx0Wdra5vy7eoS48SO1594DhyPK0+3uv2ITEr1duOIdeAuuNYzzGGhxSziDiEOupYj6mqdVW17Cgh44FjLmYRvUoxixgh2shjl/qGulNdo57Z9v2oPq8OAvPVuepmdVB9Rx3T4q5vbQwCt3a1vVB9qS2fra5Vt7XPVcAyYKL6tbq8xS1Rt6jb1We62npC3aNuBCb30K9FrZ1t6rsH+9TMUbe29m5q8aPU5V257/u/v22cfClmESPLZODlqroU+J1DR0v7qmo68DHwJDCnrW8FHlNPB14DbgZmAOccIceLwKdVdTkwHfgWWAp8V1XTqmqJOhe4GLgCmAbMUK9RZwC3t203ADN76NN7VTWz5dvJoQ+9HWg5bgRebX24B9hfVTNb+4vUC3rIE8NYbpqOGFl+rqpNbXkV8DDwQltf3b5nAVOATSrAacBm4BLgh6raC6CuAu49TI7rgLsAqupvYL86YUjM3Pb5qq2PoVPcxgJrq+rPlmNdD32aqj5LZypzDLCha9/bVfUPsFf9vvVhLnBZ1/W0cS33nh5yxTCVYhYxsgy9sbR7/UD7FvioqhZ0B6rTTuB58vPlKQAAAUxJREFUCDxXVSuG5Hj0ONpaCcyrqm3qQmB2177D9VfgoarqLnqoA8eRO4aJTDNGjCznq1e25TuAjYeJ+Ry4uj2mCHV0e8rDLmBAndjiFhzmWIBPgMXt2FHqOOAPOqOugzYAd3ddiztPPQv4DJinnqGOpTOl+V/GAr+opwJ3Dtk3Xz2lnfOFwO6We3GLR52kju4hTwxjKWYRI8tu4EF1JzABeGVoQFX9BiwE3lK306YYq+ovOtOK77c/gPx6hByPANeq39B5keOUqtpHZ9pyh7q8qj4E3gQ2t7g1wNj2SKTVwDbgA2BLD316CvgC2ESn4Hb7CfiytXV/68PrdN6/Ndj+ir+CzFL1vTzOKmKEaNNo66tq6kk+lYgTLiOziIjoexmZRURE38vILCIi+l6KWURE9L0Us4iI6HspZhER0fdSzCIiou+lmEVERN/7F2EjPV4gL6l0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Dyskeratotic': {'precision': 0.89937106918239, 'recall': 0.910828025477707, 'f1-score': 0.9050632911392406, 'support': 157}, 'Koilocytotic': {'precision': 0.9181286549707602, 'recall': 0.8177083333333334, 'f1-score': 0.8650137741046832, 'support': 192}, 'Metaplastic': {'precision': 0.8881578947368421, 'recall': 0.9246575342465754, 'f1-score': 0.9060402684563759, 'support': 146}, 'Parabasal': {'precision': 0.9583333333333334, 'recall': 0.9583333333333334, 'f1-score': 0.9583333333333334, 'support': 168}, 'Superficial-Intermediate': {'precision': 0.9125, 'recall': 0.9931972789115646, 'f1-score': 0.9511400651465799, 'support': 147}, 'accuracy': 0.9160493827160494, 'macro avg': {'precision': 0.9152981904446651, 'recall': 0.9209449010605028, 'f1-score': 0.9171181464360426, 'support': 810}, 'weighted avg': {'precision': 0.916408039811852, 'recall': 0.9160493827160494, 'f1-score': 0.9151568519853556, 'support': 810}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the file\n",
        "import joblib\n",
        "# save the model in Google drive\n",
        "joblib.dump(model, 'img_model.pkl')\n",
        "joblib.dump(pipeline, 'pipeline.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPVkWEOINaQh",
        "outputId": "61b144f4-df31-4336-ab34-f682b2c1d568"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
            "...layers\n",
            "......dense\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dense_1\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dropout\n",
            ".........vars\n",
            "......flatten\n",
            ".........vars\n",
            "...metrics\n",
            "......mean\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......mean_metric_wrapper\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "...optimizer\n",
            "......vars\n",
            ".........0\n",
            ".........1\n",
            ".........2\n",
            ".........3\n",
            ".........4\n",
            ".........5\n",
            ".........6\n",
            ".........7\n",
            ".........8\n",
            "...vars\n",
            "Keras model archive saving:\n",
            "File Name                                             Modified             Size\n",
            "variables.h5                                   2023-02-20 20:21:02      5089712\n",
            "config.json                                    2023-02-20 20:21:02         1714\n",
            "metadata.json                                  2023-02-20 20:21:02           64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pipeline.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlflow.end_run()"
      ],
      "metadata": {
        "id": "t-OEmB-vSgp0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# write to an existing experiment\n",
        "experiment = mlflow.get_experiment_by_name('/Users/rachelsickler2024@u.northwestern.edu/cervical_cancer_img')\n",
        "experiment_id = experiment.experiment_id\n",
        "run_name = 'model2'\n",
        "description = 'ResNet50, cnn - corrected confusion matrix'"
      ],
      "metadata": {
        "id": "HoVQOZfc9baj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transfer learning means taking a pre-trained machine learning model and repurposing it for another related task for faster development. It helps achieve higher performance even if the model is trained on a smaller dataset."
