[DS312] Data Augmentation_Oversampling [Model ConvNet CNN2].ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["YCIqSOz7VYLe","dYrfW8_2XEzG","q62CCa2vxN6b","xHjldVIgYVPm","7nYPv_s6KWsK"],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["KẾT QUẢ CỦA CÁC MÔ HÌNH SAU KHI XỬ LÍ MẤT CÂN BẰNG:\n","https://drive.google.com/drive/folders/1jmeYUwPDNT5dxC1V9JZ5d1o0Mgmlhgal?usp=sharing"],"metadata":{"id":"OfRIw4iHCeUy"}},{"cell_type":"markdown","source":["Dataset: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database\n","\n","Covid19-XRay-w/-ML-and-DL: https://www.kaggle.com/code/thura1601/covid19-xray-w-ml-and-dl\n","\n","\n","COVID-19 Detection using X-Ray: https://www.kaggle.com/code/chiragbmiskin/covid-19-detection-using-x-ray\n","\n","\n","Tham khảo: https://www.kaggle.com/code/kaledhoshme/probabilistic-u-net-segmentation-ambiguous-images#Introduction"],"metadata":{"id":"sG4bcPq9eTpF"}},{"cell_type":"markdown","source":["# **Library**"],"metadata":{"id":"YCIqSOz7VYLe"}},{"cell_type":"code","source":["from google.colab import files\n","\n","import os\n","import pandas as pd\n","import numpy as np\n","from PIL import Image, ImageOps\n","import plotly.express as px\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import cv2\n","from tqdm import tqdm\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","import time\n","import tensorflow as tf\n","import pathlib\n","\n","import keras\n","from keras import layers\n","from keras.models import Sequential\n","from keras.layers import Dense, Activation, Dropout, Flatten\n","from sklearn.metrics import classification_report\n","from keras.preprocessing.image import ImageDataGenerator\n","\n","from imblearn.under_sampling import RandomUnderSampler\n","from imblearn.over_sampling import RandomOverSampler\n","import torch\n","import torch.nn as nn\n","from imblearn.combine import SMOTEENN\n","from sklearn import metrics\n","\n","from keras.callbacks import EarlyStopping\n","es = EarlyStopping(monitor='val_loss', patience=5)  # Khởi tạo callback 'es'\n","\n","import tensorflow as tf\n","tf.config.run_functions_eagerly(True)\n"],"metadata":{"id":"JtKqDGbDUMYG","executionInfo":{"status":"ok","timestamp":1686289147347,"user_tz":-420,"elapsed":14792,"user":{"displayName":"Nguyệt Nguyễn Thị","userId":"12133902694888389656"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["# **Step 1: Load Data**"],"metadata":{"id":"dYrfW8_2XEzG"}},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"maFCqM_ITwZb","executionInfo":{"status":"ok","timestamp":1686289156619,"user_tz":-420,"elapsed":9275,"user":{"displayName":"Nguyệt Nguyễn Thị","userId":"12133902694888389656"}},"outputId":"7734443f-922a-4269-9857-71b032bc1f42"},"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/python3.10/dist-packages (1.5.13)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2022.12.7)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.15)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"]}],"source":["!pip install kaggle"]},{"cell_type":"code","source":["# Chọn tệp JSON API Token từ máy tính của bạn\n","uploaded = files.upload()\n","\n","# Di chuyển tệp JSON vào thư mục ~/.kaggle/\n","!mkdir -p ~/.kaggle\n","!mv kaggle.json ~/.kaggle/\n","!chmod 600 ~/.kaggle/kaggle.json"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":73},"id":"J3oq49xQVIIt","outputId":"ba0e26c5-da17-4392-916e-11802f2fa160","executionInfo":{"status":"ok","timestamp":1686289170390,"user_tz":-420,"elapsed":13784,"user":{"displayName":"Nguyệt Nguyễn Thị","userId":"12133902694888389656"}}},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-94a14975-00bc-4d5b-8c73-d64bc16a4c73\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-94a14975-00bc-4d5b-8c73-d64bc16a4c73\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. 