MURA_abnormality_detection-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MURA | Abnormality detection - Notebook 2/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook 1/2 : https://github.com/piegu/fastai-projects/blob/master/MURA_abnormality_detection.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Author: [Pierre Guillou](https://www.linkedin.com/in/pierreguillou)\n",
    "- Date: March 2019\n",
    "- MURA Dataset: https://stanfordmlgroup.github.io/competitions/mura/\n",
    "- Ref: [Fastai v1](https://docs.fast.ai/) (Deep Learning library on PyTorch)\n",
    "- Post in medium: https://medium.com/@pierre_guillou/2-2-fastai-the-new-radiology-tool-9f0b7db7bf91"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is MURA?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MURA (musculoskeletal radiographs) is a large dataset of bone X-rays. Algorithms are tasked with determining whether an X-ray study is normal or abnormal.\n",
    "\n",
    "Musculoskeletal conditions affect more than 1.7 billion people worldwide, and are the most common cause of severe, long-term pain and disability, with 30 million emergency department visits annually and increasing. We hope that our dataset can lead to significant advances in medical imaging technologies which can diagnose at the level of experts, towards improving healthcare access in parts of the world where access to skilled radiologists is limited.\n",
    "\n",
    "MURA is one of the largest public radiographic image datasets. We're making this dataset available to the community and hosting a competition to see if your models can perform as well as radiologists on the task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "from fastai.widgets import *\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.callbacks import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fastai: 1.0.50.post1\n",
      "cuda: True\n"
     ]
    }
   ],
   "source": [
    "import fastai\n",
    "print(f'fastai: {fastai.__version__}')\n",
    "print(f'cuda: {torch.cuda.is_available()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/jupyter/tutorials/data')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Config.data_path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid_labeled_studies.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/results_densenet169_112.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/results_densenet169_320.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train_image_paths.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train_labeled_studies.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid_image_paths.csv'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/data2')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = Config.data_path() / 'MURA-v1.1'\n",
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MURA-v1.1/train/XR_SHOULDER/patient00001/study1_positive/image1.png'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(path / 'train_image_paths.csv', header=None, names=['image'])\n",
    "df_train.image[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MURA-v1.1/valid/XR_WRIST/patient11185/study1_positive/image1.png'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid = pd.read_csv(path / 'valid_image_paths.csv', header=None, names=['image'])\n",
    "df_valid.image[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00005/study...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  label\n",
       "0  MURA-v1.1/train/XR_SHOULDER/patient00001/study...      1\n",
       "1  MURA-v1.1/train/XR_SHOULDER/patient00002/study...      1\n",
       "2  MURA-v1.1/train/XR_SHOULDER/patient00003/study...      1\n",
       "3  MURA-v1.1/train/XR_SHOULDER/patient00004/study...      1\n",
       "4  MURA-v1.1/train/XR_SHOULDER/patient00005/study...      1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_label = pd.read_csv(path / 'train_labeled_studies.csv', header=None, names=['image', 'label'])\n",
    "df_train_label.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create folder data2 to train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create data2 with data to train our models\n",
    "path_train = path / 'data2/train'\n",
    "path_valid = path / 'data2/valid'\n",
    "path_train.mkdir(parents=True, exist_ok=True)\n",
    "path_valid.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_train_neg = path_train / '0'\n",
    "path_train_pos = path_train / '1'\n",
    "path_train_neg.mkdir(parents=True, exist_ok=True)\n",
    "path_train_pos.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "path_valid_neg = path_valid / '0'\n",
    "path_valid_pos = path_valid / '1'\n",
    "path_valid_neg.mkdir(parents=True, exist_ok=True)\n",