      ],
      "metadata": {
        "id": "_03ySpwyDM6T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In short, the residual blocks make it considerably easier for the layers to learn identity functions. As a result, ResNet improves the efficiency of deep neural networks with more neural layers while minimizing the percentage of errors. In other words, the skip connections add the outputs from previous layers to the outputs of stacked layers, making it possible to train much deeper networks than previously possible.\n",
        "Read more at: https://viso.ai/deep-learning/resnet-residual-neural-network/"
      ],
      "metadata": {
        "id": "XXINhQkhI6Et"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with mlflow.start_run(\n",
        "        experiment_id=experiment_id,\n",
        "        run_name=run_name,\n",
        "        description=description,\n",
        ") as run:\n",
        "  # Loading ResNet50 with imagenet weights, include_top means that we're loading model without last fully connected layers\n",
        "  model_resnet       = ResNet50(weights = 'imagenet', include_top = False)\n",
        "\n",
        "  features_resnet       = model_resnet.predict(data, batch_size=32)\n",
        "\n",
        "  extracted_features = []\n",
        "  for f_r,  label in zip(features_resnet, labels):\n",
        "    f_r = f_r.reshape(f_r.shape[0] * f_r.shape[1] * f_r.shape[2])\n",
        "    f_r = np.append(f_r, label)\n",
        "    extracted_features.append(f_r)\n",
        "\n",
        "  extracted_features = np.array(extracted_features)\n",
        "\n",
        "  # Normalization\n",
        "  X = extracted_features[:, 0:-1]\n",
        "  y = extracted_features[:, -1]\n",
        "\n",
        "  scaler = MinMaxScaler()\n",
        "  scaled_X = scaler.fit_transform(X)\n",
        "      \n",
        "  extracted_features = np.array(extracted_features)\n",
        "\n",
        "  # PCA\n",
        "  pca = PCA(n_components = 0.95)\n",
        "  pca.fit(scaled_X)\n",
        "  principal_X = pca.transform(scaled_X)\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(principal_X, y, test_size = 0.2, random_state = 6140)\n",
        "\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Flatten(input_shape=(X_train.shape[1], 1)))\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(rate=0.5))\n",
        "  model.add(Dense(5, activation='softmax'))\n",
        "\n",
        "  #Compilation of the model\n",
        "  model.compile(\n",
        "      loss='sparse_categorical_crossentropy', \n",
        "      optimizer='adam', \n",
        "      metrics=['accuracy']\n",
        "  )\n",
        "\n",
        "  epochs = 15\n",
        "\n",
        "  # Train the neural network\n",
        "  nn = model.fit(X_train, y_train, batch_size=32, epochs=epochs)\n",
        "\n",
        "  # Predict on the test set and determine the accuracy\n",
        "  y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "  print(\"Accuracy is: \" + str(accuracy_score(y_test, y_pred)*100) + '%')\n",
        "\n",
        "  #########################################\n",
        "  # compute and print classification report\n",
        "  confusion = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "  # compute and print classification report\n",
        "  # print(classification_report(y_test, y_pred))\n",
        "  plot = plot_confusion_matrix_labeled(y_test, y_pred, CLASSES_LIST=CLASS_LIST)\n",
        "  plot.savefig(\"confusionMatrix.png\", bbox_inches=\"tight\")\n",
        "  # plot.show()\n",
        "  plot.close()\n",
        "  mlflow.log_artifact(\"confusionMatrix.png\")\n",
        "\n",
        "  report = classification_report(y_test, y_pred, target_names=CLASS_LIST, output_dict=True)\n",
        "\n",
        "  metrics = {}\n",
        "  metrics['Koilocytotic_precision'] = report['Koilocytotic']['precision']\n",
        "  metrics['Koilocytotic_recall'] = report['Koilocytotic']['recall']\n",
        "  metrics['Koilocytotic_f1'] = report['Koilocytotic']['f1-score']\n",
        "  metrics['Koilocytotic_support'] = report['Koilocytotic']['support']\n",
        "  metrics['Metaplastic_precision'] = report['Metaplastic']['precision']\n",
        "  metrics['Metaplastic_recall'] = report['Metaplastic']['recall']\n",
        "  metrics['Metaplastic_f1'] = report['Metaplastic']['f1-score']\n",