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data['image_file']"],"metadata":{"id":"hlDD2oefWp3P","executionInfo":{"status":"ok","timestamp":1686289209585,"user_tz":-420,"elapsed":22,"user":{"displayName":"Nguyệt Nguyễn Thị","userId":"12133902694888389656"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"9hrH_cr2qdko","executionInfo":{"status":"ok","timestamp":1686289209585,"user_tz":-420,"elapsed":19,"user":{"displayName":"Nguyệt Nguyễn Thị","userId":"12133902694888389656"}},"outputId":"0add7dff-d152-4c33-b600-6005415d257b"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      image_file  result  \\\n","0  Normal/images/Normal-6673.png  Normal   \n","1  Normal/images/Normal-7877.png  Normal   \n","2  Normal/images/Normal-7578.png  Normal   \n","3   Normal/images/Normal-953.png  Normal   \n","4  Normal/images/Normal-7951.png  Normal   \n","\n","                                                path  \n","0  /content/COVID-19_Radiography_Dataset/Normal/i...  \n","1  /content/COVID-19_Radiography_Dataset/Normal/i...  \n","2  /content/COVID-19_Radiography_Dataset/Normal/i...  \n","3  /content/COVID-19_Radiography_Dataset/Normal/i...  \n","4  /content/COVID-19_Radiography_Dataset/Normal/i...  "],"text/html":["\n","  <div id=\"df-2126efc8-3691-417f-b896-53db92da55c3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_file</th>\n","      <th>result</th>\n","      <th>path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Normal/images/Normal-6673.png</td>\n","      <td>Normal</td>\n","      <td>/content/COVID-19_Radiography_Dataset/Normal/i...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Normal/images/Normal-7877.png</td>\n","      <td>Normal</td>\n","      <td>/content/COVID-19_Radiography_Dataset/Normal/i...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Normal/images/Normal-7578.png</td>\n","      <td>Normal</td>\n","      <td>/content/COVID-19_Radiography_Dataset/Normal/i...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Normal/images/Normal-953.png</td>\n","      <td>Normal</td>\n","      <td>/content/COVID-19_Radiography_Dataset/Normal/i...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Normal/images/Normal-7951.png</td>\n","      <td>Normal</td>\n","      <td>/content/COVID-19_Radiography_Dataset/Normal/i...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2126efc8-3691-417f-b896-53db92da55c3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2126efc8-3691-417f-b896-53db92da55c3 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-2126efc8-3691-417f-b896-53db92da55c3');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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CNN model2\n","'''\n","\n","# Xử lý trước khi đưa vào mô hình\n","X_train, X_test, y_train, y_test = train_test_split(pixel_img, label_img, \n","                                                    test_size=0.2, stratify=label_img)\n","\n","num_classes = len(np.unique(y_train))\n","\n","y_train = tf.keras.utils.to_categorical(y_train)\n","y_test = tf.keras.utils.to_categorical(y_test)\n","\n","X_train = X_train.astype('float32') / 255\n","X_test = X_test.astype('float32') / 255\n","\n","input_shape = (X_train.shape[1], X_train.shape[2], 1)\n","\n","cnn_model2 = keras.Sequential(\n","    [\n","        keras.Input(shape=input_shape),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"valid\", activation=\"relu\"),\n","        layers.MaxPooling2D(pool_size=(2,2)),\n","        layers.Dropout(0.2, seed=235), \n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"valid\", activation=\"relu\"),\n","        layers.MaxPooling2D(pool_size=(2,2)),\n","        layers.Dropout(0.2, seed=235),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"valid\", activation=\"relu\"),\n","        layers.MaxPooling2D(pool_size=(2,2)),\n","        layers.Dropout(0.2, seed=235),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n","        layers.Conv2D(32, kernel_size=(3,3), padding=\"valid\", activation=\"relu\"),\n","        