    "path_valid_pos.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36808\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train/XR_FINGER/patient04021/study1_negative/image1.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train/XR_FINGER/patient03816/study1_negative/image1.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train/XR_FINGER/patient04345/study1_negative/image2.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train/XR_FINGER/patient04345/study1_negative/image3.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/train/XR_FINGER/patient04345/study1_negative/image1.png')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames_train = get_image_files(path/'train', recurse=True)\n",
    "print(len(fnames_train))\n",
    "fnames_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3197\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid/XR_FINGER/patient11855/study1_negative/image1.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid/XR_FINGER/patient11606/study1_negative/image1.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid/XR_FINGER/patient11513/study1_negative/image1.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid/XR_FINGER/patient11911/study1_positive/image2.png'),\n",
       " PosixPath('/home/jupyter/tutorials/data/MURA-v1.1/valid/XR_FINGER/patient11911/study1_positive/image3.png')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames_valid = get_image_files(path/'valid', recurse=True)\n",
    "print(len(fnames_valid))\n",
    "fnames_valid[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copy images into data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pat_label = re.compile(r'/XR_([^/]+)/[^/]+/[^/]+/[^/]+.png$')\n",
    "pat_patient = re.compile(r'/[^/]+/patient([^/]+)/[^/]+/[^/]+.png$')\n",
    "pat_study = re.compile(r'/[^/]+/[^/]+/([^/]+)/[^/]+.png$')\n",
    "# pat_study_negpos = re.compile(r'\\\\[^\\\\]+\\\\[^\\\\]+\\\\study\\d+_([^\\\\]+)\\\\[^\\\\]+.png$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.42 s, sys: 6.41 s, total: 9.83 s\n",
      "Wall time: 2min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# copy all train images in corresponding class folders under MURA-v1.1/data2/train\n",
    "for src in fnames_train:\n",
    "    # get image label\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1)\n",
    "    # get patient number\n",
    "    patient = pat_patient.search(str(src))\n",
    "    patient = patient.group(1)\n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1)  \n",
    "    # create class folder if necessary\n",
    "    if 'negative' in study:\n",
    "        path_label = path_train_neg\n",
    "    else:\n",
    "        path_label = path_train_pos\n",
    "    # copy image to its class folder\n",
    "    img_name = label + '_patient' + patient + '_' + study + '_' + src.name\n",
    "    dest = path_label / img_name\n",
    "    shutil.copy(str(src), str(dest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 336 ms, sys: 564 ms, total: 900 ms\n",
      "Wall time: 11.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# copy all valid images in corresponding class folders under MURA-v1.1/data2/valid\n",
    "for src in fnames_valid:\n",
    "    # get image label\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1)\n",
    "    # get patient number\n",
    "    patient = pat_patient.search(str(src))\n",
    "    patient = patient.group(1)\n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1)  \n",
    "    # create class folder if necessary\n",
    "    if 'negative' in study:\n",
    "        path_label = path_valid_neg\n",
    "    else:\n",
    "        path_label = path_valid_pos\n",
    "    # copy image to its class folder\n",
    "    img_name = label + '_patient' + patient + '_' + study + '_' + src.name\n",
    "    dest = path_label / img_name\n",
    "    shutil.copy(str(src), str(dest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of studies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pat_label = re.compile(r'/XR_([^/]+)/[^/]+/[^/]+/[^/]+.png$')\n",
    "pat_patient = re.compile(r'/[^/]+/patient([^/]+)/[^/]+/[^/]+.png$')\n",
    "pat_study = re.compile(r'/([^/]+)_[^/]+/[^/]+.png$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mura = ['elbow', 'finger', 'forearm', 'hand', 'humerus', 'shoulder', 'wrist']\n",
    "\n",
    "study_train_dict = dict()\n",
    "study_valid_dict = dict()\n",
    "\n",
    "for m in mura:\n",
    "    study_train_dict[m] = list()\n",
    "    study_valid_dict[m] = list()\n",
    "    \n",
    "for src in fnames_train:\n",
    "    # get image label\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1)\n",
    "    # get patient number\n",
    "    patient = pat_patient.search(str(src))\n",
    "    patient = patient.group(1)\n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1)\n",
    "    # add to label list\n",
    "    s = 'patient' + patient + '_' + study\n",
    "    study_train_dict[label.lower()].append(s)\n",
    "\n",
    "for src in fnames_valid:\n",
    "    # get image label\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1)\n",
    "    # get patient number\n",
    "    patient = pat_patient.search(str(src))\n",
