Project 2-Evaluation of Diagnostic Models.ipynb

{
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   "source": [
    "# Evaluation of Diagnostic Models\n",
    "\n",
    "Welcome to the second assignment of course 1. In this assignment, we will be working with the results of the X-ray classification model we developed in the previous assignment. In order to make the data processing a bit more manageable, we will be working with a subset of our training, and validation datasets. We will also use our manually labeled test dataset of 420 X-rays.  \n",
    "\n",
    "As a reminder, our dataset contains X-rays from 14 different conditions diagnosable from an X-ray. We'll evaluate our performance on each of these classes using the classification metrics we learned in lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "Click on these links to jump to a particular section of this assignment!\n",
    "- [1. Packages](#1)\n",
    "- [2. Overview](#2)\n",
    "- [3. Metrics](#3)\n",
    "    - [3.1 True Positives, False Positives, True Negatives, and False Negatives](#3-1)\n",
    "    - [3.2 Accuracy](#3-2)\n",
    "    - [3.3 Prevalence](#3-3)\n",
    "    - [3.4 Sensitivity and Specificity](#3-4)\n",
    "    - [3.5 PPV and NPV](#3-5)\n",
    "    - [3.6 ROC Curve](#3-6)\n",
    "- [4. Confidence Intervals](#4)\n",
    "- [5. Precision-Recall Curve](#5)\n",
    "- [6. F1 Score](#6)\n",
    "- [7. Calibration](#7)"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "Gfe98JLFgZZr"
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   "source": [
    "**By the end of this assignment you will learn about:**\n",
    "\n",
    "1. Accuracy\n",
    "1. Prevalence\n",
    "1. Specificity & Sensitivity\n",
    "1. PPV and NPV\n",
    "1. ROC curve and AUCROC (c-statistic)\n",
    "1. Confidence Intervals"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<a name='1'></a>\n",
    "## 1. Packages\n",
    "\n",
    "In this assignment, we'll make use of the following packages:\n",
    "- [numpy](https://docs.scipy.org/doc/numpy/) is a popular library for scientific computing\n",
    "- [matplotlib](https://matplotlib.org/3.1.1/contents.html) is a plotting library compatible with numpy\n",
    "- [pandas](https://pandas.pydata.org/docs/) is what we'll use to manipulate our data\n",
    "- [sklearn](https://scikit-learn.org/stable/index.html) will be used to measure the performance of our model\n",
    "\n",
    "\n",
    "Run the next cell to import all the necessary packages as well as custom util functions."
   ]
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  {
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   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd  \n",
    "\n",
    "import util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GA-7EX2vNmpX"
   },
   "source": [
    "<a name='2'></a>\n",
    "## 2. Overview\n",
    "\n",
    "We'll go through our evaluation metrics in the following order.\n",
    "\n",
    "- Metrics\n",
    "  - TP, TN, FP, FN\n",
    "  - Accuracy\n",
    "  - Prevalence\n",
    "  - Sensitivity and Specificity\n",
    "  - PPV and NPV\n",
    "  - AUC\n",
    "- Confidence Intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8V8WFFMRgZZs"
   },
   "source": [
    "Let's take a quick peek at our dataset. The data is stored in two CSV files called `train_preds.csv` and `valid_preds.csv`. We have precomputed the model outputs for our test cases. We'll work with these predictions and the true class labels throughout the assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "colab_type": "code",
    "id": "-W1mQ0j_gZZs"
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   "outputs": [],
   "source": [
    "train_results = pd.read_csv(\"train_preds.csv\")\n",
    "valid_results = pd.read_csv(\"valid_preds.csv\")\n",
    "\n",
    "# the labels in our dataset\n",
    "class_labels = ['Cardiomegaly',\n",
    " 'Emphysema',\n",
    " 'Effusion',\n",
    " 'Hernia',\n",
    " 'Infiltration',\n",
    " 'Mass',\n",
    " 'Nodule',\n",
    " 'Atelectasis',\n",
    " 'Pneumothorax',\n",
    " 'Pleural_Thickening',\n",
    " 'Pneumonia',\n",
    " 'Fibrosis',\n",
    " 'Edema',\n",
    " 'Consolidation']\n",
    "\n",
    "# the labels for prediction values in our dataset\n",
    "pred_labels = [l + \"_pred\" for l in class_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "9XoFEIaT3dPR"
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   "source": [
    "Extract the labels (y) and the predictions (pred)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "y = valid_results[class_labels].values\n",
    "pred = valid_results[pred_labels].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZCgrMdcH3iyK"
   },
   "source": [
    "Run the next cell to view them side by side."
   ]
  },
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    "outputId": "f521ab94-fb5d-4ba6-e8e4-8b0a0ca22863"
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       "   Cardiomegaly  Emphysema  Effusion  Hernia  Infiltration  Mass  Nodule  \\\n",
       "0             0          0         0       0             0     0       0   \n",
       "1             0          0         0       0             1     0       1   \n",
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       "4             0          0         0       0             0     0       0   \n",
       "\n",
       "   Atelectasis  Pneumothorax  Pleural_Thickening  ...  Infiltration_pred  \\\n",
       "0            0             0                   0  ...           0.256020   \n",
       "1            0             0                   0  ...           0.382199   \n",
       "2            0             0                   0  ...           0.427727   \n",
       "3            0             0                   0  ...           0.158596   \n",
       "4            0             0                   0  ...           0.536762   \n",
       "\n",
       "   Mass_pred  Nodule_pred  Atelectasis_pred  Pneumothorax_pred  \\\n",
       "0   0.266928     0.312440          0.460342           0.079453   \n",
       "1   0.176825     0.465807          0.489424           0.084595   \n",
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       "4   0.198797     0.273110          0.186771           0.242122   \n",
       "\n",
       "   Pleural_Thickening_pred  Pneumonia_pred  Fibrosis_pred  Edema_pred  \\\n",
       "0                 0.271495        0.276861       0.398799    0.015867   \n",
       "1                 0.377318        0.363582       0.638024    0.025948   \n",
       "2                 0.167095        0.166389       0.262463    0.007758   \n",
       "3                 0.229834        0.191281       0.344348    0.008559   \n",
       "4                 0.309786        0.411771       0.244666    0.126930   \n",
       "\n",
       "   Consolidation_pred  \n",
       "0            0.156320  \n",
       "1            0.144419  \n",
       "2            0.125790  \n",
       "3            0.119153  \n",
       "4            0.342409  \n",
       "\n",
       "[5 rows x 28 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's take a peek at our dataset\n",
    "valid_results[np.concatenate([class_labels, pred_labels])].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BktRP8PzgZZz"
   },
   "source": [
    "To further understand our dataset details, here's a histogram of the number of samples for each label in the validation dataset:"
   ]
  },
  {
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   "execution_count": 5,
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation=90)\n",
    "plt.bar(x = class_labels, height= y.sum(axis=0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WMbrDZTfOQ_t"
   },
   "source": [
    "It seem like our dataset has an imbalanced population of samples. Specifically, our dataset has a small number of patients diagnosed with a `Hernia`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MaBE2BeVOU-d"
   },
   "source": [
    "<a name='3'></a>\n",
    "## 3 Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZqCKzNTLvVc6"
   },
   "source": [
    "<a name='3-1'></a>\n",
    "### 3.1 True Positives, False Positives, True Negatives, and False Negatives\n",
    "\n",
    "The most basic statistics to compute from the model predictions are the true positives, true negatives, false positives, and false negatives. \n",
    "\n",
    "As the name suggests\n",
    "- true positive (TP): The model classifies the example as positive, and the actual label also positive.\n",
    "- false positive (FP): The model classifies the example as positive, **but** the actual label is negative.\n",
    "- true negative (TN): The model classifies the example as negative, and the actual label is also negative.\n",
    "- false negative (FN): The model classifies the example as negative, **but** the label is actually positive.\n",
    "\n",
    "We will count the number of TP, FP, TN and FN in the given data.  All of our metrics can be built off of these four statistics. \n",
    "\n",
    "Recall that the model outputs real numbers between 0 and 1.\n",
    "* To compute binary class predictions, we need to convert these to either 0 or 1. \n",
    "* We'll do this using a threshold value $th$.\n",
    "* Any model outputs above $th$ are set to 1, and below $th$ are set to 0. \n",
    "\n",