layers.MaxPooling2D(pool_size=(2,2)),\n","        layers.Dropout(0.2, seed=235),\n","        layers.Flatten(),\n","        layers.Dropout(0.5, seed=235),\n","        layers.Dense(512, activation=\"relu\"),\n","        layers.Dense(num_classes, activation=\"softmax\"),\n","    ]\n",")\n","\n","# Áp dụng Data Augmentation lên dữ liệu huấn luyện\n","augmented_data = datagen.flow(X_train, y_train, batch_size=128)\n","\n","# Xác định số lượng mẫu trong từng lớp\n","unique_classes, counts = np.unique(np.argmax(y_train, axis=1), return_counts=True)\n","num_samples = dict(zip(unique_classes, counts))\n","\n","# Tìm lớp có số lượng mẫu lớn nhất\n","max_samples = max(num_samples.values())\n","\n","# Xác định tỷ lệ oversampling cho từng lớp\n","oversampling_ratio = 2\n","sampling_strategy = {}\n","for label, count in num_samples.items():\n","    oversampling_count = int(max_samples * oversampling_ratio)\n","    sampling_strategy[label] = oversampling_count\n","\n","# Làm phẳng dữ liệu hình ảnh thành ma trận 2D\n","X_train_flat = X_train.reshape(X_train.shape[0], -1)\n","\n","over_sampler = RandomOverSampler(sampling_strategy=sampling_strategy)\n","X_oversampling_flat, y_oversampling = over_sampler.fit_resample(X_train_flat, y_train)\n","\n","# Đưa dữ liệu trở lại dạng ban đầu\n","X_oversampling = X_oversampling_flat.reshape(X_oversampling_flat.shape[0], X_train.shape[1], X_train.shape[2], 1)\n","\n","# Biên dịch mô hình\n","cnn_model2.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n","\n","# Huấn luyện mô hình với dữ liệu đã được tăng cường và oversampling\n","history = cnn_model2.fit(X_oversampling, y_oversampling, epochs=50, validation_data=(X_test, y_test), callbacks=[es])"],"metadata":{"id":"K98bM5kjZLlM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"48f6b8dc-e656-455d-dc45-4ba6a4bd8277"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/imblearn/utils/_validation.py:313: UserWarning: After over-sampling, the number of samples (16308) in class 0 will be larger than the number of samples in the majority class (class #0 -> 8154)\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/imblearn/utils/_validation.py:313: UserWarning: After over-sampling, the number of samples (16308) in class 1 will be larger than the number of samples in the majority class (class #0 -> 8154)\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/imblearn/utils/_validation.py:313: UserWarning: After over-sampling, the number of samples (16308) in class 2 will be larger than the number of samples in the majority class (class #0 -> 8154)\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/imblearn/utils/_validation.py:313: UserWarning: After over-sampling, the number of samples (16308) in class 3 will be larger than the number of samples in the majority class (class #0 -> 8154)\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/structured_function.py:254: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["2039/2039 [==============================] - 1473s 722ms/step - loss: 0.7545 - accuracy: 0.6764 - val_loss: 0.5814 - val_accuracy: 0.7758\n","Epoch 2/50\n"," 526/2039 [======>.......................] - ETA: 17:39 - loss: 0.5269 - accuracy: 0.7965"]}]},{"cell_type":"code","source":["loss, acc = cnn_model2.evaluate(X_test, y_test, verbose=0)\n","\n","print(\"Accuracy model8 adam: %.2f%%\" % (100.0 * acc))\n","\n","predictions = cnn_model2.predict(X_test)\n","pd.crosstab(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))"],"metadata":{"id":"tuirOPVuZ2Kg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cnn_model2.save('/content/Model/cnn2_model_data_augmentation_and_oversampling.h5')\n","\n","print(\"Classification report for classifier :\\n\"\n","      f\"{metrics.classification_report(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))}\\n\")"],"metadata":{"id":"b2mx8csOZ1_a"},"execution_count":null,"outputs":[]}]}