    "    patient = patient.group(1)\n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1)\n",
    "    # add to label list\n",
    "    s = 'patient' + patient + '_' + study\n",
    "    study_valid_dict[label.lower()].append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_train_studies = 0\n",
    "num_valid_studies = 0\n",
    "\n",
    "for m in mura:\n",
    "    # train\n",
    "    myset = set(study_train_dict[m])\n",
    "    num_train_studies += len(myset)\n",
    "    # valid\n",
    "    myset = set(study_valid_dict[m])\n",
    "    num_valid_studies += len(myset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13457, 1199, 14863)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 207 studies in test\n",
    "num_train_studies, num_valid_studies, num_train_studies + num_valid_studies + 207"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training with densenet169 (parameters from paper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Input images size = 112x112"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 112\n",
    "bs = 8\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:14:56 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.740211</td>\n",
       "      <td>0.662548</td>\n",
       "      <td>0.353144</td>\n",
       "      <td>0.646856</td>\n",
       "      <td>0.285176</td>\n",
       "      <td>07:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.680526</td>\n",
       "      <td>0.592127</td>\n",
       "      <td>0.297779</td>\n",
       "      <td>0.702221</td>\n",
       "      <td>0.396014</td>\n",
       "      <td>07:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.562902</td>\n",
       "      <td>0.555230</td>\n",
       "      <td>0.273381</td>\n",
       "      <td>0.726619</td>\n",
       "      <td>0.443564</td>\n",
       "      <td>07:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.523256</td>\n",
       "      <td>0.498727</td>\n",
       "      <td>0.233344</td>\n",
       "      <td>0.766656</td>\n",
       "      <td>0.526991</td>\n",
       "      <td>07:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.526669</td>\n",
       "      <td>0.489869</td>\n",
       "      <td>0.228339</td>\n",
       "      <td>0.771661</td>\n",
       "      <td>0.538790</td>\n",
       "      <td>07:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.498140</td>\n",
       "      <td>0.476925</td>\n",
       "      <td>0.223022</td>\n",
       "      <td>0.776978</td>\n",
       "      <td>0.549446</td>\n",
       "      <td>07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.493177</td>\n",
       "      <td>0.466605</td>\n",
       "      <td>0.209884</td>\n",
       "      <td>0.790116</td>\n",
       "      <td>0.576147</td>\n",
       "      <td>07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.500830</td>\n",
       "      <td>0.484909</td>\n",
       "      <td>0.220206</td>\n",
       "      <td>0.779794</td>\n",
       "      <td>0.552917</td>\n",
       "      <td>07:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.479140</td>\n",
       "      <td>0.472091</td>\n",
       "      <td>0.210197</td>\n",
       "      <td>0.789803</td>\n",
       "      <td>0.574351</td>\n",
       "      <td>07:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.489796</td>\n",
       "      <td>0.455843</td>\n",
       "      <td>0.206756</td>\n",
       "      <td>0.793244</td>\n",
       "      <td>0.583546</td>\n",
       "      <td>07:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with val_loss value: 0.6625477075576782.\n",
      "Better model found at epoch 1 with val_loss value: 0.5921269059181213.\n",
      "Better model found at epoch 2 with val_loss value: 0.5552298426628113.\n",
      "Better model found at epoch 3 with val_loss value: 0.4987266957759857.\n",
      "Better model found at epoch 4 with val_loss value: 0.48986929655075073.\n",
      "Better model found at epoch 5 with val_loss value: 0.4769245982170105.\n",
      "Better model found at epoch 6 with val_loss value: 0.4666053354740143.\n",
      "Better model found at epoch 9 with val_loss value: 0.4558432996273041.\n"
     ]
    }
   ],
   "source": [
    "lr = 1e-4\n",
    "learn.fit_one_cycle(10,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-1-n2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-1-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 50:03 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.498136</td>\n",
       "      <td>0.470024</td>\n",
       "      <td>0.213012</td>\n",
       "      <td>0.786988</td>\n",
       "      <td>0.569107</td>\n",
       "      <td>09:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.489590</td>\n",
       "      <td>0.470511</td>\n",
       "      <td>0.211761</td>\n",
       "      <td>0.788239</td>\n",
       "      <td>0.571498</td>\n",
       "      <td>10:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.498423</td>\n",
       "      <td>0.448467</td>\n",
       "      <td>0.204254</td>\n",
       "      <td>0.795746</td>\n",