    "All of our metrics (except for AUC at the end) will depend on the choice of this threshold. \n",
    "\n",
    "Fill in the functions to compute the TP, FP, TN, and FN for a given threshold below. \n",
    "\n",
    "The first one has been done for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6epwT5HJwAQb"
   },
   "outputs": [],
   "source": [
    "# UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n",
    "def true_positives(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Count true positives.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        TP (int): true positives\n",
    "    \"\"\"\n",
    "    TP = 0\n",
    "    \n",
    "    # get thresholded predictions\n",
    "    thresholded_preds = pred >= th\n",
    "\n",
    "    # compute TP\n",
    "    TP = np.sum((y == 1) & (thresholded_preds == 1))\n",
    "    \n",
    "    return TP\n",
    "\n",
    "def true_negatives(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Count true negatives.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        TN (int): true negatives\n",
    "    \"\"\"\n",
    "    TN = 0\n",
    "    \n",
    "    # get thresholded predictions\n",
    "    thresholded_preds = pred >= th\n",
    "\n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # compute TN\n",
    "    TN = np.sum((y == 0) & (thresholded_preds == 0))\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return TN\n",
    "\n",
    "def false_positives(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Count false positives.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        FP (int): false positives\n",
    "    \"\"\"\n",
    "    FP = 0\n",
    "    \n",
    "    # get thresholded predictions\n",
    "    thresholded_preds = pred >= th\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "\n",
    "    # compute FP\n",
    "    FP = np.sum((y == 0) & (thresholded_preds == 1))\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return FP\n",
    "\n",
    "def false_negatives(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Count false positives.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        FN (int): false negatives\n",
    "    \"\"\"\n",
    "    FN = 0\n",
    "    \n",
    "    # get thresholded predictions\n",
    "    thresholded_preds = pred >= th\n",
    "\n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # compute FN\n",
    "    FN = np.sum((y == 1) & (thresholded_preds == 0))\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return FN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "ORgq9Y_FyDhF",
    "outputId": "acc0073e-17b3-458f-cdda-f99a26ac1b02"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>y_test</th>\n",
       "      <th>preds_test</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>TP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>TP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>FP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>FP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>FP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>FP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>FN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>FN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>FN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>FN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    y_test  preds_test category\n",
       "0        1         0.8       TP\n",
       "1        1         0.7       TP\n",
       "2        0         0.4       TN\n",
       "3        0         0.3       TN\n",
       "4        0         0.2       TN\n",
       "5        0         0.5       FP\n",
       "6        0         0.6       FP\n",
       "7        0         0.7       FP\n",
       "8        0         0.8       FP\n",
       "9        1         0.1       FN\n",
       "10       1         0.2       FN\n",
       "11       1         0.3       FN\n",
       "12       1         0.4       FN\n",
       "13       1         0.0       FN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold: 0.5\n",
      "\n",
      "Our functions calculated: \n",
      "TP: 2\n",
      "TN: 3\n",
      "FP: 4\n",
      "FN: 5\n",
      "\n",
      "Expected results\n",
      "There are 2 TP\n",
      "There are 3 TN\n",
      "There are 4 FP\n",
      "There are 5 FN\n"
     ]
    }
   ],
   "source": [
    "# Note: we must explicity import 'display' in order for the autograder to compile the submitted code\n",
    "# Even though we could use this function without importing it, keep this import in order to allow the grader to work\n",
    "from IPython.display import display\n",
    "# Test\n",
    "df = pd.DataFrame({'y_test': [1,1,0,0,0,0,0,0,0,1,1,1,1,1],\n",
    "                   'preds_test': [0.8,0.7,0.4,0.3,0.2,0.5,0.6,0.7,0.8,0.1,0.2,0.3,0.4,0],\n",
    "                   'category': ['TP','TP','TN','TN','TN','FP','FP','FP','FP','FN','FN','FN','FN','FN']\n",
    "                  })\n",
    "\n",
    "display(df)\n",
    "#y_test = np.array([1, 0, 0, 1, 1])\n",
    "y_test = df['y_test']\n",
    "\n",
    "#preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
    "preds_test = df['preds_test']\n",
    "\n",
    "threshold = 0.5\n",
    "print(f\"threshold: {threshold}\\n\")\n",
    "\n",
    "print(f\"\"\"Our functions calculated: \n",
    "TP: {true_positives(y_test, preds_test, threshold)}\n",
    "TN: {true_negatives(y_test, preds_test, threshold)}\n",
    "FP: {false_positives(y_test, preds_test, threshold)}\n",
    "FN: {false_negatives(y_test, preds_test, threshold)}\n",
    "\"\"\")\n",
    "\n",
    "print(\"Expected results\")\n",
    "print(f\"There are {sum(df['category'] == 'TP')} TP\")\n",
    "print(f\"There are {sum(df['category'] == 'TN')} TN\")\n",
    "print(f\"There are {sum(df['category'] == 'FP')} FP\")\n",
    "print(f\"There are {sum(df['category'] == 'FN')} FN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yz0x1BdhyuUN"
   },
   "source": [
    "Run the next cell to see a summary of evaluative metrics for the model predictions for each class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "E6u3eoNCy1LU",
    "outputId": "52807e22-3463-46f8-c03b-92097b10ae0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN     Accuracy   Prevalence  Sensitivity  \\\n",
       "                                                                               \n",
       "Cardiomegaly         16  814  169   1  Not Defined  Not Defined  Not Defined   \n",
       "Emphysema            20  869  103   8  Not Defined  Not Defined  Not Defined   \n",
       "Effusion             99  690  196  15  Not Defined  Not Defined  Not Defined   \n",
       "Hernia                1  743  255   1  Not Defined  Not Defined  Not Defined   \n",
       "Infiltration        114  543  265  78  Not Defined  Not Defined  Not Defined   \n",
       "Mass                 40  789  158  13  Not Defined  Not Defined  Not Defined   \n",
       "Nodule               28  731  220  21  Not Defined  Not Defined  Not Defined   \n",
       "Atelectasis          64  657  249  30  Not Defined  Not Defined  Not Defined   \n",
       "Pneumothorax         24  785  183   8  Not Defined  Not Defined  Not Defined   \n",
       "Pleural_Thickening   24  713  259   4  Not Defined  Not Defined  Not Defined   \n",
       "Pneumonia            14  661  320   5  Not Defined  Not Defined  Not Defined   \n",
       "Fibrosis             10  725  261   4  Not Defined  Not Defined  Not Defined   \n",
       "Edema                15  767  213   5  Not Defined  Not Defined  Not Defined   \n",
       "Consolidation        36  658  297   9  Not Defined  Not Defined  Not Defined   \n",
       "\n",
       "                    Specificity          PPV          NPV          AUC  \\\n",
       "                                                                         \n",
       "Cardiomegaly        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Emphysema           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Effusion            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Hernia              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Infiltration        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Mass                Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Nodule              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Atelectasis         Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumothorax        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pleural_Thickening  Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumonia           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Fibrosis            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Edema               Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Consolidation       Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "\n",
       "                             F1 Threshold  \n",
       "                                           \n",
       "Cardiomegaly        Not Defined       0.5  \n",
       "Emphysema           Not Defined       0.5  \n",
       "Effusion            Not Defined       0.5  \n",
       "Hernia              Not Defined       0.5  \n",
       "Infiltration        Not Defined       0.5  \n",
       "Mass                Not Defined       0.5  \n",
       "Nodule              Not Defined       0.5  \n",
