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Testing_Evaluation_eff.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/yusufunlu/09dc5ab65ec49e69ec700d7a42889e58/testing_evaluation_eff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "fallen-plasma", | |
| "metadata": { | |
| "id": "fallen-plasma" | |
| }, | |
| "source": [ | |
| "## Important Imports" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "quick-property", | |
| "metadata": { | |
| "id": "quick-property" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "import numpy as np \n", | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import cv2\n", | |
| "import os\n", | |
| "from sklearn.metrics import confusion_matrix\n", | |
| "from keras.models import load_model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Mount colab to parent of Google Drive, so we can reach to project's folder and Google Drive which refers to all the folders I have" | |
| ], | |
| "metadata": { | |
| "id": "eVCWAFva3JnE" | |
| }, | |
| "id": "eVCWAFva3JnE" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "nW7s0gwPlBqD", | |
| "outputId": "b88c315b-4246-4c36-b840-d48f2db5e833" | |
| }, | |
| "id": "nW7s0gwPlBqD", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Mounted at /content/drive\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "This should json file to not expose my credentials" | |
| ], | |
| "metadata": { | |
| "id": "1LT1HRtK3e2o" | |
| }, | |
| "id": "1LT1HRtK3e2o" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "os.environ['KAGGLE_USERNAME'] = \"yusufunlu\" # username from the json file\n", | |
| "os.environ['KAGGLE_KEY'] = \"6d464cc201a36e8743ed8fbaa39b235f\" # key from the json file" | |
| ], | |
| "metadata": { | |
| "id": "s9_9MIcXkkeP" | |
| }, | |
| "id": "s9_9MIcXkkeP", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Data is stored on Kaggle so downloading from kaggle to colab is better than downloading the user's machine and upload it because user's internet is probably slower than colab. On the other hand it is better for automation and escaping from errors. Lastly this method create reproducible codes" | |
| ], | |
| "metadata": { | |
| "id": "RocQCkb93kPR" | |
| }, | |
| "id": "RocQCkb93kPR" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!kaggle datasets download -d hgunraj/covidxct" | |
| ], | |
| "metadata": { | |
| "id": "J-F9lqC-lEWa", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "457cb65c-f009-4095-f72a-773fd3ac33bd" | |
| }, | |
| "id": "J-F9lqC-lEWa", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading covidxct.zip to /content\n", | |
| "100% 28.7G/28.7G [04:27<00:00, 123MB/s]\n", | |
| "100% 28.7G/28.7G [04:27<00:00, 115MB/s]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Data is stored as zip file on kaggle. On the other hand \"!\" means that it is linux command not a python function" | |
| ], | |
| "metadata": { | |
| "id": "PEiSHfKq4FzJ" | |
| }, | |
| "id": "PEiSHfKq4FzJ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!unzip covidxct.zip" | |
| ], | |
| "metadata": { | |
| "id": "qcsCn1Dxl6Dk", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "7fa3a8b8-1d86-4c76-9221-587952efbe00" | |
| }, | |
| "id": "qcsCn1Dxl6Dk", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", | |
| " inflating: 2A_images/volume-covid19-A-0382_ct-0028.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0382_ct-0035.png \n", | |
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| " inflating: 2A_images/volume-covid19-A-0698_ct-0000.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0001.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0002.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0003.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0004.png \n", | |
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| " inflating: 2A_images/volume-covid19-A-0698_ct-0006.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0007.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0008.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0009.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0010.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0011.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0012.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0013.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0014.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0015.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0016.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0017.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0018.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0019.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0020.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0021.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0022.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0023.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0024.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0025.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0026.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0027.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0028.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0029.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0030.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0698_ct-0031.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0699-0021.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0699-0023.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0699-0026.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0699-0040.png \n", | |