       "      <td>0.587606</td>\n",
       "      <td>10:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.454724</td>\n",
       "      <td>0.443896</td>\n",
       "      <td>0.200500</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>0.596168</td>\n",
       "      <td>09:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.457709</td>\n",
       "      <td>0.442879</td>\n",
       "      <td>0.196747</td>\n",
       "      <td>0.803253</td>\n",
       "      <td>0.602720</td>\n",
       "      <td>09:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with val_loss value: 0.4700239300727844.\n",
      "Better model found at epoch 2 with val_loss value: 0.4484669268131256.\n",
      "Better model found at epoch 3 with val_loss value: 0.4438956081867218.\n",
      "Better model found at epoch 4 with val_loss value: 0.44287851452827454.\n"
     ]
    }
   ],
   "source": [
    "lr = 1e-5\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-2-n2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-2-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 37:43 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.468389</td>\n",
       "      <td>0.436768</td>\n",
       "      <td>0.195183</td>\n",
       "      <td>0.804817</td>\n",
       "      <td>0.606974</td>\n",
       "      <td>07:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.421702</td>\n",
       "      <td>0.446237</td>\n",
       "      <td>0.197060</td>\n",
       "      <td>0.802940</td>\n",
       "      <td>0.601840</td>\n",
       "      <td>07:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.437157</td>\n",
       "      <td>0.438054</td>\n",
       "      <td>0.194870</td>\n",
       "      <td>0.805130</td>\n",
       "      <td>0.607211</td>\n",
       "      <td>07:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.487413</td>\n",
       "      <td>0.444061</td>\n",
       "      <td>0.196121</td>\n",
       "      <td>0.803879</td>\n",
       "      <td>0.603640</td>\n",
       "      <td>07:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.495843</td>\n",
       "      <td>0.446020</td>\n",
       "      <td>0.195809</td>\n",
       "      <td>0.804191</td>\n",
       "      <td>0.604283</td>\n",
       "      <td>07:31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with val_loss value: 0.43676796555519104.\n"
     ]
    }
   ],
   "source": [
    "lr = 1e-6\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-3-n2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unfreeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.purge()\n",
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 48:34 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.454974</td>\n",
       "      <td>0.443591</td>\n",
       "      <td>0.199562</td>\n",
       "      <td>0.800438</td>\n",
       "      <td>0.596850</td>\n",
       "      <td>09:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.443154</td>\n",
       "      <td>0.444782</td>\n",
       "      <td>0.196747</td>\n",
       "      <td>0.803253</td>\n",
       "      <td>0.602849</td>\n",
       "      <td>09:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.450476</td>\n",
       "      <td>0.445256</td>\n",
       "      <td>0.193619</td>\n",
       "      <td>0.806381</td>\n",
       "      <td>0.608676</td>\n",
       "      <td>09:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.474479</td>\n",
       "      <td>0.439208</td>\n",
       "      <td>0.191742</td>\n",
       "      <td>0.808258</td>\n",
       "      <td>0.613704</td>\n",
       "      <td>09:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.454220</td>\n",
       "      <td>0.448611</td>\n",
       "      <td>0.199249</td>\n",
       "      <td>0.800751</td>\n",
       "      <td>0.597340</td>\n",
       "      <td>09:43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with val_loss value: 0.4435909390449524.\n",
      "Better model found at epoch 3 with val_loss value: 0.4392077624797821.\n"
     ]
    }
   ],
   "source": [
    "lr=4e-6\n",
    "learn.fit_one_cycle(5,max_lr=slice(lr/100,lr),callbacks=[ShowGraph(learn),SaveModelCallback(learn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-4-n2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results by image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 112\n",
    "bs = 8\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-4-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "\n",
    "losses,idxs = interp.top_losses()\n",
    "\n",
    "len(data.valid_ds)==len(losses)==len(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first interpretation\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1', '0', 408), ('0', '1', 205)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp.most_confused(min_val=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.plot_top_losses(9, figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy and kappa by study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source for kappa score calculation: [Cohen's kappa in plain English](https://stats.stackexchange.com/questions/82162/cohens-kappa-in-plain-english)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-4-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jupyter/tutorials/data/MURA-v1.1/data2/valid/1/HAND_patient11538_study1_positive_image1.png\n"