       "Atelectasis         Not Defined       0.5  \n",
       "Pneumothorax        Not Defined       0.5  \n",
       "Pleural_Thickening  Not Defined       0.5  \n",
       "Pneumonia           Not Defined       0.5  \n",
       "Fibrosis            Not Defined       0.5  \n",
       "Edema               Not Defined       0.5  \n",
       "Consolidation       Not Defined       0.5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.get_performance_metrics(y, pred, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F6M_l5QNy3ux"
   },
   "source": [
    "Right now it only has TP, TN, FP, FN. Throughout this assignment we'll fill in all the other metrics to learn more about our model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ISy-naajOTp7"
   },
   "source": [
    "<a name='3-2'></a>\n",
    "### 3.2 Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qMNxd3IGgZZ3"
   },
   "source": [
    "\n",
    "Let's use a threshold of .5 for the probability cutoff for our predictions for all classes and calculate our model's accuracy as we would normally do in a machine learning problem. \n",
    "\n",
    "$$accuracy = \\frac{\\text{true positives} + \\text{true negatives}}{\\text{true positives} + \\text{true negatives} + \\text{false positives} + \\text{false negatives}}$$\n",
    "\n",
    "Use this formula to compute accuracy below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>    \n",
    "<summary>\n",
    "    <font size=\"3\" color=\"darkgreen\"><b>Hints</b></font>\n",
    "</summary>\n",
    "<p>\n",
    "<ul>\n",
    "    <li>Remember to set the value for the threshold when calling the functions.</li>\n",
    "</ul>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tcMIPS4jQbDE"
   },
   "outputs": [],
   "source": [
    "# UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n",
    "def get_accuracy(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Compute accuracy of predictions at threshold.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        accuracy (float): accuracy of predictions at threshold\n",
    "    \"\"\"\n",
    "    accuracy = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # get TP, FP, TN, FN using our previously defined functions\n",
    "    TP = true_positives(y, pred, th)\n",
    "    FP = false_positives(y, pred, th)\n",
    "    TN = true_negatives(y, pred, th)\n",
    "    FN = false_negatives(y, pred, th)\n",
    "\n",
    "    # Compute accuracy using TP, FP, TN, FN\n",
    "    accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "Sa5ypRRlRhVz",
    "outputId": "a2a37bdc-7ef4-4824-9ea2-9678f6b0ccfb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test case:\n",
      "test labels: {y_test}\n",
      "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
      "threshold: 0.5\n",
      "computed accuracy: 0.6\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "print(\"Test case:\")\n",
    "\n",
    "y_test = np.array([1, 0, 0, 1, 1])\n",
    "print('test labels: {y_test}')\n",
    "\n",
    "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
    "print(f'test predictions: {preds_test}')\n",
    "\n",
    "threshold = 0.5\n",
    "print(f\"threshold: {threshold}\")\n",
    "\n",
    "print(f\"computed accuracy: {get_accuracy(y_test, preds_test, threshold)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected output:\n",
    "\n",
    "```Python\n",
    "test labels: {y_test}\n",
    "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
    "threshold: 0.5\n",
    "computed accuracy: 0.6\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GGq_9OUBu9Er"
   },
   "source": [
    "Run the next cell to see the accuracy of the model output for each class, as well as the number of true positives, true negatives, false positives, and false negatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "wmaDXXy0gZZ7",
    "outputId": "7210644a-5bd9-4862-ca2b-4475f23e4515"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy   Prevalence  Sensitivity  \\\n",
       "                                                                           \n",
       "Cardiomegaly         16  814  169   1     0.83  Not Defined  Not Defined   \n",
       "Emphysema            20  869  103   8    0.889  Not Defined  Not Defined   \n",
       "Effusion             99  690  196  15    0.789  Not Defined  Not Defined   \n",
       "Hernia                1  743  255   1    0.744  Not Defined  Not Defined   \n",
       "Infiltration        114  543  265  78    0.657  Not Defined  Not Defined   \n",
       "Mass                 40  789  158  13    0.829  Not Defined  Not Defined   \n",
       "Nodule               28  731  220  21    0.759  Not Defined  Not Defined   \n",
       "Atelectasis          64  657  249  30    0.721  Not Defined  Not Defined   \n",
       "Pneumothorax         24  785  183   8    0.809  Not Defined  Not Defined   \n",
       "Pleural_Thickening   24  713  259   4    0.737  Not Defined  Not Defined   \n",
       "Pneumonia            14  661  320   5    0.675  Not Defined  Not Defined   \n",
       "Fibrosis             10  725  261   4    0.735  Not Defined  Not Defined   \n",
       "Edema                15  767  213   5    0.782  Not Defined  Not Defined   \n",
       "Consolidation        36  658  297   9    0.694  Not Defined  Not Defined   \n",
       "\n",
       "                    Specificity          PPV          NPV          AUC  \\\n",
       "                                                                         \n",
       "Cardiomegaly        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Emphysema           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Effusion            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Hernia              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Infiltration        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Mass                Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Nodule              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Atelectasis         Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumothorax        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pleural_Thickening  Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumonia           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Fibrosis            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Edema               Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Consolidation       Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "\n",
       "                             F1 Threshold  \n",
       "                                           \n",
       "Cardiomegaly        Not Defined       0.5  \n",
       "Emphysema           Not Defined       0.5  \n",
       "Effusion            Not Defined       0.5  \n",
       "Hernia              Not Defined       0.5  \n",
       "Infiltration        Not Defined       0.5  \n",
       "Mass                Not Defined       0.5  \n",
       "Nodule              Not Defined       0.5  \n",
       "Atelectasis         Not Defined       0.5  \n",
       "Pneumothorax        Not Defined       0.5  \n",
       "Pleural_Thickening  Not Defined       0.5  \n",
       "Pneumonia           Not Defined       0.5  \n",
       "Fibrosis            Not Defined       0.5  \n",
       "Edema               Not Defined       0.5  \n",
       "Consolidation       Not Defined       0.5  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HtVllCgFgZZ-"
   },
   "source": [
    "If we were to judge our model's performance based on the accuracy metric, we would say that our model is not very accurate for detecting the `Infiltration` cases (accuracy of 0.657) but pretty accurate for detecting `Emphysema` (accuracy of 0.889). \n",
    "\n",
    "**But is that really the case?...**\n",
    "\n",
    "Let's imagine a model that simply predicts that any patient does **Not** have `Emphysema`, regardless of patient's measurements. Let's calculate the accuracy for such a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6N-2iQrqgZZ_",
    "outputId": "840e3863-17e1-4905-9575-a7fd73367137"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.972"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_accuracy(valid_results[\"Emphysema\"].values, np.zeros(len(valid_results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BhSRo0iqKy_4"
   },
   "source": [
    "As you can see above, such a model would be 97% accurate! Even better than our deep learning based model. \n",
    "\n",
    "But is this really a good model? Wouldn't this model be wrong 100% of the time if the patient actually had this condition?\n",
    "\n",
    "In the following sections, we will address this concern with more advanced model measures - **sensitivity and specificity** - that evaluate how well the model predicts positives for patients with the condition and negatives for cases that actually do not have the condition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nat--E8KgZaD"
   },
   "source": [
    "<a name='3-3'></a>\n",
    "### 3.3 Prevalence\n",
    "Another important concept is **prevalence**. \n",
    "* In a medical context, prevalence is the proportion of people in the population who have the disease (or condition, etc). \n",
    "* In machine learning terms, this is the proportion of positive examples. The expression for prevalence is:\n",