| " inflating: 2A_images/volume-covid19-A-0699-0041.png \n", | |
| " inflating: metadata.csv \n", | |
| " inflating: test_COVIDx_CT-2A.txt \n", | |
| " inflating: train_COVIDx_CT-2A.txt \n", | |
| " inflating: val_COVIDx_CT-2A.txt \n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We shouldn't be using magic numbers and magic strings in code for managing those variables and external configurations. File is downloaded into project's root folder in our case which is an upper level of 'MyDrive' folder" | |
| ], | |
| "metadata": { | |
| "id": "FZfy94vD4YVV" | |
| }, | |
| "id": "FZfy94vD4YVV" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#!cp drive/MyDrive/covid/2A_images/ -r ." | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "392Cn3PTFBtg", | |
| "outputId": "eaa8f7ac-9cbf-4f2c-f24f-53678a86e074" | |
| }, | |
| "id": "392Cn3PTFBtg", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "cp: cannot access 'drive/MyDrive/covid/2A_images/': Input/output error\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#!cp drive/MyDrive/covid/test_COVIDx_CT-2A.txt ." | |
| ], | |
| "metadata": { | |
| "id": "gLkECIHvGEE2" | |
| }, | |
| "id": "gLkECIHvGEE2", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dataset_path= '.'" | |
| ], | |
| "metadata": { | |
| "id": "XRx99y2dlLra" | |
| }, | |
| "id": "XRx99y2dlLra", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Below functions used to be in extenal python file then I moved them into this notebook." | |
| ], | |
| "metadata": { | |
| "id": "QHmnQ7xM43T9" | |
| }, | |
| "id": "QHmnQ7xM43T9" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "golden-thread", | |
| "metadata": { | |
| "id": "golden-thread" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import cv2\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import itertools\n", | |
| "SEED =12\n", | |
| "\n", | |
| "def load_labels(label_file):\n", | |
| " \"\"\"Loads image filenames, classes, and bounding boxes\"\"\"\n", | |
| " fnames, classes, bboxes = [], [], []\n", | |
| " with open(label_file, 'r') as f:\n", | |
| " for line in f.readlines():\n", | |
| " #typical line is like \"NCP_341_1834_0022.png 2 0 130 512 429\"\n", | |
| " #clean \"\\n\" character from line(it doesn't change original line of file)\n", | |
| " #split the line into chunks, default delimiter is \" \" space character\n", | |
| " fname, cls, xmin, ymin, xmax, ymax = line.strip('\\n').split()\n", | |
| " #As an example below\n", | |
| " #fname=NCP_341_1834_0022.png,cls=2,xmin=0,ymin=130,xmax=512,ymax=429\n", | |
| " fnames.append(fname)\n", | |
| " classes.append(int(cls))\n", | |
| " bboxes.append((int(xmin), int(ymin), int(xmax), int(ymax)))\n", | |
| " #Below line was different as including windows dataset path in original code\n", | |
| " #by dataset_path it is externalized\n", | |
| " #fnames include path+filename not only file names\n", | |
| " fnames = [dataset_path + \"/2A_images/\" + fname for fname in fnames]\n", | |
| " #create and numy array\n", | |
| " fnames = np.array(fnames)\n", | |
| " classes = np.array(classes)\n", | |
| " #return 3 values as tuple\n", | |
| " return fnames, classes, bboxes\n", | |
| "def index_generator(fnames , SET):\n", | |
| " \"\"\"Generated random index of a particular class\"\"\"\n", | |
| " np.random.seed(SEED)\n", | |
| " index = np.random.randint(1,len(fnames),size = SET)\n", | |
| " return index \n", | |
| "def numberofclasses(classes, index):\n", | |
| " class0 = len((np.where(classes[index]==0))[0])\n", | |
| " class1 = len((np.where(classes[index]==1))[0])\n", | |
| " class2 = len((np.where(classes[index]==2))[0])\n", | |
| " return class0 , class1, class2\n", | |
| "def dataframe_generator(test_index ,classes_test ):\n", | |
| " \"\"\"Returns 1 dataframes of datasets distribution\"\"\"\n", | |
| " index = [\"Normal\" , \"Pneumonia\" , \"COVID -19\"]\n", | |
| " test_DF = numberofclasses(classes_test, test_index)\n", | |
| " #\"Normal\",\"Pneumonia\",\"COVID -19\" used as index\n", | |
| " df = pd.DataFrame({'test': test_DF } , index = index)\n", | |
| " return df\n", | |
| "def data_constructor(filepath, classes , dim_size ,index ,bboxes , interpolation = cv2.INTER_AREA , intensify =False):\n", | |
| " \"\"\"Constructs and splits X and Y for training , validtion and test\"\"\"\n", | |
| " np.random.seed(SEED)\n", | |
| " y = np.array(classes[index])\n", | |
| " x = []\n", | |
| " print(\"filepath: \"+ filepath[0])\n", | |
| " for i in index:\n", | |
| " img = cv2.imread(filepath[i])\n", | |
| " if img is None:\n", | |
| " result = \"Image is empty!!\"\n", | |
| " else:\n", | |
| " result = \"Image is not empty!!\"\n", | |
| " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", | |
| " x1,y1,x2,y2 = bboxes[i]\n", | |
| " img = img[y1:y2,x1:x2]\n", | |
| " img = cv2.resize(img, dim_size , interpolation = interpolation)\n", | |
| " x.append(img)\n", | |
| " \n", | |
| " x = np.array(x)\n", | |
| " if intensify == True:\n", | |
| " x= x/255\n", | |
| " return x , y \n", | |
| "def phase1_labels_updater(efficient_y):\n", | |
| " \"\"\"\n", | |
| " Updates labels for Phase-1 testing\n", | |
| " \"\"\"\n", | |
| " for i in range(len(efficient_y)):\n", | |
| " if efficient_y[i] == 2:\n", | |
| " efficient_y[i] =1 \n", | |
| " elif efficient_y[i]==1:\n", | |