     ]
    }
   ],
   "source": [
    "for img_url in data.valid_ds.x.items:\n",
    "    print(img_url)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions on validation\n",
    "preds_val, y_val = learn.get_preds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file to save \n",
    "file = path / 'results_densenet169_112.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          accuracy  kappa\n",
      "elbow        0.869  0.737\n",
      "finger       0.764  0.530\n",
      "forearm      0.814  0.628\n",
      "hand         0.796  0.556\n",
      "humerus      0.837  0.673\n",
      "shoulder     0.773  0.546\n",
      "wrist        0.821  0.632\n",
      "overall      0.808  0.614\n",
      "CPU times: user 544 ms, sys: 0 ns, total: 544 ms\n",
      "Wall time: 139 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "probs_of_images_by_studies = dict()\n",
    "num_img_by_studies = dict()\n",
    "labels_of_studies = dict()\n",
    "num_studies_by_label = dict()\n",
    "list_studies_by_label = dict()\n",
    "idx_by_studies = dict()\n",
    "results_by_label = dict()\n",
    "acc_by_label = OrderedDict()\n",
    "kappa_score = OrderedDict()\n",
    "\n",
    "pat_label = re.compile(r'/([^/]+)_patient[^/]+.png$')\n",
    "pat_study = re.compile(r'/([^/]+)_[^_]+.png$')\n",
    "\n",
    "for idx, src in enumerate(data.valid_ds.x.items):\n",
    "    # get label name\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1) \n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1) \n",
    "    # sum probabilities by study\n",
    "    if study in probs_of_images_by_studies:\n",
    "        probs_of_images_by_studies[study] += preds_val[idx,:].clone()\n",
    "        num_img_by_studies[study] += 1\n",
    "        idx_by_studies[study].append(idx)\n",
    "    else:\n",
    "        probs_of_images_by_studies[study] = preds_val[idx,:].clone()\n",
    "        num_img_by_studies[study] = 1\n",
    "        labels_of_studies[study] = label\n",
    "        idx_by_studies[study] = [idx]\n",
    "        \n",
    "for m in mura:\n",
    "    num_studies_by_label[m] = sum([1 for k,v in labels_of_studies.items() if v.lower() == m])\n",
    "    list_studies_by_label[m] = [k for k,v in labels_of_studies.items() if v.lower() == m]\n",
    "    \n",
    "for m in mura:\n",
    "    list_studies = list_studies_by_label[m]\n",
    "    TN, FP, TP, FN = 0, 0, 0, 0\n",
    "    for study in list_studies:\n",
    "        preds_val2 = preds_val.clone()\n",
    "        idxs = idx_by_studies[study]\n",
    "        preds = preds_val2[idxs]\n",
    "        y_hat = np.argmax(preds, axis=1)\n",
    "        y = y_val[idxs]\n",
    "        if 'negative' in study:\n",
    "            TN += sum(y_hat.numpy() == y.numpy())\n",
    "            FP += sum(y_hat.numpy() != y.numpy())\n",
    "        else:\n",
    "            TP += sum(y_hat.numpy() == y.numpy())\n",
    "            FN += sum(y_hat.numpy() != y.numpy())\n",
    "    results_by_label[m] = [TN, FP, TP, FN]\n",
    "    # accuracy\n",
    "    acc_by_label[m] = (TN + TP) / (TN+FP+FN+TP)\n",
    "    # kappa\n",
    "    ground_truth_neg = TN+FN\n",
    "    ground_truth_pos = FP+TP\n",
    "    classifier_neg = TN+FP\n",
    "    classifier_pos = FN+TP\n",
    "    total = classifier_neg + classifier_pos\n",
    "    observed_accuracy = (TN + TP) / total\n",
    "    expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "    kappa_score[m] = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "# overall accuracy and kappa\n",
    "[TN, FP, TP, FN] = [0,0,0,0]\n",
    "for m in mura:\n",
    "    results = results_by_label[m]\n",
    "    TN += results[0]\n",
    "    FP += results[1]\n",
    "    TP += results[2]\n",
    "    FN += results[3]\n",
    "# overall accuracy \n",
    "acc_by_label['overall'] = (TN + TP) / (TN+FP+FN+TP)\n",
    "# overall kappa\n",
    "ground_truth_neg = TN+FN\n",
    "ground_truth_pos = FP+TP\n",
    "classifier_neg = TN+FP\n",
    "classifier_pos = FN+TP\n",
    "total = classifier_neg + classifier_pos\n",
    "observed_accuracy = (TN + TP) / total\n",
    "expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "kappa_score['overall'] = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "# Create DataFrame\n",
    "for k, v in acc_by_label.items():\n",
    "    acc_by_label[k] = round(v, 3)\n",
    "for k, v in kappa_score.items():\n",
    "    kappa_score[k] = round(v, 3)\n",
    "    \n",
    "df = pd.DataFrame([acc_by_label, kappa_score], index =['accuracy', 'kappa']) \n",
    "df.name = 'Results on validation at a study level'\n",
    "df = df.transpose()\n",