    "\n",
    "$$prevalence = \\frac{1}{N} \\sum_{i} y_i$$\n",
    "\n",
    "where $y_i = 1$ when the example is 'positive' (has the disease).\n",
    "\n",
    "Let's measure prevalence for each disease:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>    \n",
    "<summary>\n",
    "    <font size=\"3\" color=\"darkgreen\"><b>Hints</b></font>\n",
    "</summary>\n",
    "<p>\n",
    "<ul>\n",
    "    <li>\n",
    "    You can use <a href=\"https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html\" > np.mean </a> to calculate the formula.</li>\n",
    "    <li>Actually, the automatic grader is expecting numpy.mean, so please use it instead of using an equally valid but different way of calculating the prevalence. =) </li>\n",
    "</ul>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dHkdpG-RVRNp"
   },
   "outputs": [],
   "source": [
    "# UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n",
    "def get_prevalence(y):\n",
    "    \"\"\"\n",
    "    Compute prevalence.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "    Returns:\n",
    "        prevalence (float): prevalence of positive cases\n",
    "    \"\"\"\n",
    "    prevalence = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    prevalence = np.mean(y)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return prevalence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "djP5dqqsVabT",
    "outputId": "a3888472-26c5-4340-e310-026d98a5fe2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test case:\n",
      "\n",
      "test labels: [1 0 0 1 1 0 0 0 0 1]\n",
      "computed prevalence: 0.4\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "print(\"Test case:\\n\")\n",
    "\n",
    "y_test = np.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 1])\n",
    "print(f'test labels: {y_test}')\n",
    "\n",
    "print(f\"computed prevalence: {get_prevalence(y_test)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "lpBSG_kBVPXt",
    "outputId": "d9ce79fb-8222-43bf-fcef-1e3fdbbc8d66"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.017</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.028</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.114</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.002</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.192</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.053</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.049</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.094</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.032</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.028</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.019</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.014</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.045</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy Prevalence  Sensitivity  \\\n",
       "                                                                         \n",
       "Cardiomegaly         16  814  169   1     0.83      0.017  Not Defined   \n",
       "Emphysema            20  869  103   8    0.889      0.028  Not Defined   \n",
       "Effusion             99  690  196  15    0.789      0.114  Not Defined   \n",
       "Hernia                1  743  255   1    0.744      0.002  Not Defined   \n",
       "Infiltration        114  543  265  78    0.657      0.192  Not Defined   \n",
       "Mass                 40  789  158  13    0.829      0.053  Not Defined   \n",
       "Nodule               28  731  220  21    0.759      0.049  Not Defined   \n",
       "Atelectasis          64  657  249  30    0.721      0.094  Not Defined   \n",
       "Pneumothorax         24  785  183   8    0.809      0.032  Not Defined   \n",
       "Pleural_Thickening   24  713  259   4    0.737      0.028  Not Defined   \n",
       "Pneumonia            14  661  320   5    0.675      0.019  Not Defined   \n",
       "Fibrosis             10  725  261   4    0.735      0.014  Not Defined   \n",
       "Edema                15  767  213   5    0.782       0.02  Not Defined   \n",
       "Consolidation        36  658  297   9    0.694      0.045  Not Defined   \n",
       "\n",
       "                    Specificity          PPV          NPV          AUC  \\\n",
       "                                                                         \n",
       "Cardiomegaly        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Emphysema           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Effusion            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Hernia              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Infiltration        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Mass                Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Nodule              Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Atelectasis         Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumothorax        Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pleural_Thickening  Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Pneumonia           Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Fibrosis            Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Edema               Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "Consolidation       Not Defined  Not Defined  Not Defined  Not Defined   \n",
       "\n",
       "                             F1 Threshold  \n",
       "                                           \n",
       "Cardiomegaly        Not Defined       0.5  \n",
       "Emphysema           Not Defined       0.5  \n",
       "Effusion            Not Defined       0.5  \n",
       "Hernia              Not Defined       0.5  \n",
       "Infiltration        Not Defined       0.5  \n",
       "Mass                Not Defined       0.5  \n",
       "Nodule              Not Defined       0.5  \n",
       "Atelectasis         Not Defined       0.5  \n",
       "Pneumothorax        Not Defined       0.5  \n",
       "Pleural_Thickening  Not Defined       0.5  \n",
       "Pneumonia           Not Defined       0.5  \n",
       "Fibrosis            Not Defined       0.5  \n",
       "Edema               Not Defined       0.5  \n",
       "Consolidation       Not Defined       0.5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Hernia` has a prevalence 0.002, which is the rarest among the studied conditions in our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n5vUNrXOgZaK"
   },
   "source": [
    "<a name='3-4'></a>\n",
    "### 3.4 Sensitivity and Specificity\n",
    "<img src=\"sens_spec.png\" width=\"30%\">\n",
    "\n",
    "Sensitivity and specificity are two of the most prominent numbers that are used to measure diagnostics tests.\n",
    "- Sensitivity is the probability that our test outputs positive given that the case is actually positive.\n",
    "- Specificity is the probability that the test outputs negative given that the case is actually negative. \n",
    "\n",
    "We can phrase this easily in terms of true positives, true negatives, false positives, and false negatives: \n",
    "\n",
    "$$sensitivity = \\frac{\\text{true positives}}{\\text{true positives} + \\text{false negatives}}$$\n",
    "\n",
    "$$specificity = \\frac{\\text{true negatives}}{\\text{true negatives} + \\text{false positives}}$$\n",
    "\n",
    "Let's calculate sensitivity and specificity for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W31FpMtmYhPr"
   },
   "outputs": [],
   "source": [
    "# UNQ_C4 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n",
    "def get_sensitivity(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Compute sensitivity of predictions at threshold.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        sensitivity (float): probability that our test outputs positive given that the case is actually positive\n",
    "    \"\"\"\n",
    "    sensitivity = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # get TP and FN using our previously defined functions\n",
    "    TP = true_positives(y, pred, th)\n",
    "    FN = false_negatives(y, pred, th)\n",
    "\n",
    "    # use TP and FN to compute sensitivity\n",
    "    sensitivity = TP / (TP + FN)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return sensitivity\n",
    "\n",
    "def get_specificity(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Compute specificity of predictions at threshold.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        specificity (float): probability that the test outputs negative given that the case is actually negative\n",
    "    \"\"\"\n",
    "    specificity = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # get TN and FP using our previously defined functions\n",
    "    TN = true_negatives(y, pred, th)\n",
    "    FP = false_positives(y, pred, th)\n",
    "    \n",
    "    # use TN and FP to compute specificity \n",
    "    specificity = TN / (TN + FP)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "RInygNYDXdHz",
    "outputId": "1915ff27-1701-4ee7-ddf7-5a058faee29c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test case\n",
      "test labels: [1 0 0 1 1]\n",
      "\n",
      "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
      "\n",
      "threshold: 0.5\n",
      "\n",