        "  metrics['Metaplastic_support'] = report['Metaplastic']['support']\n",
        "  metrics['Dyskeratotic_precision'] = report['Dyskeratotic']['precision']\n",
        "  metrics['Dyskeratotic_recall'] = report['Dyskeratotic']['recall']\n",
        "  metrics['Dyskeratotic_f1'] = report['Dyskeratotic']['f1-score']\n",
        "  metrics['Dyskeratotic_support'] = report['Dyskeratotic']['support']\n",
        "  metrics['Parabasal_precision'] = report['Parabasal']['precision']\n",
        "  metrics['Parabasal_recall'] = report['Parabasal']['recall']\n",
        "  metrics['Parabasal_f1'] = report['Parabasal']['f1-score']\n",
        "  metrics['Parabasal_support'] = report['Parabasal']['support']\n",
        "  metrics['SI_precision'] = report['Superficial-Intermediate']['precision']\n",
        "  metrics['SI_recall'] = report['Superficial-Intermediate']['recall']\n",
        "  metrics['SI_f1'] = report['Superficial-Intermediate']['f1-score']\n",
        "  metrics['SI_support'] = report['Superficial-Intermediate']['support']\n",
        "  metrics['accuracy'] = report['accuracy']\n",
        "  metrics['tp_Koilocytotic'] = confusion[0][0]\n",
        "  metrics['tp_Metaplastic'] = confusion[1][1]\n",
        "  metrics['tp_Dyskeratotic'] = confusion[2][2]\n",
        "  metrics['tp_Parabasal'] = confusion[3][3]\n",
        "  metrics['tp_SI'] = confusion[4][4]\n",
        "  metrics['f1_score'] = f1_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "  mlflow.log_metrics(metrics)\n",
        "\n",
        "  joblib.dump(model, 'model2.pkl')\n",
        "  mlflow.log_artifact('model2.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0x5jWKnFTsx3",
        "outputId": "ec2df961-b2c8-44ee-809b-ba86af2bc6db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127/127 [==============================] - 54s 407ms/step\n",
            "Epoch 1/15\n",
            "102/102 [==============================] - 1s 7ms/step - loss: 0.9039 - accuracy: 0.7601\n",
            "Epoch 2/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.2415 - accuracy: 0.9515\n",
            "Epoch 3/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.1049 - accuracy: 0.9821\n",
            "Epoch 4/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0584 - accuracy: 0.9926\n",
            "Epoch 5/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.0376 - accuracy: 0.9957\n",
            "Epoch 6/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0247 - accuracy: 0.9978\n",
            "Epoch 7/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.0169 - accuracy: 0.9994\n",
            "Epoch 8/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0126 - accuracy: 0.9997\n",
            "Epoch 9/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 1.0000\n",
            "Epoch 10/15\n",
            "102/102 [==============================] - 0s 5ms/step - loss: 0.0076 - accuracy: 1.0000\n",
            "Epoch 11/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 1.0000\n",
            "Epoch 12/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0055 - accuracy: 1.0000\n",
            "Epoch 13/15\n",
            "102/102 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 1.0000\n",
            "Epoch 14/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0039 - accuracy: 1.0000\n",
            "Epoch 15/15\n",
            "102/102 [==============================] - 1s 5ms/step - loss: 0.0032 - accuracy: 1.0000\n",
            "1/1 [==============================] - 0s 57ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023/01/29 16:01:28 WARNING mlflow.utils.requirements_utils: Found jaxlib version (0.3.25+cuda11.cudnn805) contains a local version label (+cuda11.cudnn805). MLflow logged a pip requirement for this package as 'jaxlib==0.3.25' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - 0s 2ms/step\n",
            "Accuracy is: 91.48148148148148%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# API"
      ],
      "metadata": {
        "id": "zRmQJQVQ_cIg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get model from MLFlow"
      ],
      "metadata": {
        "id": "3_VY4vocJAPn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logged_model = 'runs:/a6577ce929c841ffbcf3163b34728fe8/model'\n",
        "\n",
        "# # Load model as a PyFuncModel.\n",
        "# loaded_model = mlflow.pyfunc.load_model(logged_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXPjhrNIIu5G",