| " efficient_y[i] = 0 \n", | |
| " return efficient_y \n", | |
| "def index_extractor(y_preds, y_test, test_index ,classes_test):\n", | |
| " \"\"\"\n", | |
| " Extracts indexs of all the Original classes and Predicted classes by Phase-1 Model \n", | |
| " \"\"\"\n", | |
| " yclass_0 = np.where(y_preds==0)[0]\n", | |
| " yclass_1 = np.where(y_preds==1)[0]\n", | |
| " class_0 = np.where(y_test==0)[0]\n", | |
| " class_1 = np.where(y_test==1)[0]\n", | |
| " yclass0 = test_index[yclass_0]\n", | |
| " yclass1 = test_index[yclass_1]\n", | |
| " class0 = test_index[class_0]\n", | |
| " class1 =test_index[class_1]\n", | |
| " y00=np.where(classes_test[yclass0]==0)[0]\n", | |
| " y01=np.where(classes_test[yclass0]==1)[0]\n", | |
| " y02=np.where(classes_test[yclass0]==2)[0]\n", | |
| " y20=np.where(classes_test[yclass1]==0)[0]\n", | |
| " y21=np.where(classes_test[yclass1]==1)[0]\n", | |
| " index_00 = yclass0[y00]\n", | |
| " index_01 = yclass0[y01]\n", | |
| " index_02 = yclass0[y02]\n", | |
| " index_20 = yclass1[y20]\n", | |
| " index_21 = yclass1[y21]\n", | |
| " index_0 = np.concatenate((index_00,index_01))\n", | |
| " return index_0,index_02,index_20,index_21\n", | |
| "def plot_confusion_matrix(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.Blues):\n", | |
| " \"\"\"\n", | |
| " This function prints and plots the confusion matrix.\n", | |
| " \"\"\"\n", | |
| " print(cm)\n", | |
| " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", | |
| " plt.title(title)\n", | |
| " plt.colorbar()\n", | |
| " tick_marks = np.arange(len(classes))\n", | |
| " plt.xticks(tick_marks, classes, rotation=45)\n", | |
| " plt.yticks(tick_marks, classes)\n", | |
| " fmt = '.2f' if normalize else 'd'\n", | |
| " thresh = cm.max() / 2.\n", | |
| " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", | |
| " plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\",color=\"white\" if cm[i, j] > thresh else \"black\")\n", | |
| " plt.tight_layout()\n", | |
| " plt.ylabel('True label')\n", | |
| " plt.xlabel('Predicted label')\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "e7da1c96", | |
| "metadata": { | |
| "id": "e7da1c96" | |
| }, | |
| "source": [ | |
| "## Setting up parameters and directories" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "upset-aruba", | |
| "metadata": { | |
| "id": "upset-aruba" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "IMG_HEIGHT = 128\n", | |
| "IMG_WIDTH = 128\n", | |
| "DIM = (IMG_HEIGHT,IMG_WIDTH)\n", | |
| "TEST_SET= 25658\n", | |
| "SEED=12 \n", | |
| "label_file_test = dataset_path+\"/test_COVIDx_CT-2A.txt\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "labeled-location", | |
| "metadata": { | |
| "id": "labeled-location" | |
| }, | |
| "source": [ | |
| "## Loading test data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "leading-neutral", | |
| "metadata": { | |
| "id": "leading-neutral", | |
| "outputId": "52bd02fc-4194-4c49-f40b-e8c7072253d0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 525 | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "FINAL IMAGE COUNT:\n", | |
| "Total Test Images = 25658\n", | |
| "test 12117\n", | |
| "Name: Normal, dtype: int64\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " test\n", | |
| "Normal 12117\n", | |
| "Pneumonia 7571\n", | |
| "COVID -19 5970" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-ab093ca9-37bc-4b66-b06e-cded219de74c\">\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>test</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Normal</th>\n", | |
| " <td>12117</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Pneumonia</th>\n", | |
| " <td>7571</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>COVID -19</th>\n", | |
| " <td>5970</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab093ca9-37bc-4b66-b06e-cded219de74c')\"\n", | |
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| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
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| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
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| " border-radius: 50%;\n", | |
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| "\n", | |
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| " background-color: #E2EBFA;\n", | |
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| " fill: #174EA6;\n", | |
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| "\n", | |
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| " [theme=dark] .colab-df-convert:hover {\n", | |
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| "\n", | |
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| " const buttonEl =\n", | |
| " document.querySelector('#df-ab093ca9-37bc-4b66-b06e-cded219de74c button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
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| " const element = document.querySelector('#df-ab093ca9-37bc-4b66-b06e-cded219de74c');\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": {}, | |
| "execution_count": 78 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "fnames_test , classes_test , bboxes_test = load_labels(label_file_test)\n", | |
| "test_index = index_generator(fnames_test , TEST_SET)\n", | |
| "df = dataframe_generator(test_index,classes_test)\n", | |
| "df.plot.bar( title = \"ORIGNAL\" );\n", | |
| "print(\"FINAL IMAGE COUNT:\")\n", | |
| "print(\"Total Test Images = \",len(test_index))\n", | |