      "computed sensitivity: 0.67\n",
      "computed specificity: 0.50\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "print(\"Test case\")\n",
    "\n",
    "y_test = np.array([1, 0, 0, 1, 1])\n",
    "print(f'test labels: {y_test}\\n')\n",
    "\n",
    "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
    "print(f'test predictions: {preds_test}\\n')\n",
    "\n",
    "threshold = 0.5\n",
    "print(f\"threshold: {threshold}\\n\")\n",
    "\n",
    "print(f\"computed sensitivity: {get_sensitivity(y_test, preds_test, threshold):.2f}\")\n",
    "print(f\"computed specificity: {get_specificity(y_test, preds_test, threshold):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected output:\n",
    "\n",
    "```Python\n",
    "Test case\n",
    "test labels: [1 0 0 1 1]\n",
    "\n",
    "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
    "\n",
    "threshold: 0.5\n",
    "\n",
    "computed sensitivity: 0.67\n",
    "computed specificity: 0.50\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "EA3vVNp5gZaO",
    "outputId": "1f82e534-0353-406e-df6a-b5926db06803"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.828</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.894</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.779</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.744</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.672</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.833</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.769</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.725</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.811</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.734</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.674</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.735</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.783</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.689</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy Prevalence Sensitivity  \\\n",
       "                                                                        \n",
       "Cardiomegaly         16  814  169   1     0.83      0.017       0.941   \n",
       "Emphysema            20  869  103   8    0.889      0.028       0.714   \n",
       "Effusion             99  690  196  15    0.789      0.114       0.868   \n",
       "Hernia                1  743  255   1    0.744      0.002         0.5   \n",
       "Infiltration        114  543  265  78    0.657      0.192       0.594   \n",
       "Mass                 40  789  158  13    0.829      0.053       0.755   \n",
       "Nodule               28  731  220  21    0.759      0.049       0.571   \n",
       "Atelectasis          64  657  249  30    0.721      0.094       0.681   \n",
       "Pneumothorax         24  785  183   8    0.809      0.032        0.75   \n",
       "Pleural_Thickening   24  713  259   4    0.737      0.028       0.857   \n",
       "Pneumonia            14  661  320   5    0.675      0.019       0.737   \n",
       "Fibrosis             10  725  261   4    0.735      0.014       0.714   \n",
       "Edema                15  767  213   5    0.782       0.02        0.75   \n",
       "Consolidation        36  658  297   9    0.694      0.045         0.8   \n",
       "\n",
       "                   Specificity          PPV          NPV          AUC  \\\n",
       "                                                                        \n",
       "Cardiomegaly             0.828  Not Defined  Not Defined  Not Defined   \n",
       "Emphysema                0.894  Not Defined  Not Defined  Not Defined   \n",
       "Effusion                 0.779  Not Defined  Not Defined  Not Defined   \n",
       "Hernia                   0.744  Not Defined  Not Defined  Not Defined   \n",
       "Infiltration             0.672  Not Defined  Not Defined  Not Defined   \n",
       "Mass                     0.833  Not Defined  Not Defined  Not Defined   \n",
       "Nodule                   0.769  Not Defined  Not Defined  Not Defined   \n",
       "Atelectasis              0.725  Not Defined  Not Defined  Not Defined   \n",
       "Pneumothorax             0.811  Not Defined  Not Defined  Not Defined   \n",
       "Pleural_Thickening       0.734  Not Defined  Not Defined  Not Defined   \n",
       "Pneumonia                0.674  Not Defined  Not Defined  Not Defined   \n",
       "Fibrosis                 0.735  Not Defined  Not Defined  Not Defined   \n",
       "Edema                    0.783  Not Defined  Not Defined  Not Defined   \n",
       "Consolidation            0.689  Not Defined  Not Defined  Not Defined   \n",
       "\n",
       "                             F1 Threshold  \n",
       "                                           \n",
       "Cardiomegaly        Not Defined       0.5  \n",
       "Emphysema           Not Defined       0.5  \n",
       "Effusion            Not Defined       0.5  \n",
       "Hernia              Not Defined       0.5  \n",
       "Infiltration        Not Defined       0.5  \n",
       "Mass                Not Defined       0.5  \n",
       "Nodule              Not Defined       0.5  \n",
       "Atelectasis         Not Defined       0.5  \n",
       "Pneumothorax        Not Defined       0.5  \n",
       "Pleural_Thickening  Not Defined       0.5  \n",
       "Pneumonia           Not Defined       0.5  \n",
       "Fibrosis            Not Defined       0.5  \n",
       "Edema               Not Defined       0.5  \n",
       "Consolidation       Not Defined       0.5  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
    "                        sens=get_sensitivity, spec=get_specificity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ExuKMpQCgZaR"
   },
   "source": [
    "Note that specificity and sensitivity do not depend on the prevalence of the positive class in the dataset. \n",
    "* This is because the statistics are only computed within people of the same class\n",
    "* Sensitivity only considers output on people in the positive class\n",
    "* Similarly, specificity only considers output on people in the negative class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uib5ZXZ8gZaS"
   },
   "source": [
    "<a name='3-5'></a>\n",
    "### 3.5 PPV and NPV\n",
    "\n",
    "Diagnostically, however, sensitivity and specificity are not helpful. Sensitivity, for example, tells us the probability our test outputs positive given that the person already has the condition. Here, we are conditioning on the thing we would like to find out (whether the patient has the condition)!\n",
    "\n",
    "What would be more helpful is the probability that the person has the disease given that our test outputs positive. That brings us to positive predictive value (PPV) and negative predictive value (NPV).\n",
    "\n",
    "- Positive predictive value (PPV) is the probability that subjects with a positive screening test truly have the disease.\n",
    "- Negative predictive value (NPV) is the probability that subjects with a negative screening test truly don't have the disease.\n",
    "\n",
    "Again, we can formulate these in terms of true positives, true negatives, false positives, and false negatives: \n",
    "\n",
    "$$PPV = \\frac{\\text{true positives}}{\\text{true positives} + \\text{false positives}}$$ \n",
    "\n",
    "$$NPV = \\frac{\\text{true negatives}}{\\text{true negatives} + \\text{false negatives}}$$\n",
    "\n",
    "\n",
    "Let's calculate PPV & NPV for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8nPknnBaeyPH"
   },
   "outputs": [],
   "source": [
    "# UNQ_C5 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)\n",
    "def get_ppv(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Compute PPV of predictions at threshold.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        PPV (float): positive predictive value of predictions at threshold\n",
    "    \"\"\"\n",
    "    PPV = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # get TP and FP using our previously defined functions\n",
    "    TP = true_positives(y, pred, th)\n",
    "    FP = false_positives(y, pred, th)\n",
    "\n",
    "    # use TP and FP to compute PPV\n",
    "    PPV = TP / (TP + FP)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return PPV\n",
    "\n",
    "def get_npv(y, pred, th=0.5):\n",
    "    \"\"\"\n",
    "    Compute NPV of predictions at threshold.\n",
    "\n",
    "    Args:\n",
    "        y (np.array): ground truth, size (n_examples)\n",
    "        pred (np.array): model output, size (n_examples)\n",
    "        th (float): cutoff value for positive prediction from model\n",
    "    Returns:\n",
    "        NPV (float): negative predictive value of predictions at threshold\n",
    "    \"\"\"\n",
    "    NPV = 0.0\n",
    "    \n",
    "    ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###\n",
    "    \n",
    "    # get TN and FN using our previously defined functions\n",
    "    TN = true_negatives(y, pred, th)\n",
    "    FN = false_negatives(y, pred, th)\n",
    "\n",
    "    # use TN and FN to compute NPV\n",
    "    NPV = TN / (TN + FN)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return NPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "rHMPfR45fB5k",
    "outputId": "e9759c8f-17ef-47df-c612-d7c3b46970ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test case:\n",