    "\n",
    "# save df\n",
    "df.to_csv(file,header=True)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check overall accuracy and kappa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first interpretation\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = interp.confusion_matrix()\n",
    "\n",
    "TN = cm[0,0]\n",
    "FP = cm[0,1]\n",
    "FN = cm[1,0]\n",
    "TP = cm[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.808, 0.614)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy\n",
    "acc = (TN + TP) / (TN+FP+FN+TP)\n",
    "\n",
    "# kappa\n",
    "ground_truth_neg = TN+FN\n",
    "ground_truth_pos = FP+TP\n",
    "classifier_neg = TN+FP\n",
    "classifier_pos = FN+TP\n",
    "total = classifier_neg + classifier_pos\n",
    "observed_accuracy = (TN + TP) / total\n",
    "expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "kappa_score = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "round(acc,3), round(kappa_score,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input images size = 320x320"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1479"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 320\n",
    "bs = 8\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)\n",
    "learn.load('densenet169-stage-4-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# freeze the model\n",
    "learn.purge()\n",
    "learn.freeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:32:21 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.487032</td>\n",
       "      <td>0.463355</td>\n",
       "      <td>0.206444</td>\n",
       "      <td>0.793556</td>\n",
       "      <td>0.582541</td>\n",
       "      <td>18:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.481628</td>\n",
       "      <td>0.418717</td>\n",
       "      <td>0.183610</td>\n",
       "      <td>0.816390</td>\n",
       "      <td>0.630148</td>\n",
       "      <td>18:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.468028</td>\n",
       "      <td>0.440058</td>\n",
       "      <td>0.194870</td>\n",
       "      <td>0.805130</td>\n",
       "      <td>0.605613</td>\n",
       "      <td>18:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.453611</td>\n",
       "      <td>0.423079</td>\n",
       "      <td>0.182358</td>\n",
       "      <td>0.817642</td>\n",
       "      <td>0.631395</td>\n",
       "      <td>18:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.430081</td>\n",
       "      <td>0.408062</td>\n",
       "      <td>0.177979</td>\n",
       "      <td>0.822021</td>\n",
       "      <td>0.641063</td>\n",
       "      <td>18:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with kappa_score value: 0.582541286945343.\n",
      "Better model found at epoch 1 with kappa_score value: 0.6301482915878296.\n",
      "Better model found at epoch 3 with kappa_score value: 0.631394624710083.\n",
      "Better model found at epoch 4 with kappa_score value: 0.6410631537437439.\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-4\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn, monitor='kappa_score', mode='max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-5-n2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = None\n",
    "gc.collect()\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)\n",
    "learn.load('densenet169-stage-5-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:32:49 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.449537</td>\n",
       "      <td>0.423536</td>\n",
       "      <td>0.176728</td>\n",
       "      <td>0.823272</td>\n",
       "      <td>0.643355</td>\n",
       "      <td>18:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.427389</td>\n",
       "      <td>0.427020</td>\n",
       "      <td>0.185799</td>\n",
       "      <td>0.814201</td>\n",
       "      <td>0.624796</td>\n",
       "      <td>18:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.418857</td>\n",
       "      <td>0.424010</td>\n",
       "      <td>0.183610</td>\n",
       "      <td>0.816390</td>\n",
       "      <td>0.629187</td>\n",
       "      <td>18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.425186</td>\n",
       "      <td>0.413969</td>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.825774</td>\n",
       "      <td>0.648481</td>\n",
       "      <td>18:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.443072</td>\n",
       "      <td>0.408605</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.649103</td>\n",
       "      <td>18:33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with kappa_score value: 0.6433550119400024.\n",
      "Better model found at epoch 3 with kappa_score value: 0.648481011390686.\n",
      "Better model found at epoch 4 with kappa_score value: 0.6491025686264038.\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-4\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn, monitor='kappa_score', mode='max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-6-n2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bs = 16 (to go a bit faster in training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10077"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 320\n",