| "print(df.loc[\"Normal\"])\n", | |
| "df.loc[:]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2a1bc570", | |
| "metadata": { | |
| "id": "2a1bc570" | |
| }, | |
| "source": [ | |
| "### Loading images for Phase - 1 testing" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "intellectual-opposition", | |
| "metadata": { | |
| "id": "intellectual-opposition", | |
| "outputId": "445ea2c5-32c2-45ac-d39d-d2e323903fdc", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "filepath: ./2A_images/NCP_341_1834_0022.png\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "((25658, 128, 128, 3), (25658,))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 79 | |
| } | |
| ], | |
| "source": [ | |
| "efficientnet_x, efficientnet_y = data_constructor(fnames_test,classes_test, DIM , index =test_index , bboxes = bboxes_test)\n", | |
| "efficientnet_x = tf.keras.applications.efficientnet_v2.preprocess_input(efficientnet_x)\n", | |
| "efficientnet_y = phase1_labels_updater(efficientnet_y)\n", | |
| "efficientnet_x.shape , efficientnet_y.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "img = cv2.imread(fnames_test[0])\n", | |
| "if img is None:\n", | |
| " print(\"Image is empty!!\")\n", | |
| "else:\n", | |
| " print(\"Image is not empty!!\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "74C0RA-qKjHn", | |
| "outputId": "b1b315dd-e602-47c4-da43-91e37f64c557" | |
| }, | |
| "id": "74C0RA-qKjHn", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Image is not empty!!\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "d8af4bd4", | |
| "metadata": { | |
| "id": "d8af4bd4" | |
| }, | |
| "source": [ | |
| "### Loading Phase-1 Trained Model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "sixth-visiting", | |
| "metadata": { | |
| "id": "sixth-visiting", | |
| "outputId": "b2bfc452-0f37-4cff-df45-2027960737da", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "802/802 [==============================] - 4616s 6s/step - loss: 3961675.5000 - accuracy: 0.2432\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "phase1_modelv2 = tf.keras.models.load_model(\"eff-2-128.h5\")\n", | |
| "efficientnet = phase1_modelv2.evaluate(efficientnet_x, efficientnet_y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "589d3ac9", | |
| "metadata": { | |
| "id": "589d3ac9" | |
| }, | |
| "source": [ | |
| "### Predicting NON-COVID and COVID-19" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "excellent-california", | |
| "metadata": { | |
| "id": "excellent-california" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "efficient_preds = phase1_modelv2.predict(efficientnet_x)\n", | |
| "efficient_preds = np.rint(efficient_preds).astype(\"int\")\n", | |
| "efficient_preds= efficient_preds.reshape(TEST_SET)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "c65728ba", | |
| "metadata": { | |
| "id": "c65728ba" | |
| }, | |
| "source": [ | |
| "## CONFUSION MATRIX of PHASE -1 " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "29cbe311", | |
| "metadata": { | |
| "id": "29cbe311", | |
| "outputId": "66cc0163-d4ca-4606-860b-9b7a585e02e0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 347 | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[ 444 19244]\n", | |
| " [ 175 5795]]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "cm1 = confusion_matrix(efficientnet_y ,efficient_preds)\n", | |
| "plot_confusion_matrix(cm1,[\"Non-COVID\",\"COVID-19\"] )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm1" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_1TzfFxT4ng5", | |
| "outputId": "2c3a29bd-2824-422c-9c58-e08572e9fd5a" | |
| }, | |
| "id": "_1TzfFxT4ng5", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 444, 19244],\n", | |
| " [ 175, 5795]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 84 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "#261 covid but we estimated wrong and 151 non-covid but predicted wrong " | |
| ], | |
| "metadata": { | |
| "id": "2SmgHdKmU2l5" | |
| }, | |
| "id": "2SmgHdKmU2l5" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "66e22995", | |
| "metadata": { | |
| "id": "66e22995" | |
| }, | |
| "source": [ | |
| "### Extracting index for Phase-2 Testing" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "539e9ede", | |
| "metadata": { | |
| "id": "539e9ede" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "index_0,index_02,index_20,index_21 = index_extractor(efficient_preds, efficientnet_y, test_index ,classes_test)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "f017215a", | |
| "metadata": { | |
| "id": "f017215a" | |
| }, | |
| "source": [ | |
| "### Passing True Positive from result of Phase-1 Testing to Phase-2 model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "06cd2725", | |
| "metadata": { | |
| "id": "06cd2725", | |
| "outputId": "996aa58c-d0a0-4108-ae7e-c80c99c24fe7", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "filepath: ./2A_images/NCP_341_1834_0022.png\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "((444, 128, 128, 3), (444,))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 86 | |
| } | |
| ], | |
| "source": [ | |
| "inception_x, inception_y = data_constructor(fnames_test,classes_test, DIM , index =index_0 , bboxes = bboxes_test)\n", | |
| "inception_x = tf.keras.applications.inception_v3.preprocess_input(inception_x)\n", | |