      "\n",
      "test labels: [1 0 0 1 1]\n",
      "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
      "\n",
      "threshold: 0.5\n",
      "\n",
      "computed ppv: 0.67\n",
      "computed npv: 0.50\n"
     ]
    }
   ],
   "source": [
    "# Test\n",
    "print(\"Test case:\\n\")\n",
    "\n",
    "y_test = np.array([1, 0, 0, 1, 1])\n",
    "print(f'test labels: {y_test}')\n",
    "\n",
    "preds_test = np.array([0.8, 0.8, 0.4, 0.6, 0.3])\n",
    "print(f'test predictions: {preds_test}\\n')\n",
    "\n",
    "threshold = 0.5\n",
    "print(f\"threshold: {threshold}\\n\")\n",
    "\n",
    "print(f\"computed ppv: {get_ppv(y_test, preds_test, threshold):.2f}\")\n",
    "print(f\"computed npv: {get_npv(y_test, preds_test, threshold):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected output:\n",
    "\n",
    "```Python\n",
    "Test case:\n",
    "\n",
    "test labels: [1 0 0 1 1]\n",
    "test predictions: [0.8 0.8 0.4 0.6 0.3]\n",
    "\n",
    "threshold: 0.5\n",
    "\n",
    "computed ppv: 0.67\n",
    "computed npv: 0.50\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "Upr9wVL-gZaW",
    "outputId": "e8fe9db4-f7a3-4403-c36e-60cfb84385c3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.999</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.991</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.979</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.999</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.874</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.984</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.972</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.956</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.994</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.992</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.995</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.994</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.987</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy Prevalence Sensitivity  \\\n",
       "                                                                        \n",
       "Cardiomegaly         16  814  169   1     0.83      0.017       0.941   \n",
       "Emphysema            20  869  103   8    0.889      0.028       0.714   \n",
       "Effusion             99  690  196  15    0.789      0.114       0.868   \n",
       "Hernia                1  743  255   1    0.744      0.002         0.5   \n",
       "Infiltration        114  543  265  78    0.657      0.192       0.594   \n",
       "Mass                 40  789  158  13    0.829      0.053       0.755   \n",
       "Nodule               28  731  220  21    0.759      0.049       0.571   \n",
       "Atelectasis          64  657  249  30    0.721      0.094       0.681   \n",
       "Pneumothorax         24  785  183   8    0.809      0.032        0.75   \n",
       "Pleural_Thickening   24  713  259   4    0.737      0.028       0.857   \n",
       "Pneumonia            14  661  320   5    0.675      0.019       0.737   \n",
       "Fibrosis             10  725  261   4    0.735      0.014       0.714   \n",
       "Edema                15  767  213   5    0.782       0.02        0.75   \n",
       "Consolidation        36  658  297   9    0.694      0.045         0.8   \n",
       "\n",
       "                   Specificity    PPV    NPV          AUC           F1  \\\n",
       "                                                                         \n",
       "Cardiomegaly             0.828  0.086  0.999  Not Defined  Not Defined   \n",
       "Emphysema                0.894  0.163  0.991  Not Defined  Not Defined   \n",
       "Effusion                 0.779  0.336  0.979  Not Defined  Not Defined   \n",
       "Hernia                   0.744  0.004  0.999  Not Defined  Not Defined   \n",
       "Infiltration             0.672  0.301  0.874  Not Defined  Not Defined   \n",
       "Mass                     0.833  0.202  0.984  Not Defined  Not Defined   \n",
       "Nodule                   0.769  0.113  0.972  Not Defined  Not Defined   \n",
       "Atelectasis              0.725  0.204  0.956  Not Defined  Not Defined   \n",
       "Pneumothorax             0.811  0.116   0.99  Not Defined  Not Defined   \n",
       "Pleural_Thickening       0.734  0.085  0.994  Not Defined  Not Defined   \n",
       "Pneumonia                0.674  0.042  0.992  Not Defined  Not Defined   \n",
       "Fibrosis                 0.735  0.037  0.995  Not Defined  Not Defined   \n",
       "Edema                    0.783  0.066  0.994  Not Defined  Not Defined   \n",
       "Consolidation            0.689  0.108  0.987  Not Defined  Not Defined   \n",
       "\n",
       "                   Threshold  \n",
       "                              \n",
       "Cardiomegaly             0.5  \n",
       "Emphysema                0.5  \n",
       "Effusion                 0.5  \n",
       "Hernia                   0.5  \n",
       "Infiltration             0.5  \n",
       "Mass                     0.5  \n",
       "Nodule                   0.5  \n",
       "Atelectasis              0.5  \n",
       "Pneumothorax             0.5  \n",
       "Pleural_Thickening       0.5  \n",
       "Pneumonia                0.5  \n",
       "Fibrosis                 0.5  \n",
       "Edema                    0.5  \n",
       "Consolidation            0.5  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
    "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sx_454Rpff-D"
   },
   "source": [
    "Notice that despite having very high sensitivity and accuracy, the PPV of the predictions could still be very low. \n",
    "\n",
    "This is the case with `Edema`, for example. \n",
    "* The sensitivity for `Edema` is 0.75.\n",
    "* However, given that the model predicted positive, the probability that a person has Edema (its PPV) is only 0.066!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5DEnn_rTgZaa"
   },
   "source": [
    "<a name='3-6'></a>\n",
    "### 3.6 ROC Curve\n",
    "\n",
    "So far we have been operating under the assumption that our model's prediction of `0.5` and above should be treated as positive and otherwise it should be treated as negative. This however was a rather arbitrary choice. One way to see this, is to look at a very informative visualization called the receiver operating characteristic (ROC) curve.\n",
    "\n",
    "The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The ideal point is at the top left, with a true positive rate of 1 and a false positive rate of 0. The various points on the curve are generated by gradually changing the threshold.\n",
    "\n",
    "Let's look at this curve for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "lA-UFT9ngZae",
    "outputId": "66725978-cb15-4ade-c4da-33deca6c509d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.get_curve(y, pred, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "adjBlFIqgZah"
   },
   "source": [
    "The area under the ROC curve is also called AUCROC or C-statistic and is a measure of goodness of fit. In medical literature this number also gives the probability that a randomly selected patient who experienced a condition had a higher risk score than a patient who had not experienced the event. This summarizes the model output across all thresholds, and provides a good sense of the discriminative power of a given model.\n",
    "\n",
    "Let's use the `sklearn` metric function of `roc_auc_score` to add this score to our metrics table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "ipMeytjAgZai",
    "outputId": "a249365b-c1a9-45d5-943b-a63880a25a38"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.933</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.935</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.891</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.644</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.888</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.745</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.781</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.826</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.868</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.762</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.801</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.856</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.799</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy Prevalence Sensitivity  \\\n",
       "                                                                        \n",
       "Cardiomegaly         16  814  169   1     0.83      0.017       0.941   \n",
       "Emphysema            20  869  103   8    0.889      0.028       0.714   \n",
       "Effusion             99  690  196  15    0.789      0.114       0.868   \n",
       "Hernia                1  743  255   1    0.744      0.002         0.5   \n",
       "Infiltration        114  543  265  78    0.657      0.192       0.594   \n",
       "Mass                 40  789  158  13    0.829      0.053       0.755   \n",
       "Nodule               28  731  220  21    0.759      0.049       0.571   \n",
       "Atelectasis          64  657  249  30    0.721      0.094       0.681   \n",
       "Pneumothorax         24  785  183   8    0.809      0.032        0.75   \n",