        "outputId": "4ef6c0d3-8306-4de2-d388-1b12ae9d4df2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023/02/19 19:31:30 WARNING mlflow.pyfunc: Detected one or more mismatches between the model's dependencies and the current Python environment:\n",
            " - fsspec (current: 2023.1.0, required: fsspec==2022.11.0)\n",
            " - google-cloud-bigquery-storage (current: 2.18.1, required: google-cloud-bigquery-storage==2.17.0)\n",
            " - google-cloud-bigquery (current: 3.4.2, required: google-cloud-bigquery==3.4.1)\n",
            " - tensorflow (current: 2.11.0, required: tensorflow==2.9.2)\n",
            "To fix the mismatches, call `mlflow.pyfunc.get_model_dependencies(model_uri)` to fetch the model's environment and install dependencies using the resulting environment file.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mlflow.pyfunc.get_model_dependencies(logged_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "DREUatsITbcX",
        "outputId": "a397652c-8ff5-46c8-def3-7d824a7e6852"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023/02/19 19:31:42 INFO mlflow.pyfunc: To install the dependencies that were used to train the model, run the following command: '%pip install -r /tmp/tmp4yxswq8l/model/requirements.txt'.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/tmp/tmp4yxswq8l/model/requirements.txt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -r /tmp/tmp4yxswq8l/model/requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jSvSJrCcTpF4",
        "outputId": "dbeda009-ad39-46d2-9253-c607b332f598"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: jaxlib==0.3.25 in /usr/local/lib/python3.8/dist-packages (from -r /tmp/tmp4yxswq8l/model/requirements.txt (line 16)) (0.3.25+cuda11.cudnn805)\n",
            "Requirement already satisfied: oauth2client==4.1.3 in /usr/local/lib/python3.8/dist-packages (from -r /tmp/tmp4yxswq8l/model/requirements.txt (line 17)) (4.1.3)\n",
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            "Collecting tensorflow==2.9.2\n",
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            "Requirement already satisfied: numba in /usr/local/lib/python3.8/dist-packages (from shap<1,>=0.40->mlflow<3,>=2.1->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 1)) (0.56.4)\n",
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            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard<2.10,>=2.9->tensorflow==2.9.2->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 23)) (0.6.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard<2.10,>=2.9->tensorflow==2.9.2->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 23)) (0.4.6)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.8/dist-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow<3,>=2.1->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 1)) (5.0.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow==2.9.2->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 23)) (1.3.1)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->ipython~=7.9.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.8.3)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.8/dist-packages (from jupyter-core>=4.6.1->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (3.0.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython~=7.9.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.2.6)\n",
            "Requirement already satisfied: ptyprocess in /usr/local/lib/python3.8/dist-packages (from terminado>=0.8.3->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.7.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.8/dist-packages (from argon2-cffi->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (21.2.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.8/dist-packages (from nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.6.0)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.8/dist-packages (from nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (6.0.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (1.5.0)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.8/dist-packages (from nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.7.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (4.3.3)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (2.16.2)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba->shap<1,>=0.40->mlflow<3,>=2.1->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 1)) (0.39.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (22.2.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.19.3)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.8/dist-packages (from bleach->nbconvert->notebook~=6.3.0->google-colab==1.0.0->-r /tmp/tmp4yxswq8l/model/requirements.txt (line 14)) (0.5.1)\n",