| "inception_x.shape , inception_y.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!wget https://github.com/sanskar-hasija/COVID-19_Detection/blob/main/models/phase-2.h5" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Pa-qlgDbB4Hw", | |
| "outputId": "9236fbc1-5ccc-41f9-f91d-5c7a3292765f" | |
| }, | |
| "id": "Pa-qlgDbB4Hw", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-05-20 05:03:21-- https://github.com/sanskar-hasija/COVID-19_Detection/blob/main/models/phase-2.h5\n", | |
| "Resolving github.com (github.com)... 140.82.112.4\n", | |
| "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: unspecified [text/html]\n", | |
| "Saving to: ‘phase-2.h5.1’\n", | |
| "\n", | |
| "phase-2.h5.1 [ <=> ] 123.98K --.-KB/s in 0.08s \n", | |
| "\n", | |
| "2022-05-20 05:03:21 (1.60 MB/s) - ‘phase-2.h5.1’ saved [126957]\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!cp drive/MyDrive/covid/phase-2.h5 ." | |
| ], | |
| "metadata": { | |
| "id": "O2vJFu3HCQrU" | |
| }, | |
| "id": "O2vJFu3HCQrU", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "0d34d884", | |
| "metadata": { | |
| "id": "0d34d884", | |
| "outputId": "7ceca292-13f1-4b3b-e89e-8b72bd8e2a9f", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "14/14 [==============================] - 14s 858ms/step - loss: 0.0049 - accuracy: 0.9955\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "phase2_model = tf.keras.models.load_model(\"phase-2.h5\")\n", | |
| "inception = phase2_model.evaluate(inception_x , inception_y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "2a2b5adf", | |
| "metadata": { | |
| "id": "2a2b5adf" | |
| }, | |
| "source": [ | |
| "### Predicting Normal and Pnumonia " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "6e35f28a", | |
| "metadata": { | |
| "id": "6e35f28a" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "inception_preds = phase2_model.predict(inception_x)\n", | |
| "inception_preds = np.rint(inception_preds).astype(\"int\")\n", | |
| "inception_preds = inception_preds.reshape(len(index_0))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "edf34bfa", | |
| "metadata": { | |
| "id": "edf34bfa" | |
| }, | |
| "source": [ | |
| "## CONFUSION MATRIX of PHASE -2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "766ba434", | |
| "metadata": { | |
| "id": "766ba434", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 347 | |
| }, | |
| "outputId": "92af47bd-561e-4964-91da-97b0208ad7ce" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[262 2]\n", | |
| " [ 0 180]]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "cm2 = confusion_matrix(inception_y ,inception_preds )\n", | |
| "plot_confusion_matrix(cm2,[\"Normal\",\"Pnumonia\"] )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm2" | |
| ], | |
| "metadata": { | |
| "id": "fzThVBmE4xoX", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "67324b4f-fa4c-42d0-c404-3b9edd7ea2bc" | |
| }, | |
| "id": "fzThVBmE4xoX", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[262, 2],\n", | |
| " [ 0, 180]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 92 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "#model can distinguish phonemon vs normal better than covid19 distinguish" | |
| ], | |
| "metadata": { | |
| "id": "-Vpi2fP5VLTu" | |
| }, | |
| "id": "-Vpi2fP5VLTu" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "ae69c950", | |
| "metadata": { | |
| "id": "ae69c950" | |
| }, | |
| "source": [ | |
| "### Passing False Positives from Phase-1 to Phase-2 model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "f0963758", | |
| "metadata": { | |
| "id": "f0963758", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "3089cf39-3bd7-442b-abcf-98e1f20abf2d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "filepath: ./2A_images/NCP_341_1834_0022.png\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "((175, 128, 128, 3), (175,))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 93 | |
| } | |
| ], | |
| "source": [ | |
| "FN_x, FN_y = data_constructor(fnames_test,classes_test, DIM , index =index_02 , bboxes = bboxes_test)\n", | |
| "FN_x = tf.keras.applications.inception_v3.preprocess_input(FN_x)\n", | |
| "FN_x.shape , FN_y.shape" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "48f9df72", | |
| "metadata": { | |
| "id": "48f9df72" | |
| }, | |
| "source": [ | |
| "## CONFUSION MATRIX of False Positives of Phase-1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "90aa21cd", | |
| "metadata": { | |
| "id": "90aa21cd", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 365 | |
| }, | |
| "outputId": "85033368-b008-4d56-b0ae-974a6734cd68" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[ 0 0 0]\n", | |
| " [ 0 0 0]\n", | |
| " [ 22 153 0]]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" 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| }, | |
| "metadata": { | |
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| } | |
| } | |
| ], | |
| "source": [ | |
| "FN_preds = phase2_model.predict(FN_x)\n", | |
| "FN_preds = np.rint(FN_preds).astype(\"int\")\n", | |
| "FN_preds = FN_preds.reshape(len(index_02))\n", | |
| "cm3 = confusion_matrix(FN_y,FN_preds)\n", | |
| "plot_confusion_matrix(cm3,[\"Normal\",\"Pnumonia\",\"Covid-19\"] )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm3 = confusion_matrix(FN_y,FN_preds)\n", | |
| "plot_confusion_matrix(cm3,[\"Normal\",\"Pnumonia\",\"Covid-19\"] )" | |
| ], | |
| "metadata": { | |
| "id": "7OrgE_Ts6jjV", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 365 | |
| }, | |