       "Pleural_Thickening   24  713  259   4    0.737      0.028       0.857   \n",
       "Pneumonia            14  661  320   5    0.675      0.019       0.737   \n",
       "Fibrosis             10  725  261   4    0.735      0.014       0.714   \n",
       "Edema                15  767  213   5    0.782       0.02        0.75   \n",
       "Consolidation        36  658  297   9    0.694      0.045         0.8   \n",
       "\n",
       "                   Specificity    PPV    NPV    AUC           F1 Threshold  \n",
       "                                                                            \n",
       "Cardiomegaly             0.828  0.086  0.999  0.933  Not Defined       0.5  \n",
       "Emphysema                0.894  0.163  0.991  0.935  Not Defined       0.5  \n",
       "Effusion                 0.779  0.336  0.979  0.891  Not Defined       0.5  \n",
       "Hernia                   0.744  0.004  0.999  0.644  Not Defined       0.5  \n",
       "Infiltration             0.672  0.301  0.874  0.696  Not Defined       0.5  \n",
       "Mass                     0.833  0.202  0.984  0.888  Not Defined       0.5  \n",
       "Nodule                   0.769  0.113  0.972  0.745  Not Defined       0.5  \n",
       "Atelectasis              0.725  0.204  0.956  0.781  Not Defined       0.5  \n",
       "Pneumothorax             0.811  0.116   0.99  0.826  Not Defined       0.5  \n",
       "Pleural_Thickening       0.734  0.085  0.994  0.868  Not Defined       0.5  \n",
       "Pneumonia                0.674  0.042  0.992  0.762  Not Defined       0.5  \n",
       "Fibrosis                 0.735  0.037  0.995  0.801  Not Defined       0.5  \n",
       "Edema                    0.783  0.066  0.994  0.856  Not Defined       0.5  \n",
       "Consolidation            0.689  0.108  0.987  0.799  Not Defined       0.5  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
    "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv, auc=roc_auc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_x5U0YTYgZaz"
   },
   "source": [
    "<a name='4'></a>\n",
    "## 4. Confidence Intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dBE9owjVgZa0"
   },
   "source": [
    "Of course our dataset is only a sample of the real world, and our calculated values for all above metrics is an estimate of the real world values. It would be good to quantify this uncertainty due to the sampling of our dataset. We'll do this through the use of confidence intervals. A 95\\% confidence interval for an estimate $\\hat{s}$ of a parameter $s$ is an interval $I = (a, b)$ such that 95\\% of the time when the experiment is run, the true value $s$ is contained in $I$. More concretely, if we were to run the experiment many times, then the fraction of those experiments for which $I$ contains the true parameter would tend towards 95\\%.\n",
    "\n",
    "While some estimates come with methods for computing the confidence interval analytically, more complicated statistics, such as the AUC for example, are difficult. For these we can use a method called the *bootstrap*. The bootstrap estimates the uncertainty by resampling the dataset with replacement. For each resampling $i$, we will get a new estimate, $\\hat{s}_i$. We can then estimate the distribution of $\\hat{s}$ by using the distribution of $\\hat{s}_i$ for our bootstrap samples.\n",
    "\n",
    "In the code below, we create bootstrap samples and compute sample AUCs from those samples. Note that we use stratified random sampling (sampling from the positive and negative classes separately) to make sure that members of each class are represented. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y9iFosWagZa1"
   },
   "outputs": [],
   "source": [
    "def bootstrap_auc(y, pred, classes, bootstraps = 100, fold_size = 1000):\n",
    "    statistics = np.zeros((len(classes), bootstraps))\n",
    "\n",
    "    for c in range(len(classes)):\n",
    "        df = pd.DataFrame(columns=['y', 'pred'])\n",
    "        df.loc[:, 'y'] = y[:, c]\n",
    "        df.loc[:, 'pred'] = pred[:, c]\n",
    "        # get positive examples for stratified sampling\n",
    "        df_pos = df[df.y == 1]\n",
    "        df_neg = df[df.y == 0]\n",
    "        prevalence = len(df_pos) / len(df)\n",
    "        for i in range(bootstraps):\n",
    "            # stratified sampling of positive and negative examples\n",
    "            pos_sample = df_pos.sample(n = int(fold_size * prevalence), replace=True)\n",
    "            neg_sample = df_neg.sample(n = int(fold_size * (1-prevalence)), replace=True)\n",
    "\n",
    "            y_sample = np.concatenate([pos_sample.y.values, neg_sample.y.values])\n",
    "            pred_sample = np.concatenate([pos_sample.pred.values, neg_sample.pred.values])\n",
    "            score = roc_auc_score(y_sample, pred_sample)\n",
    "            statistics[c][i] = score\n",
    "    return statistics\n",
    "\n",
    "statistics = bootstrap_auc(y, pred, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0PF5X9cBsj4"
   },
   "source": [
    "Now we can compute confidence intervals from the sample statistics that we computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "3pczjtYdgZa4",
    "outputId": "dbb0dca8-a4f2-460d-cf26-5b8d9a0e935b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Mean AUC (CI 5%-95%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>0.94 (0.90-0.96)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>0.93 (0.91-0.96)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>0.89 (0.87-0.91)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>0.65 (0.30-0.98)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>0.70 (0.66-0.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>0.89 (0.85-0.92)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>0.75 (0.69-0.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>0.78 (0.74-0.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>0.82 (0.75-0.89)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>0.87 (0.82-0.92)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>0.76 (0.67-0.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>0.80 (0.75-0.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>0.85 (0.80-0.90)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>0.80 (0.75-0.84)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Mean AUC (CI 5%-95%)\n",
       "Cardiomegaly           0.94 (0.90-0.96)\n",
       "Emphysema              0.93 (0.91-0.96)\n",
       "Effusion               0.89 (0.87-0.91)\n",
       "Hernia                 0.65 (0.30-0.98)\n",
       "Infiltration           0.70 (0.66-0.74)\n",
       "Mass                   0.89 (0.85-0.92)\n",
       "Nodule                 0.75 (0.69-0.81)\n",
       "Atelectasis            0.78 (0.74-0.81)\n",
       "Pneumothorax           0.82 (0.75-0.89)\n",
       "Pleural_Thickening     0.87 (0.82-0.92)\n",
       "Pneumonia              0.76 (0.67-0.84)\n",
       "Fibrosis               0.80 (0.75-0.85)\n",
       "Edema                  0.85 (0.80-0.90)\n",
       "Consolidation          0.80 (0.75-0.84)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "util.print_confidence_intervals(class_labels, statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hVwBqqGFgZa8"
   },
   "source": [
    "As you can see, our confidence intervals are much wider for some classes than for others. Hernia, for example, has an interval around (0.30 - 0.98), indicating that we can't be certain it is better than chance (at 0.5). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mu-YFGp-gZam"
   },
   "source": [
    "<a name='5'></a>\n",
    "## 5. Precision-Recall Curve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qFHQ1N-W5uki"
   },
   "source": [
    "Precision-Recall is a useful measure of success of prediction when the classes are very imbalanced. \n",
    "\n",
    "In information retrieval\n",
    "- Precision is a measure of result relevancy and that is equivalent to our previously defined PPV. \n",
    "- Recall is a measure of how many truly relevant results are returned and that is equivalent to our previously defined sensitivity measure.\n",
    "\n",
    "The precision-recall curve (PRC) shows the trade-off between precision and recall for different thresholds. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. \n",
    "\n",
    "High scores for both show that the classifier is returning accurate results (high precision), as well as returning a majority of all positive results (high recall).\n",
    "\n",
    "Run the following cell to generate a PRC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "-ZauXuN2gZao",
    "outputId": "c54c393a-bcc7-453f-f6ca-e0676328bd09"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "util.get_curve(y, pred, class_labels, curve='prc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2M_NqLxggZau"
   },
   "source": [
    "<a name='6'></a>\n",
    "## 6. F1 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NRDnbrt4gZaw"
   },
   "source": [
    "F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. \n",
    "\n",
    "Again, we can simply use `sklearn`'s utility metric function of `f1_score` to add this measure to our performance table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "colab_type": "code",