            "Installing collected packages: keras, flatbuffers, tensorflow-estimator, packaging, keras-preprocessing, jedi, fsspec, tensorboard, tensorflow, google-cloud-bigquery-storage, google-cloud-bigquery\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.11.0\n",
            "    Uninstalling keras-2.11.0:\n",
            "      Successfully uninstalled keras-2.11.0\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 23.1.21\n",
            "    Uninstalling flatbuffers-23.1.21:\n",
            "      Successfully uninstalled flatbuffers-23.1.21\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.11.0\n",
            "    Uninstalling tensorflow-estimator-2.11.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.11.0\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 22.0\n",
            "    Uninstalling packaging-22.0:\n",
            "      Successfully uninstalled packaging-22.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2023.1.0\n",
            "    Uninstalling fsspec-2023.1.0:\n",
            "      Successfully uninstalled fsspec-2023.1.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.11.2\n",
            "    Uninstalling tensorboard-2.11.2:\n",
            "      Successfully uninstalled tensorboard-2.11.2\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.11.0\n",
            "    Uninstalling tensorflow-2.11.0:\n",
            "      Successfully uninstalled tensorflow-2.11.0\n",
            "  Attempting uninstall: google-cloud-bigquery-storage\n",
            "    Found existing installation: google-cloud-bigquery-storage 2.18.1\n",
            "    Uninstalling google-cloud-bigquery-storage-2.18.1:\n",
            "      Successfully uninstalled google-cloud-bigquery-storage-2.18.1\n",
            "  Attempting uninstall: google-cloud-bigquery\n",
            "    Found existing installation: google-cloud-bigquery 3.4.2\n",
            "    Uninstalling google-cloud-bigquery-3.4.2:\n",
            "      Successfully uninstalled google-cloud-bigquery-3.4.2\n",
            "Successfully installed flatbuffers-1.12 fsspec-2022.11.0 google-cloud-bigquery-3.4.1 google-cloud-bigquery-storage-2.17.0 jedi-0.18.2 keras-2.9.0 keras-preprocessing-1.1.2 packaging-21.3 tensorboard-2.9.1 tensorflow-2.9.2 tensorflow-estimator-2.9.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "flatbuffers",
                  "fsspec",
                  "google",
                  "keras",
                  "packaging",
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # save the file\n",
        "# import joblib\n",
        "# # save the model in Google drive\n",
        "# joblib.dump(loaded_model, 'drive/MyDrive/img_model.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puw3HPEtjoRb",
        "outputId": "76b404c5-24d0-45ec-fc37-2552e094c2d9"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
            "...layers\n",
            "......dense\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dense_1\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dropout\n",
            ".........vars\n",
            "......flatten\n",
            ".........vars\n",
            "...metrics\n",
            "......mean\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......mean_metric_wrapper\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "...vars\n",
            "Keras model archive saving:\n",
            "File Name                                             Modified             Size\n",
            "variables.h5                                   2023-02-20 00:11:41      1707400\n",
            "config.json                                    2023-02-20 00:11:41         2379\n",
            "metadata.json                                  2023-02-20 00:11:41           64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/MyDrive/img_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# m = mlflow.artifacts.download_artifacts('runs:/a6577ce929c841ffbcf3163b34728fe8/model2.pkl')"
      ],
      "metadata": {