    "bs = 16\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)\n",
    "learn.load('densenet169-stage-6-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:24:32 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.413274</td>\n",
       "      <td>0.408363</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.649027</td>\n",
       "      <td>16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.385019</td>\n",
       "      <td>0.403746</td>\n",
       "      <td>0.170472</td>\n",
       "      <td>0.829528</td>\n",
       "      <td>0.656648</td>\n",
       "      <td>16:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.399650</td>\n",
       "      <td>0.406853</td>\n",
       "      <td>0.170160</td>\n",
       "      <td>0.829840</td>\n",
       "      <td>0.656880</td>\n",
       "      <td>16:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.425203</td>\n",
       "      <td>0.404733</td>\n",
       "      <td>0.169221</td>\n",
       "      <td>0.830779</td>\n",
       "      <td>0.658818</td>\n",
       "      <td>16:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.402293</td>\n",
       "      <td>0.407672</td>\n",
       "      <td>0.169847</td>\n",
       "      <td>0.830153</td>\n",
       "      <td>0.657409</td>\n",
       "      <td>16:54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with kappa_score value: 0.64902663230896.\n",
      "Better model found at epoch 1 with kappa_score value: 0.6566483974456787.\n",
      "Better model found at epoch 2 with kappa_score value: 0.6568800210952759.\n",
      "Better model found at epoch 3 with kappa_score value: 0.6588183641433716.\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-5\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn, monitor='kappa_score', mode='max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-7-n2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:24:36 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.374961</td>\n",
       "      <td>0.406740</td>\n",
       "      <td>0.170472</td>\n",
       "      <td>0.829528</td>\n",
       "      <td>0.656166</td>\n",
       "      <td>16:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.376538</td>\n",
       "      <td>0.403568</td>\n",
       "      <td>0.168908</td>\n",
       "      <td>0.831092</td>\n",
       "      <td>0.659458</td>\n",
       "      <td>16:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.403122</td>\n",
       "      <td>0.404368</td>\n",
       "      <td>0.165780</td>\n",
       "      <td>0.834220</td>\n",
       "      <td>0.666071</td>\n",
       "      <td>16:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.392660</td>\n",
       "      <td>0.406049</td>\n",
       "      <td>0.169221</td>\n",
       "      <td>0.830779</td>\n",
       "      <td>0.658781</td>\n",
       "      <td>16:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.376754</td>\n",
       "      <td>0.409395</td>\n",
       "      <td>0.171098</td>\n",
       "      <td>0.828902</td>\n",
       "      <td>0.654792</td>\n",
       "      <td>16:56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with kappa_score value: 0.6561656594276428.\n",
      "Better model found at epoch 1 with kappa_score value: 0.6594582200050354.\n",
      "Better model found at epoch 2 with kappa_score value: 0.6660714149475098.\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-6\n",
    "learn.fit_one_cycle(5,max_lr=lr,callbacks=[ShowGraph(learn),SaveModelCallback(learn, monitor='kappa_score', mode='max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-8-n2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unfreeze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 320\n",
    "bs = 16\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)\n",
    "learn.load('densenet169-stage-8-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.purge()\n",
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:47:32 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>kappa_score</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.391157</td>\n",
       "      <td>0.416407</td>\n",
       "      <td>0.171723</td>\n",
       "      <td>0.828277</td>\n",
       "      <td>0.653248</td>\n",
       "      <td>21:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.398008</td>\n",
       "      <td>0.398131</td>\n",
       "      <td>0.163278</td>\n",
       "      <td>0.836722</td>\n",
       "      <td>0.671413</td>\n",
       "      <td>21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.399078</td>\n",
       "      <td>0.421693</td>\n",
       "      <td>0.181733</td>\n",
       "      <td>0.818267</td>\n",
       "      <td>0.632440</td>\n",
       "      <td>21:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.396580</td>\n",