| "outputId": "87e9b035-9166-47de-b3a9-c816b7bf7604" | |
| }, | |
| "id": "7OrgE_Ts6jjV", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[ 0 0 0]\n", | |
| " [ 0 0 0]\n", | |
| " [ 22 153 0]]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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XYo7ufC0EhXRpldQfOBoYGhHrAr2BfYBfAmdHxBrAO8DBHY21luZWJwLn59c2wBnALh09oc27pqYmvn/0kVx7/c08+sQExo0ZzTMTJtQ7rLrwtZijJ1yLAlsF9AEWlNQHWAiYAmwL/ClvHwXs1tE4aymx7kGaZGtqRBwIrA8s3tET2rx76MEHWX31NVh1tdXo27cve+69Dzdcf231D3ZDvhZzdPdrIdXcjrWfpPEVr0MrjxMRk4FfAa+QEup7pFv/dyNiVt5tEqm3aYfUklinR8RsYJakxYA3gJU6ekKbd6+9NpkBA+b8X9C//wAmT55cx4jqx9dijp5wLWrs0jotIoZWvEZ+9hhaEtgVWJXUNn9hYHiRcdYyCMt4SUsAl5Cy+ofAP6t9SFITqVK4D2nQlv0baaBsSbsAgyKie1VEmXVjBXVp/QrwYkS8CSDpGmALYAlJfXKpdQDQ4V+lqiXWiDgiIt6NiIuBr5IS5IE1HHt6RGyQK4dnkAZzaRgRcV1Zk+qKK/Zn0qRXP12ePHkS/ft3+K6l1Hwt5uju10JUrwaosUvrK8BmkhZSamawHTABuJNU9QmwP9DhepS5JlZJG7V8AUsBffL79rgHWEPSMEl3SfqTpGcl/SF/MSS9JKlffj9U0l35/UmSRkm6R9LLknaXdIakJyXdkocyRNJ2kh7N6y+XNH/FcU+W9Ejetk5ef4CkC/L7nSU9kD//N0nLtfP7damhG2/MxIn/4qUXX2TGjBmMGzuGHXfqmc8TfS3m6PbXooZqgFryakQ8QHpI9QjprroXMBL4CXCMpImkJleXdTTUtqoCzmorNtITtKryU7cRwC151YbAYNIQhPeRiuD3VjnM6qQWCYNI1RDfiIgfS/oLsKOkW4ArgO0i4nlJvwcOJ81+AKnOZSNJRwDHktqvVboX2CwiQtIhwI+BH7byXQ4FDgVYaeWVa/n6naJPnz6cfe4F7Lzj9jQ1NbH/AQcxaPDgusVTT74Wc/SEa1FUO9aIOBE4scXqF4BNijh+Wx0EtpnHYy8o6bH8/h5S9t8ceDAiJgHk7QOpnlhvjoiZkp4ktTlrTtJP5s+vTaozeT6vH0UaM7Y5sV6T/30Y2L2V4w8AxkpaAegLvNhaELkSfCTAkCFD5zo1eFcYPmIHho/YoZ4hNAxfizm687UQ0LskXa9qmpqlg6ZHxAaVK/KvzScVq5oqYpjFnKqJloO8fAIQEbMlzYyI5qQ2m9q+Q/M5K89X6Xzg1xFxnaRhwEk1HNPMulhJhgqoredVF3kJGJLff6Odn30OGChpjbz8LeAf7fj84sx5Arh/O89tZl2kl6q/GkEjJdaTgXMljaedkxVGxH9IfX3H5eqC2cDF7TjESfmzDwPT2nNuM+saUjFdWrtC1dvo/NR+P2C1iDhF0srA8hHxYFufi4hFWll3F3BXxfJ3K97fA6zVymdOmttxK7dFxB2kB2MtPz+w4v14YFh+fwXpgRcRcS3z0LTCzLpGSapYayqx/gb4EvDNvPwBcGGnRWRm1ormWVrLMDVLLQ9+Ns1NlR4FiIh3JPXt5LjMzD6nkeou21JLYp0pqTep7SqSliHVYZqZdZkyzSBQyw/AecBfgGUlnUZqc/qLTo3KzKwVRfS86gpVS6wR8Yf8tHw7UjXHbhHxTKdHZmbWQkkKrDW1ClgZ+Bi4vnJdRLzSmYGZmVVqfnhVBrXUsd7InEkFFyCNYfgcqb+/mVnXEPQuydOrWqoCvli5nEe2OqLTIjIzmwu1Z/KVOmr3WAER8YikTTsjGDOzuUlVAfWOoja11LEeU7HYC9iINOSfmVmXKktzq1pKrItWvJ9FqnP9c+eEY2bWum5TYs0dAxaNiGO7KB4zs9Y1UDvVauaaWJsn1ZK0RVcGZGY2N92hudWDpPrUxyRdB4wDPmreGBHXzO2DZmZFE8U1t8ozT18KrEtqTnoQqRnpWNKsJC8Be0XEOx05fi1hLgC8RZrjaidg5/yvmVkXEr1qeNXoXOCWiFgHWB94BjgOuCMi1gTuyMsd0laJddncIuAp5nQQaFbX+Z7MrOcRxdSxSloc2Bo4ACAiZgAzJO1KHq+ZNG/eXaSZW9utrcTaG1gEWv0JcGI1s65V+9Qr/fJMJM1G5olAm60KvAn8TtL6pElGvwcsFxFT8j5TgeU6GmpbiXVKRJzS0QObmRUp1bHWlFmnRcTQNrb3IT0/OioiHpB0Li1u+yMiJHW4ANlWHWs5Hr+ZWY9R0AwCk4BJEfFAXv4TKdG+LmkFgPzvGx2Os41t23X0oGZmnaGI8VgjYirwqqS186rtgAnAdcyZpXl/5mEevLlWBUTE2x09qJlZ0SToXVw71qOAP+Rppl4gzfLcC7ha0sHAy8BeHT14uwdhMTOrl6LSakQ8BrRWD1vInboTq5mVQncb6NrMrCGUI606sZpZaYheJRneyonVzEpB1NYHvxE4sZpZach1rGZmxSpHWnVi7bAZs2bz6lsf1zuMhrDe8B/XO4SG8c5DF9Q7hG6r4HasncqJ1cxKw1UBZmYFK0dadWI1s5IQrgowMytcSfKqE6uZlYVQSSoDnFjNrDRcYjUzK5CbW5mZdYKS5FUnVjMrD9exmpkVKI3HWu8oalOWwWLMzIqaTBAASb0lPSrphry8qqQHJE2UNDZP29KxODv6QTOzrqYa/tcO3wOeqVj+JXB2RKwBvAMc3NE4nVjNrBSaqwKqvWo6ljQA2BG4NC8L2JY0FTbAKGC3jsbqOlYzK4fab/X7SRpfsTwyIka22Occ4MfAonl5aeDdiJiVlycB/TsaqhOrmZVGjQXSaRHR2gys6RjSTsAbEfGwpGHFRPZZTqxmVgoFztK6BbCLpB2ABYDFgHOBJST1yaXWAcDkjp7AdaxmVhqq4VVNRBwfEQMiYiCwD/D3iNgPuBPYI++2P3BtR+N0YjWz0pBU9TUPfgIcI2kiqc71so4eyFUBZlYaRXdpjYi7gLvy+xeATYo4rhOrmZVGSTpeObGaWYmUJLM6sZpZKUiFtQrodE6sZlYa5UirTqxmViYlyaxOrGZWEu0bvaqe3I61JKZMnsS3dh/BiK2GsMPWQxl1yYUA/PLkE9h+yw3ZeZtNOOLAfXj/vXfrHGnnuPjE/Xj5jv9l/LgTPl330+/swL9v/Tn3jzmO+8ccx/ZbDgJg6OBVPl33wNjj2GWb9eoVdpe77dZbWG/w2gxeZw3OPOP0eodTqFo6BzRK2nWJtSR69+nNcSf9gsHrbciHH37A7l/bki223pYtvrwtP/zpKfTp04czT/0Zvz3vV/zov39e73ALd+X193Px2H9w6anf/sz686+6k3OuvOMz657+92tssd8ZNDXNZvl+i/HA2OO58e6naGqa3ZUhd7mmpia+f/SR3Hjz7fQfMIAtN9uYnXbahS8MGlTv0IrTKJmzCpdYS2LZ5VZg8HobArDIIouy+ppr8/rU19hy2Ffo0yf9Pq4/ZBOmTulw9+aGdt8j/+bt9z6uad/p/5n5aRKdv+98RERnhtYwHnrwQVZffQ1WXW01+vbty55778MN13e4V2ZDKng81k7jxFpCk155mQlPPc76G238mfV/Hv17tt72a3WKqj4O22drHhx7PBefuB9LLLrgp+s3XncVHv7TTxk/7gSOPm1Mty+tArz22mQGDFjp0+X+/QcweXL3+qEtajzWztbliVXS8pLGSPq3pIcl3SRprXYeY6ik8+ay7SVJ/VpZv46kf0r6RNKxLbZ9T9JTkp6W9P32faOu9dFHH3LUIftywilnsMiii326/qJzzqB3nz7s8o196hhd17pk3D0M2vkkNt3ndKZOe5/Tj9n9020PPfUyQ/Y4jS3/6wx+dNDXmL+va71Kr0SVrF2aWPMo3X8B7oqI1SNiCHA8sFx7jhMR4yPi6Hae/m3gaOBXLWJaF/h/pD7C6wM7SVqjncfuEjNnzuSog/dl5933Zvsdd/10/TVjruTO22/mrAsvn9dBKErljbc/YPbsICK4/Jr7GLruKp/b57kXX+fDjz9h8Bor1iHCrrXiiv2ZNOnVT5cnT55E//4dHqu5IbkqoHXbADMj4uLmFRHxOHCvpDNzqfFJSXsD5JLtjs37SrpC0h6ShlVMALa0pNtyafNS5vKbFRFvRMRDwMwWm74APBARH+dxGP8B7P65A9RZRHDCDw5n9TXX5qDD5vym3P3327jkwnO4eNTVLLjQQnWMsOst329OiX3Xbddnwr+nALDKikvTu3f60155hSVZe9Xlefm1t+oSY1cauvHGTJz4L1568UVmzJjBuLFj2HGnXeodVmFE6n1V7dUIuvr+aF3g4VbW7w5sQCox9gMeknQ3MBbYC7gxz5i4HXA4sGnFZ08E7o2IU3ISbu8EYE8Bp0laGpgO7ACMb21HSYcChwKsWFGX1RUefvCfXPun0az9hcHsst1mABxz/En8/Gc/YsaMTzhg750B2GDIJpxyRqu1JKU26n8PYKsha9JviUWYeMupnHrxTWw9ZE3WW3sAEcHLU97mqJ+PBmDzDVfj2AO/xsxZTcyeHXzvF2N5692P6vwNOl+fPn04+9wL2HnH7WlqamL/Aw5i0ODB9Q6rUI2SOKtplIqnLYHREdEEvC7pH8DGwM3AuZLmB4YDd0fE9Ba3u1uTS5gRcaOkd9pz4oh4RtIvgduAj4DHgKa57DsSGAnwxfU36tJHzUM33Zznp34+OQz7yvCuDKNu9j/+is+tG/XXf7a67+gbH2L0jQ91ckSNafiIHRg+Yod6h9FpGuVWv5qurgp4GhhS684R8R/SWInbA3uTSrA1kXSkpMfyq80Ktoi4LCKGRMTWpGlvn6/1PGbWdcpSFdDVifXvwPz5lhoASesB7wJ7S+otaRlSKfTBvMtY4EBgK+CWVo55N7BvPtYIYEmAiLgwIjbIr9faCkrSsvnflUml3z92/CuaWWcpS2Lt0qqAiAhJXwfOkfQT4D/AS8D3gUWAx4EAfhwRU/PHbgOuBK6NiBmtHPZkYLSkp4H/A15p7dySlifVnS4GzM7NqgZFxPvAn3Md60zgyIjonv1CzUostaaa98wpaSXg96TWSEGaHvtcSUuRCnIDSXlpr4hoV9Visy6vY82lx71a2fSj/Gq5/0xgqRbr7mLOdApvAVVbxedEPWAu27aq9nkzq7PiSqSzgB9GxCOSFgUelnQ7cABwR0ScLuk44DjSPFjt5p5XZlYaBc3SOiUiHsnvPwCeAfoDuwKj8m6jgN06GmejtAowM6ui5llY+0mqbDI5Mrfo+fwRpYHAhsADwHIRMSVvmko7Oy5VcmI1s9KosSpgWkQMrX4sLQL8Gfh+RLxfmbTz86AON6l0VYCZlUKRQwVImo+UVP8QEdfk1a9LWiFvXwF4o6OxOrGaWXkUkFnzmCWXAc9ExK8rNl0H7J/f7w90eMxFVwWYWWkUNDXLFsC3gCclPZbXnQCcDlwt6WDgZVpvvVQTJ1YzK40i0mpE3NvGobYr4BROrGZWEg3Us6oaJ1YzK4U0bGA5MqsTq5mVRjnSqhOrmZVISQqsTqxmVh5lGY/VidXMSsMlVjOzAjXSeKvVOLGaWWm4KsDMrGjlyKtOrGZWHr2cWM3MiiRXBZiZFSn1vKp3FLVxYjWz0nBiNTMrmKsCzMyK5HasZmbFas/UK/XmxGpmpVGWYQM955WZlUZzt9a2XrUdR8MlPSdpoqTjio7TidXMSqOIWVol9QYuBEYAg4BvShpUZJxOrGZWHsXMf70JMDEiXoiIGcAYYNciw3Qdq5mVgihsltb+wKsVy5OATYs4cDMn1g566olHp621/MIv1zmMfsC0OsfQKBriWiw434X1DqFZI1yPVYo82COPPHzrgvOpXw27LiBpfMXyyIgYWWQs1TixdlBELFPvGCSNj4ih9Y6jEfhafFZ3vB4RMbygQ00GVqpYHpDXFcZ1rGbW0zwErClpVUl9gX2A64o8gUusZtajRMQsSd8FbgV6A5dHxNNFnsOJtdy6tN6owflafJavRxsi4ibgps46viKis45tZtYjuY7VzKxgTqxmZgVzYjUzK5gTazcmabCkgfWOoxGpLMMkdRJJG0r6hqSVJC1a73i6Gz+86sYkjSL9eP4sIurdS6xhSFLkP3S0mxUAAA28SURBVHxJfXN/8R5D0o7AucANwJLAK8AfI+KZugbWjTixdmN5FJ+RQBPwi4h4qb4R1V+LpHoEsCXwb+CuiLijrsF1AUkLAb8BLomI+yRtA5wFPAxcEBGP1zXAbsJVAd1M5S1uRDQB3wHmA37W06sFWkmqewK/AjYCTpW0Zz3j6woR8THQF9gmL98JPA68A2wHIMl5YR75AnYjLRLHppI2johZwMFAkJJroQNjlEUeb/PA/H5RYHFgF2Ar0n8HFwPfk7R73YLsRC3qlC8B1pA0UtJZwBLA7cDXJS0YEbPrEmQ34qqAbkjSD0lJ431S/dmvgRdJt4BLAsdGxKtzP0L3I2lz4DlgKeAl0g/N6qQBj3chleKuBT4C9o6ID+oTafEkbQkMBK6JiI8lLZaXdwU+BM6NiNmSRgM/iYhX6hZsN+ESazcj6evAVyPiy8DzwFeAo0lDuB0BTAVm1S/C+oiI/yN14f4pcBzpb/8jYGZevyPwLLB/N0uq2wB3AzsBe0taOCLej4gnIuLUiDg7J9UDgPWB6fWMt7twibXkKm//8/JGwLvAV4Gvk5LplcAbwAk96cmvpF6Vt7X5Yd52wM7ApIj4paSRwHLAF4A9IuKJ+kRbvHz7vwewDGls1i2Bx4AxueSqiAhJOwFnAntGxFP1i7j78CAsJdaiTnUQ8EJEPJKX1wfOiogXJN1JGn/yzfpF2/Wak6qkA4GFgFcj4jpJM0jzHB0ZEYdKWgLoHRFv1TPeouWkeTMwPSKaJC0MbAD0kjQ6Ij7K+90g6dGIKHRM0p7MibXEKpLqUcAhwDRJvwbuACYAZ0u6GtieVG9Y7xHlu1wujR0HXAV8VdIXI+I0SQEcKmm+iDinvlF2noj4sGJxFGmGk/WBNyStAfwnIn7jpFosJ9YSalFSXRbYHPgyqfnQHsCiwF+B94BhpHrDF+oTbf1I+hbwJeAbEfFULsWfLOmEiPiFpCZgYn2j7BrN1SKSriDNTno8sBbpR9cK5sRaQhVJ9TukJDp/RLwLXJKTxdfyulGS/pjbs3Z7LeubSXWLewP3AE/l14nAOZJmRsSZdQiz00jaGlgrIi7NyyI9R5kNLAB8nJPrAGAwsEVETKhfxN2XWwWUVG5v+V3gY+CLks4GiIjLSVNPbC5psZ6YVCWtnZ9+/xo4CjhJ0kb5WjxFaiUxuo7hdpYm4BeS9mtekRPpNsAfKsYEmAYMc1LtPG4VUBItEseXgf8HjIuIa3OPqsuAxyPimLzP4hHxXr3irZdc37wX6UHdFFIJdQfgJ8CBEfFgHcPrFM2N//PDqu8CJwAnRcRISasClwMXRsSf6hlnT+LEWgItkurupPrUtYAngV9HxNScXP8M3BYRx7dyW9ztSdqU1KvoK8DKpPrVrYB9gR8CuwNbR8QndQuyE1Q0m/o+MJRUt34QqWQ+BlgpIia0bH5mncd1rCVQkVSHk27/t8uv/YERkm6MiJdy5wBVfqY7q0gozT8iCwJPR8QbpKfeU0mJZrPcZvXS7pRUJa0TEc/ma7AS8G3gv3ISHQXcSPpTuBTmND+zzuc61pKQNAw4HHgokr8B15BKr3tIWiYiXokeMjxgixL54vnfR0l94I8AiIhJpHrHtfP2t7s2ys6Tu6XuLWkpgNxF+Umgj6Q+ucrjRGBkdx3/oJE5sTaoFoNmQOrrPwVYLTcbIiL+AtxMapfYo8YUrSjFfxe4RdL/kPr+fx/YQdJ5kg4BhgB3Vn6mm/gA+AUwQNJv87o3SVUeC+flKcAfSKNXWRdyHWsDalGnujOpb/+7wHjSAMVvA2Mj4sm8zyItGoJ3W5X1hJI2JJXKziM9oJpB6hc/ATiGNMDImO7aTTM/mFqO9GPyVET8XNLvSE3wZpB6We3UE9sw15sTawOqqDs8gtSj6mbSg5ffkRLrWcAnwOUR8XT9Iu1aLX5wvkrqprt0RJwpaXVgH9IQeNdFxD11DLXT5R+Vw3OX3EGkwWWejYhT8x3NKqT65n/XNdAeylUBDUTSyrn9ZeQeVXsB+0XET0m9qw4j9a46DegNvF6/aLteRVI9iPT0f0vgl5JWywnkSlJJ7Wu5X3y30UrV0ARgA0nH5vaoZwCrSjo/Ih6PiOucVOvHibVBSFqOVD92eL61f4PUkHsGQES8Q7rl+2JETAF+1EP7/m9FGkd0WEQcBPw3cH9+Qv4KcBFwTuQBRrqLih+VAZKWy60bDgPWlNQfeJr03ZX/lqyOnFgbx5ukHlMrAgfmEspEYIyk5mZxq5AeVvSmB46pmm1KqgLYJVcNnEaqGpkgaa2ImBTdaJSqypKqpMHABcCJknYj/X0sBKwTaaaIR4EfRkSPupNpRK5jrTNJawK9IuK5/B/RTqRBMh7LPWcuIj31f4KUVPbriV0R80O8gRFxvqTDSR0k7omIa/L2Y4AbIuL5esZZpBZ1yotGxAdKU+sMJs0K0fzQbgCwvRNq43AHgTqStDRpupBpkk4mtbkcSWqXuYak70TE4blH0QLALyPixfpF3HVaJBWRnvDvLml6RFwk6WhgK0nzR8ToPC5At1Lx/Y8FtlYaPOVMYBxpoJ1tSZMADiH97ViDcIm1ziRtC/wN+B7wRdKcVB+S6lb7kSZ5+1136jHUHs1NySTNRyqx/zfwp4i4RNJxpFYAp0X3mk5lCOnh5POkGWRPJ3XTHUFKpo9FxEUV+/cPj6faUFxirbOI+Luk7Um3deuT2iVuS2o6tAmp19BoUvOqHiOXUjcHrpD01dxl9wHS0+/TJM2OiNMlLdnNkupw4FTgfOA10tCHz0XE+8BYSW8Bv5H094h4DsBJtfH44VUDiIjbgWNJQ9p9FBGjSAOHfIHUwLtHjFJV+aAmd9u9jzRg99WSVo6ImcB9pF5oeyqN4PVOncItnNKoZecDR0TE73OX3OeA2bk6iNyV+W7S3Yw1KJdYG0RE3ChpNqnp0Je605PtWrSoU92T9EDmTxHxo1z//Gelgb03AmYD3+qGPzhDgAsi4qHc338W6UdkMvD13NRsGrANcFL9wrRqnFgbSETcLKkv8DdJQ3rCaETNpdSKpPpfwA+AF4BNJV0VESdK+pDUjncd4JCI6DYTI1b8qKxKGvIPoCl3331P0pmkAXi+kLftkkuz1qD88KoB9bC+/80ls+YHeScBO+QHVt8jNS26NpfoewELdddrk7//CcBPIuLh/H17RcQspcFmbgNeiogeNeBOGbmOtQF118TRUu7vf6Wk4yR9A1gMGATsBxAR55LqnfdVmm01uvm1eQC4lzQc4JCImJ2T6j6kgaunO6mWg0usVhf56fcppP79ywLLk4a46w98ExgVEePyvocBf42IqXUKt8vk7qkHkwYyHw9MJ828u0d3HaWrO3JitS6nNDjzNGDXiLhe0sqkZlR/AG4gzay6D+nh1VX1i7Q+JC1IepD1FdKYqnd2px5lPYEfXlmXi4i3cxfVMyT9IyJeUZq2e8U8stdNwPzATpKuBT6MHlQCiIjppCqBe+sdi3WMS6xWN5JGkDpG3EoafGa/nFSQtAjpwc37dQzRrEOcWK2uJH2F9LR7+Yh4Q9KCzcnVrKzcKsDqKvck2hG4U9KyTqrWHbiO1equomPELZKGknu01jsus45yVYA1jJ7UMcK6NydWM7OCuY7VzKxgTqxmZgVzYjUzK5gTq5lZwZxYrUMkNUl6TNJTksZJWmgejnWFpD3y+0slDWpj32GSNu/AOV6S9LlR9+e2vsU+7WqpIOmkPAGg9VBOrNZR0yNig4hYlzTx4WGVGyV1qI10RBxSZXrvYaS5sMwalhOrFeEe0nTdwyTdI+k6YIKk3pLOlPSQpCfy1CoouUDSc5L+Rho2kLztrtxJAEnDJT0i6XFJd0gaSErgP8il5a0kLSPpz/kcD0naIn92aUm3SXpa0qWAqELSXyU9nD9zaIttZ+f1d0haJq9bXdIt+TP3SFqniItp5eeeVzZPcsl0BHBLXrURsG5EvJiT03sRsbGk+YH7JN0GbEiafXYQaVbaCcDlLY67DHAJsHU+1lJ5VKyLSaNd/Srv90fg7Ii4Nw8/eCtpCpMTgXsj4hRJO5LGOK3moHyOBYGHJP05zz22MDA+In4g6X/ysb8LjAQOi4h/KU329xvSDLvWwzmxWkctKOmx/P4e4DLSLfqDEfFiXv81YL3m+lNgcWBNYGtgdEQ0Aa9J+nsrx98MuLv5WBHx9lzi+AowSHMmeF0sj4y1NbB7/uyNkmqZzfVoSV/P71fKsb5FmrxwbF5/FXBNPsfmwLiKc89fwzmsB3BitY6aHhEbVK7ICeajylXAURFxa4v9digwjl7AZhHxn1ZiqZmkYaQk/aWI+FjSXcACc9k98nnfbXkNzMB1rNa5bgUOlzQfgKS1JC0M3E2a16m3pBVI0zm3dD+wtaRV82eXyus/ABat2O824KjmBUnNie5uYN+8bgSwZJVYFwfeyUl1HVKJuVkv0vQo5GPem8eJfVFpqu7meuP1q5zDeggnVutMl5LqTx+R9BTwW9Jd0l+Af+Vtvwf+2fKDeXrrQ0m33Y8z51b8euDrzQ+vgKOBofnh2ATmtE44mZSYnyZVCbxSJdZbgD6SngFOJyX2Zh8Bm+TvsC1pri5Ikx4enON7Gti1hmtiPYAHYTEzK5hLrGZmBXNiNTMrmBOrmVnBnFjNzArmxGpmVjAnVjOzgjmxmpkV7P8DWsTQaoUxy6MAAAAASUVORK5CYII=\n" 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| }, | |