        "id": "LoQpId9W38H-"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# joblib.dump(m, 'drive/MyDrive/img_model2.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMHEXbu04KNE",
        "outputId": "accf26da-9af0-4bb0-8e73-6d25625eb690"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/MyDrive/img_model2.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Put the API together"
      ],
      "metadata": {
        "id": "iOfm7k8aVjvK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Parameters: an array of images - should really be isolated to a single\n",
        "type of cell for consistency with training data\n",
        "Returns: array of scaled features\n",
        "'''\n",
        "\n",
        "def preprocess_images_for_model(input_imgs):\n",
        "  print(\"preprocessing images for the model\")\n",
        "  data=[]\n",
        "\n",
        "  height = 64\n",
        "  width = 64\n",
        "  channels = 3\n",
        "  classes = 43\n",
        "  n_inputs = height * width * channels\n",
        "\n",
        "  print(\"formatting images\")\n",
        "  for input_img in input_imgs:\n",
        "    try:\n",
        "        image=cv2.imread(input_img)\n",
        "        image_from_array = Image.fromarray(image, 'RGB')\n",
        "        size_image = image_from_array.resize((height, width))\n",
        "        data.append(np.array(size_image))\n",
        "    except AttributeError:\n",
        "        print(\"Something is wrong with the image(s)\")\n",
        "\n",
        "  # Converting data to ndarray\n",
        "  print(\"converting images to array\")\n",
        "  data = np.array(data)\n",
        "\n",
        "  print(\"shape\", data.shape)\n",
        "\n",
        "\n",
        "  # Loading ResNet50 with imagenet weights, include_top means that we're loading model without last fully connected layers\n",
        "  print(\"starting resnet feature extraction\")\n",
        "  model_resnet = ResNet50(weights = 'imagenet', include_top = False)\n",
        "\n",
        "  print(\"resnet feature predict\")\n",
        "  features_resnet = model_resnet.predict(data, batch_size=32)\n",
        "  print(\"features_resnet_shape:\", features_resnet.shape)\n",
        "\n",
        "  print(\"assembling resnet features\")\n",
        "  extracted_features = []\n",
        "\n",
        "  for f_r in features_resnet:\n",
        "    f_r = f_r.reshape(f_r.shape[0] * f_r.shape[1] * f_r.shape[2])\n",
        "    f_r = np.append(f_r, 0)\n",
        "    extracted_features.append(f_r)\n",
        "\n",
        "  extracted_features = np.array(extracted_features)\n",
        "  print(\"extracted features shape:\", extracted_features.shape)\n",
        "\n",
        "  # Normalization\n",
        "  print(\"normalizing features\")\n",
        "  X = extracted_features[:, 0:-1]\n",
        "\n",
        "  print(\"loading pipeline\")\n",
        "  pipeline = joblib.load('pipeline.pkl')\n",
        "  print(\"transforming with pipeline\")\n",
        "  principal_X = pipeline.transform(X)\n",
        "\n",
        "  return principal_X"
      ],
      "metadata": {
        "id": "sFPjgfrD_iIG"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Parameter: Array of unprocessed images with cells isolate\n",
        "Returns: Array of predictions for inputted images\n",
        "'''\n",
        "def predict_imgs(input_images):\n",
        "  print(\"starting predict_imgs function)\")\n",
        "  # make sure the model loads before you go to the trouble of pre-processing\n",
        "  print(\"loading model\")\n",
        "  model = joblib.load('img_model.pkl')\n",
        "  print(\"model:\", model)\n",
        "\n",
        "  # send the images away for preprocessing\n",
        "  print(\"preprocessing images\")\n",
        "  X = preprocess_images_for_model(input_images)\n",
        "\n",
        "  # inferences\n",
        "  print(\"making predictions\")\n",
        "  print(\"X:\", X)\n",
        "  predictions = np.argmax(model.predict(X), axis=1)\n",
        "  \n",
        "\n",
        "  return predictions"
      ],
      "metadata": {
        "id": "TTSQbq4DVoTw"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test it out - get images from dataset\n",
        "images = []\n",
        "\n",
        "path1 = \"im_Dyskeratotic/im_Dyskeratotic/CROPPED/\"\n",
        "path2 = \"im_Koilocytotic/im_Koilocytotic/CROPPED/\"\n",
        "path3 = \"im_Metaplastic/im_Metaplastic/CROPPED/\"\n",