    "id": "oNHl3FBUgZax",
    "outputId": "c3d45590-b4dc-4f05-827b-54961bd628ff"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Prevalence</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>AUC</th>\n",
       "      <th>F1</th>\n",
       "      <th>Threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>16</td>\n",
       "      <td>814</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emphysema</th>\n",
       "      <td>20</td>\n",
       "      <td>869</td>\n",
       "      <td>103</td>\n",
       "      <td>8</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effusion</th>\n",
       "      <td>99</td>\n",
       "      <td>690</td>\n",
       "      <td>196</td>\n",
       "      <td>15</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernia</th>\n",
       "      <td>1</td>\n",
       "      <td>743</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Infiltration</th>\n",
       "      <td>114</td>\n",
       "      <td>543</td>\n",
       "      <td>265</td>\n",
       "      <td>78</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>40</td>\n",
       "      <td>789</td>\n",
       "      <td>158</td>\n",
       "      <td>13</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nodule</th>\n",
       "      <td>28</td>\n",
       "      <td>731</td>\n",
       "      <td>220</td>\n",
       "      <td>21</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>64</td>\n",
       "      <td>657</td>\n",
       "      <td>249</td>\n",
       "      <td>30</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>24</td>\n",
       "      <td>785</td>\n",
       "      <td>183</td>\n",
       "      <td>8</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural_Thickening</th>\n",
       "      <td>24</td>\n",
       "      <td>713</td>\n",
       "      <td>259</td>\n",
       "      <td>4</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>14</td>\n",
       "      <td>661</td>\n",
       "      <td>320</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibrosis</th>\n",
       "      <td>10</td>\n",
       "      <td>725</td>\n",
       "      <td>261</td>\n",
       "      <td>4</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>15</td>\n",
       "      <td>767</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>0.782</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>36</td>\n",
       "      <td>658</td>\n",
       "      <td>297</td>\n",
       "      <td>9</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TP   TN   FP  FN Accuracy Prevalence Sensitivity  \\\n",
       "                                                                        \n",
       "Cardiomegaly         16  814  169   1     0.83      0.017       0.941   \n",
       "Emphysema            20  869  103   8    0.889      0.028       0.714   \n",
       "Effusion             99  690  196  15    0.789      0.114       0.868   \n",
       "Hernia                1  743  255   1    0.744      0.002         0.5   \n",
       "Infiltration        114  543  265  78    0.657      0.192       0.594   \n",
       "Mass                 40  789  158  13    0.829      0.053       0.755   \n",
       "Nodule               28  731  220  21    0.759      0.049       0.571   \n",
       "Atelectasis          64  657  249  30    0.721      0.094       0.681   \n",
       "Pneumothorax         24  785  183   8    0.809      0.032        0.75   \n",
       "Pleural_Thickening   24  713  259   4    0.737      0.028       0.857   \n",
       "Pneumonia            14  661  320   5    0.675      0.019       0.737   \n",
       "Fibrosis             10  725  261   4    0.735      0.014       0.714   \n",
       "Edema                15  767  213   5    0.782       0.02        0.75   \n",
       "Consolidation        36  658  297   9    0.694      0.045         0.8   \n",
       "\n",
       "                   Specificity    PPV    NPV    AUC     F1 Threshold  \n",
       "                                                                      \n",
       "Cardiomegaly             0.828  0.086  0.999  0.933  0.158       0.5  \n",
       "Emphysema                0.894  0.163  0.991  0.935  0.265       0.5  \n",
       "Effusion                 0.779  0.336  0.979  0.891  0.484       0.5  \n",
       "Hernia                   0.744  0.004  0.999  0.644  0.008       0.5  \n",
       "Infiltration             0.672  0.301  0.874  0.696  0.399       0.5  \n",
       "Mass                     0.833  0.202  0.984  0.888  0.319       0.5  \n",
       "Nodule                   0.769  0.113  0.972  0.745  0.189       0.5  \n",
       "Atelectasis              0.725  0.204  0.956  0.781  0.314       0.5  \n",
       "Pneumothorax             0.811  0.116   0.99  0.826  0.201       0.5  \n",
       "Pleural_Thickening       0.734  0.085  0.994  0.868  0.154       0.5  \n",
       "Pneumonia                0.674  0.042  0.992  0.762  0.079       0.5  \n",
       "Fibrosis                 0.735  0.037  0.995  0.801   0.07       0.5  \n",
       "Edema                    0.783  0.066  0.994  0.856  0.121       0.5  \n",
       "Consolidation            0.689  0.108  0.987  0.799   0.19       0.5  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "util.get_performance_metrics(y, pred, class_labels, acc=get_accuracy, prevalence=get_prevalence, \n",
    "                        sens=get_sensitivity, spec=get_specificity, ppv=get_ppv, npv=get_npv, auc=roc_auc_score,f1=f1_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kE9MV08bgZa9"
   },
   "source": [
    "<a name='7'></a>\n",
    "## 7. Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LOSnToD4gZa9"
   },
   "source": [
    "When performing classification we often want not only to predict the class label, but also obtain a probability of each label. This probability would ideally give us some kind of confidence on the prediction. In order to observe how our model's generated probabilities are aligned with the real probabilities, we can plot what's called a *calibration curve*. \n",
    "\n",
    "In order to generate a calibration plot, we first bucketize our predictions to a fixed number of separate bins (e.g. 5) between 0 and 1. We then calculate a point for each bin: the x-value for each point is the mean for the probability that our model has assigned to these points and the y-value for each point fraction of true positives in that bin. We then plot these points in a linear plot. A well-calibrated model has a calibration curve that almost aligns with the y=x line.\n",
    "\n",
    "The `sklearn` library has a utility `calibration_curve` for generating a calibration plot. Let's use it and take a look at our model's calibration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VnomxuuJgZa-"
   },
   "outputs": [],
   "source": [
    "from sklearn.calibration import calibration_curve\n",
    "def plot_calibration_curve(y, pred):\n",
    "    plt.figure(figsize=(20, 20))\n",
    "    for i in range(len(class_labels)):\n",
    "        plt.subplot(4, 4, i + 1)\n",
    "        fraction_of_positives, mean_predicted_value = calibration_curve(y[:,i], pred[:,i], n_bins=20)\n",
    "        plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "        plt.plot(mean_predicted_value, fraction_of_positives, marker='.')\n",
    "        plt.xlabel(\"Predicted Value\")\n",
    "        plt.ylabel(\"Fraction of Positives\")\n",
    "        plt.title(class_labels[i])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P8qlOhMjgZbB",
    "outputId": "b4a496d7-032f-42e2-a31e-b4913c982a94"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_curve(y, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FpFvjFXhgZbD"
   },
   "source": [
    "As the above plots show, for most predictions our model's calibration plot does not resemble a well calibrated plot. How can we fix that?...\n",
    "\n",
    "Thankfully, there is a very useful method called [Platt scaling](https://en.wikipedia.org/wiki/Platt_scaling) which works by fitting a logistic regression model to our model's scores. To build this model, we will be using the training portion of our dataset to generate the linear model and then will use the model to calibrate the predictions for our test portion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fxFg3dvXgZbE",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR \n",
    "\n",
    "y_train = train_results[class_labels].values\n",
    "pred_train = train_results[pred_labels].values\n",
    "pred_calibrated = np.zeros_like(pred)\n",
    "\n",
    "for i in range(len(class_labels)):\n",
    "    lr = LR(solver='liblinear', max_iter=10000)\n",
    "    lr.fit(pred_train[:, i].reshape(-1, 1), y_train[:, i])    \n",
    "    pred_calibrated[:, i] = lr.predict_proba(pred[:, i].reshape(-1, 1))[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k-XNpul1gZbH",
    "outputId": "2cfe338a-6a19-4b34-a189-e5126d79e252"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_calibration_curve(y[:,], pred_calibrated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# That's it!\n",
    "Congratulations! That was a lot of metrics to get familiarized with. \n",
    "We hope that you feel a lot more confident in your understanding of medical diagnostic evaluation and test your models correctly in your future work :)"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}