| "metadata": { | |
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| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "FN_y.shape" | |
| ], | |
| "metadata": { | |
| "id": "DjAdSzR56JkT", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "dd8cdbf0-493f-40be-9e1c-df1e4b5c5fbb" | |
| }, | |
| "id": "DjAdSzR56JkT", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(175,)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 96 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "FN_preds.shape" | |
| ], | |
| "metadata": { | |
| "id": "9Y4XWs7h6MUb", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "a1382c78-6459-4283-8091-fc44cca1ab39" | |
| }, | |
| "id": "9Y4XWs7h6MUb", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(175,)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 97 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm1.shape" | |
| ], | |
| "metadata": { | |
| "id": "a0afHepb3rrG", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "b56156fc-ebfe-447d-fab8-56b1199fcba6" | |
| }, | |
| "id": "a0afHepb3rrG", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(2, 2)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 98 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm1" | |
| ], | |
| "metadata": { | |
| "id": "cZJP3w2A4Eal", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ddc93e30-2bb6-41bf-a55c-011ea6e5e9f7" | |
| }, | |
| "id": "cZJP3w2A4Eal", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 444, 19244],\n", | |
| " [ 175, 5795]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 99 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm2" | |
| ], | |
| "metadata": { | |
| "id": "jXQRbBkx4GPs", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "2b438b10-f517-4e4a-a236-2baab4617160" | |
| }, | |
| "id": "jXQRbBkx4GPs", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[262, 2],\n", | |
| " [ 0, 180]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 100 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "cm3" | |
| ], | |
| "metadata": { | |
| "id": "DwjgzQJ74LDg", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "ad6d8d13-f20f-4c20-c2ac-7931a4f82175" | |
| }, | |
| "id": "DwjgzQJ74LDg", | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 0, 0, 0],\n", | |
| " [ 0, 0, 0],\n", | |
| " [ 22, 153, 0]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 101 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "f2c16ee1", | |
| "metadata": { | |
| "id": "f2c16ee1" | |
| }, | |
| "source": [ | |
| "## FINAL CONFUSION MATRIX OF ALL 3 CLASSES" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "57e9c97a", | |
| "metadata": { | |
| "id": "57e9c97a", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 365 | |
| }, | |
| "outputId": "050a8e79-c8a5-4a53-81f8-f2c9ca99d181" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[ 262 2 11853]\n", | |
| " [ 0 180 7391]\n", | |
| " [ 22 153 5795]]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ], | |
| "source": [ | |
| "cm =[[cm2[0,0] , cm2[0,1] , len(index_20)],\n", | |
| " [cm2[1,0] , cm2[1,1] , len(index_21)],\n", | |
| " [cm3[2,0] , cm3[2,1] , cm1[1,1]]]\n", | |
| "cm = np.array(cm)\n", | |
| "plot_confusion_matrix(cm,[\"Normal\",\"Pnumonia\",\"Covid-19\"] )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "daf80378", | |
| "metadata": { | |
| "id": "daf80378" | |
| }, | |
| "source": [ | |
| "## FINAL ACCURACY" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "602024b0", | |
| "metadata": { | |
| "id": "602024b0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "3dac5a12-7c61-43eb-9eae-acc3fac5e419" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Final Accuracy = 24.31%\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "final_accuracy = (cm[0,0]+cm[1,1]+cm[2,2]) / np.sum(cm)\n", | |
| "print(\"Final Accuracy = \"\"{:.2%}\".format(final_accuracy))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "final_accuracy = (cm1[0,0]+cm1[1,1]) / np.sum(cm1)\n", | |
| "print(\"Final Accuracy = \"\"{:.2%}\".format(final_accuracy))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "NFVXyHmIQPA4", | |
| "outputId": "e96fc238-e539-427d-efd8-8d78bc8cc243" | |
| }, | |
| "id": "NFVXyHmIQPA4", | |
| "execution_count": 107, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Final Accuracy = 24.32%\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "final_accuracy = (cm2[0,0]+cm2[1,1]) / np.sum(cm2)\n", | |
| "print(\"Final Accuracy = \"\"{:.2%}\".format(final_accuracy))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "zw6njMR1QX_L", | |
| "outputId": "4ae489dd-dc77-499f-91a2-5b3fd0575220" | |
| }, | |
| "id": "zw6njMR1QX_L", | |
| "execution_count": 108, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Final Accuracy = 99.55%\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.13" | |
| }, | |
| "colab": { | |
| "name": "Testing_Evaluation_eff.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "background_execution": "on", | |
| "include_colab_link": true | |
| }, | |
| "accelerator": "TPU" | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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