        "path4 = \"im_Parabasal/im_Parabasal/CROPPED/\"\n",
        "path5 = \"im_Superficial-Intermediate/im_Superficial-Intermediate/CROPPED/\"\n",
        "\n",
        "# path = [path1, path2, path3, path4, path5]\n",
        "path = [path1]\n",
        "\n",
        "# print(Class)\n",
        "i = 0\n",
        "for p in path:\n",
        "    Class=os.listdir(p)\n",
        "    for a in Class:\n",
        "      # this is actually getting all the files that end in 'p'\n",
        "        if(a[-1] == 'p'):\n",
        "          try:\n",
        "            images.append(p+a)\n",
        "          except:\n",
        "            print(\"images problems\")\n",
        "    i+=1\n",
        "\n",
        "\n",
        "#### real api for images from the app would start here ####\n",
        "# input to predict_images is an array of images\n",
        "predictions = predict_imgs(images)\n",
        "print(\"predictions:\", predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85q_1CWhYE8F",
        "outputId": "effc33ec-854c-4669-b1d2-6146519e2719"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "starting predict_imgs function)\n",
            "loading model\n",
            "Keras model archive loading:\n",
            "File Name                                             Modified             Size\n",
            "variables.h5                                   2023-02-20 20:21:02      5089712\n",
            "config.json                                    2023-02-20 20:21:02         1714\n",
            "metadata.json                                  2023-02-20 20:21:02           64\n",
            "Keras weights file (<HDF5 file \"variables.h5\" (mode r)>) loading:\n",
            "...layers\n",
            "......dense\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dense_1\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......dropout\n",
            ".........vars\n",
            "......flatten\n",
            ".........vars\n",
            "...metrics\n",
            "......mean\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "......mean_metric_wrapper\n",
            ".........vars\n",
            "............0\n",
            "............1\n",
            "...optimizer\n",
            "......vars\n",
            ".........0\n",
            ".........1\n",
            ".........2\n",
            ".........3\n",
            ".........4\n",
            ".........5\n",
            ".........6\n",
            ".........7\n",
            ".........8\n",
            "...vars\n",
            "model: <keras.engine.sequential.Sequential object at 0x7fa337f8a970>\n",
            "preprocessing images\n",
            "preprocessing images for the model\n",
            "formatting images\n",
            "converting images to array\n",
            "shape (813, 64, 64, 3)\n",
            "starting resnet feature extraction\n",
            "resnet feature predict\n",
            "26/26 [==============================] - 16s 537ms/step\n",
            "features_resnet_shape: (813, 2, 2, 2048)\n",
            "assembling resnet features\n",
            "extracted features shape: (813, 8193)\n",
            "normalizing features\n",
            "loading pipeline\n",
            "transforming with pipeline\n",
            "making predictions\n",
            "X: [[-4.61719178e-01 -1.31568786e+00  6.98095137e-01 ...  1.41159883e-02\n",
            "   3.39670316e-02  3.61611788e-02]\n",
            " [ 6.00696164e-02  3.91550265e+00  6.56537723e-01 ...  5.26635765e-03\n",
            "  -2.54619738e-02 -1.36165200e-01]\n",
            " [ 4.15753238e-01 -4.57591105e-02 -3.18154287e+00 ...  1.97333427e-02\n",
            "   2.07785902e-03 -1.00996574e-01]\n",
            " ...\n",
            " [-1.48225355e+00  8.51786128e-02 -1.61522896e+00 ...  2.24821260e-02\n",
            "   1.25188002e-01  8.09896738e-02]\n",
            " [-9.41792441e-01 -1.15953109e+00 -1.01906215e+00 ... -6.97255997e-02\n",
            "   9.64217283e-02 -1.92108425e-02]\n",
            " [ 1.24726612e+00  8.90235764e-01 -2.85436153e+00 ...  7.49675148e-02\n",
            "   3.68760998e-02 -6.13638656e-02]]\n",
            "26/26 [==============================] - 0s 3ms/step\n",
            "predictions: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0\n",
            " 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## wtf is going onnnnn??"
      ],
      "metadata": {
        "id": "RWj26vAXos_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEJTNr4tRchS",
        "outputId": "e9d19a6b-a6c1-48e1-c94d-ecaf3a8ee29a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_resnet[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTz_6e5_RfTV",
        "outputId": "c62873a8-9f75-4742-ce90-5ae032999fcf"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 2, 2048)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_features[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYWl8JF9Sirw",
        "outputId": "1daa4bb9-fefb-4035-e56d-ee2542a1bbdd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8193,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "principal_X[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNe4H4ZjS-FR",
        "outputId": "352a7e9a-a933-4b76-c028-305d54f03f60"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1643,)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save some images for testing the app"
      ],
      "metadata": {
        "id": "GyP3ICEElqrD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "# shutil.copy(origin, target)\n",
        "\n",
        "shutil.copy('im_Dyskeratotic/im_Dyskeratotic/002.bmp', 'drive/MyDrive/002dyskeratotic.bmp')\n",
        "shutil.copy('im_Dyskeratotic/im_Dyskeratotic/023.bmp', 'drive/MyDrive/023dyskeratotic.bmp')\n",
        "shutil.copy('im_Dyskeratotic/im_Dyskeratotic/068.bmp', 'drive/MyDrive/068dyskeratotic.bmp')\n",
        "shutil.copy('im_Koilocytotic/im_Koilocytotic/005.bmp', 'drive/MyDrive/005Koilocytotic.bmp')\n",
        "shutil.copy('im_Koilocytotic/im_Koilocytotic/041.bmp', 'drive/MyDrive/041Koilocytotic.bmp')\n",
        "shutil.copy('im_Koilocytotic/im_Koilocytotic/096.bmp', 'drive/MyDrive/096Koilocytotic.bmp')\n",
        "shutil.copy('im_Metaplastic/im_Metaplastic/003.bmp', 'drive/MyDrive/003Metaplastic.bmp')\n",
        "shutil.copy('im_Metaplastic/im_Metaplastic/020.bmp', 'drive/MyDrive/020Metaplastic.bmp')\n",
        "shutil.copy('im_Metaplastic/im_Metaplastic/132.bmp', 'drive/MyDrive/132Metaplastic.bmp')\n",
        "shutil.copy('im_Parabasal/im_Parabasal/025.bmp', 'drive/MyDrive/025Parabasal.bmp')\n",
        "shutil.copy('im_Parabasal/im_Parabasal/052.bmp', 'drive/MyDrive/052Parabasal.bmp')\n",
        "shutil.copy('im_Parabasal/im_Parabasal/064.bmp', 'drive/MyDrive/064Parabasal.bmp')\n",
        "shutil.copy('im_Superficial-Intermediate/im_Superficial-Intermediate/002.bmp', 'drive/MyDrive/002Superficial-Intermediate.bmp')\n",
        "shutil.copy('im_Superficial-Intermediate/im_Superficial-Intermediate/021.bmp', 'drive/MyDrive/021Superficial-Intermediate.bmp')\n",
        "shutil.copy('im_Superficial-Intermediate/im_Superficial-Intermediate/067.bmp', 'drive/MyDrive/067Superficial-Intermediate.bmp')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "K3zIJAW6T7xH",
        "outputId": "4af9077d-04d8-4939-aff8-973430a8b795"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'drive/MyDrive/067Superficial-Intermediate.bmp'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    }
  ]
}