       "      <td>0.399806</td>\n",
       "      <td>0.161714</td>\n",
       "      <td>0.838286</td>\n",
       "      <td>0.674253</td>\n",
       "      <td>21:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.371800</td>\n",
       "      <td>0.408447</td>\n",
       "      <td>0.166406</td>\n",
       "      <td>0.833594</td>\n",
       "      <td>0.664394</td>\n",
       "      <td>21:27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with kappa_score value: 0.6532483696937561.\n",
      "Better model found at epoch 1 with kappa_score value: 0.6714134216308594.\n",
      "Better model found at epoch 3 with kappa_score value: 0.6742532253265381.\n"
     ]
    }
   ],
   "source": [
    "lr=3e-5\n",
    "learn.fit_one_cycle(5,max_lr=slice(lr/100,lr),callbacks=[ShowGraph(learn),SaveModelCallback(learn, monitor='kappa_score', mode='max')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('densenet169-stage-9-n2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0z+LvQJaZHQ48AswGngbOCaowEZG9Vm7bwf/OWszignKmDO/F984Zw+lj+5OU2H4H7os3LQ2L+vDwHRcBv3H3B81sYZCFiYhU19bzh7l5zHgrj25pycy4fBLnjNONdLHQ0rCoMbPLgKuB88LzkoMpSUQEVmzdwbeeCw3yd8GEgfzwvCN1dVMMtTQsrgVuAn7i7uvDI8k+FVxZItKR/XNhAXf9Ywnd0pJ5+KosPj9Wg/zFWovCIjzi663w6fDh6e5+X5CFiUjHU11bz09eXs7jH2xk6vBe/P7ySfRN18nreNDSq6HmEnreRBKQCxSZ2dvu/u0AaxORdqq8soZN2yvo0TmZHp2T6ZqaROHOKr7+t4/J2VjKDScO586zR+sEdhxp6WGo7u6+w8xuAP7q7j80s8VBFiYi7dPHm0qZ/kQOxbuqPp2XmGAkmpGUaDx42UTOGz8whhVKY1oaFklmNgD4CvC9AOsRkXbshdzN3DFrMYd1S+N3l01kT00d5RU1lFVWU1ldz6VThjCqf3qsy5RGtDQs7iH0xLv33H2BmY0A1gRXloi0J/X1zgNvrubBOXlMGd6LP105WVc2tTEtPcE9E5gZMb0OuDiookSk/aisruM7M3N5Zck2vpI1mB9fOI6UJJ2LaGtaeoJ7MPAgcALgwLvAN929IMDaRKSN21JWyY1PZLN86w6+d84YbjhpuG6oa6NaGu9/JTTEx0BgEPBieN5+mdlZZrbKzPLM7M5Gll9jZkVmlht+3RCx7GozWxN+Xd3COkUkTny8qZTzf/8eG7dX8MjVWdw4bYSCog1r6TmLvu4eGQ6Pmdlt+1vBzBKBGcDpQAGwwMxmh+/ZiPScu9/cYN1ewA+BLEJ7MjnhdUtbWK+IxNDfc0I31Q3okcYzN04lUyet27yW7lkUm9mVZpYYfl0JbG9mnSlAnruvc/dq4FngghZu70zgDXcvCQfEG8BZLVxXRGJk+64q7p69jO/MXMTkoT3519dPUFC0Ey3ds7gO+D3wAKFv+u8TGgJkfwYB+RHTBcDURtpdbGbTgNXAt9w9v4l1B7WwVhFpZRu37+bheeuZmZPPnpp6rjpuKP/3hbEk66a6dqOlV0NtInQH96fCh6F+s5/VGjs46Q2mXwSeCT8n4ybgceDUFq6LmU0HpgNkZGTspxQRCUJe4U4eeHMN/16ylaSEBC6aOIgbpw3n8H7am2hvDuVJed9m/2FRAAyJmB4MbIls4O6Rh7L+Avw8Yt2TG6w7t+EG3P0h4CGArKysz4SJiASjtq6eP7+zjt++uYbUpASmTxvJtScMo3+3tFiXJgE5lLBo7rKGBUBmeITazcClwOX7vIHZAHffGp48H1gR/vk14KfhQQsBzgDuOoRaRSRKVmzdwR2zFrF08w7OHTeAH11wpJ5U1wEcSljs95t8+GFJNxP64E8EHnX3ZWZ2D5Dt7rOBW83sfKAWKAGuCa9bYmb3EgocgHvcveQQahWRQ+Tu/H5OHr+bs4Zuacn84YpJnDNuQKzLklZi7k1/5pvZThoPBQM6ufuhhE1UZWVleXZ2dqzLEGm33lldxFWPzufccQO498KjNFxHO2FmOe6e1Vy7/X7Yu7vOUokIALNyCujeKZlfXzKe1KTEWJcjrUzXtYlIs3buqeG1Zds4b/wABUUHpbAQkWa9smQrVbX1XDxpcKxLkRhRWIhIs/6es5kRfbswYUiPWJciMaKwEJH92rS9gvkbSrh40mANBNiBKSxEZL/+/nEBZnDRRI2405EpLESkSfX1zj8WFnD8yN4M7NEp1uVIDCksRKRJ2RtLyS+p1IltUViISNP+nlNAl5REzjrqsFiXIjGmsBCRRlVW1/Hykq2cPW4AnVPiZrAGiRGFhYg06vXl29hVVatDUAIoLESkEcu2lPPgnDwG9ejE1OG9Yl2OxAHtW4rIp3buqeHXb6zm8fc30LNzCr/88ngSEnRvhSgsRITQJbIvLdnKj19aTtGuKq6YmsEdZ4yme+fkWJcmcUJhIdJB7aqq5d01RcxZWchbq4oo2lnFuEHd+ctVWYzXsB7SgMJCpIPZU1PHt57L5c0Vn1BT56SnJTFtVF/OGNufLxw9kEQddpJGKCxEOhB3565/LOHVZdu47oThnD62P5OH9iQ5Ude6yP4pLEQ6kL++t4F/LtzMd04fxS2nZca6HGlD9HVCpIN4f20xP3llBWeM7c83Tjk81uVIGxNoWJjZWWa2yszyzOzO/bT7kpm5mWWFp4eZWaWZ5YZffwqyTpH2rqC0gpufXsjwPl349SUTdDmsHLDADkOZWSIwAzgdKAAWmNlsd1/eoF06cCvwUYO3WOvuE4KqT6Q9qqqt453VxSQnGj07p9CrSwqdUxK56akcamrr+fNXJ9M1VUef5cAF+VczBchz93UAZvYscAGwvEG7e4H7gdsDrEWk3XtndRF3z17GuuLdn1lmBg9flcXIvl1jUJm0B0GGxSAgP2K6AJga2cDMJgJD3P0lM2sYFsPNbCGwA/i+u88LsFaRNmtzWSX3vricV5dtY1jvzvzpysn0TU+ldHc1pRWhV2b/dE45ol+sS5U2LMiwaOygqH+60CwBeAC4ppF2W4EMd99uZpOBf5nZke6+Y58NmE0HpgNkZGREq26RNuPJDzfyk5dDO+u3nzGKG6eNIDUpMcZVSXsUZFgUAEMipgcDWyKm04GjgLnh5/oeBsw2s/PdPRuoAnD3HDNbC4wCsiM34O4PAQ8BZGVlOSIdSF7hLu6evYzjRvTmvovHMbhn51iXJO1YkFdDLQAyzWy4maUAlwKz9y5093J37+Puw9x9GPAhcL67Z5tZ3/AJcsxsBJAJrAuwVpE25/5XV9IpOZHfXDpBQSGBCyws3L0WuBl4DVgBPO/uy8zsHjM7v5nVpwGLzWwRMAu4yd1LgqpVpK1ZsKGE15d/wk2fG0GfrqmxLkc6AHNvH0dvsrKyPDs7u/mGIm2cu/PFP77PlrJK5t5+Cp1SdI5CDp6Z5bh7VnPtdAe3SBvz6tJtLNxUxndOP0JBIa1GYSHShtTU1fPzV1cyqn9XLp6sx51K61FYiLQhz8zfxIbtFdx19hgNJS6tSmEh0kaUV9bw2zfXcNyI3px8RN9YlyMdjAaJEYlTldV1fLyplI/Wl/DRuu0szC+juraeu84ZTfjeJJFWo7AQiTPuzsycAu55cTm7qmpJMDhyYHeuOnYop4/tz9GD9chTaX0KC5E4UrK7mu+Gn2Q3dXgvbjp5JFlDe5Kelhzr0qSDU1iIxIm3Vxdx+8xFlFVUc9fZo7nhpBE6iS1xQ2EhEgd+9581/PqN1Yzq35XHrj2GIwd2j3VJIvtQWIjE2MuLt/LrN1Zz0cRB/OyL40hL1o12En906axIgNydd1YXsaGRBxIBrNq2kztmLWJSRg9+fvHRCgqJW9qzEAnIx5tK+enLK8jeWEqn5ETuvfAovhRx13V5ZQ3/82Q2XVKT+OOVk0lJ0nc3iV/66xSJso3bd/ONv33MF//wPhu2V3D3eWMZP6Q7t89cxLefy2V3VS319c5tzy6koLSSP14xif7d0mJdtsh+ac9CpIXWFu1ia9kejjgsnb7p+w4LXrhzD3NXFjFnZSH/WfkJSQkJfPO0TKZPG0GX1CS+etwwHpyzht/9Zw25+WVMHdGLt1YVce8FR5I1rFeMeiTScgoLkWaUV9Tw6zdW8eSHG6kPj+jfp2sqYwakM6x3F3Lzy1iyuRyAw7qlccXUoXzt5JH77C0kJhi3fX4UU4f35pvPLuSZ+fl8afJgrjx2aCy6JHLA9DwLaTPyCnfSNz2N7p1a5wa1unrn+ex8fvHaKsoqqrli6lDOOLI/qz/ZxYqtO1ixdQfri3czZkA3Th3dj1OO6MeYAenNDsWxfVcVry7bxsWTBuuEtsRcS59noT0LiXvz15fwwBur+WDddlKTEjjrqMO4JGsIx47oTUJAN61t2l7Bzc98zOKCco4Z1pO7z5/y6b0PJ2Ue2iB+vbumcsVU7VFI26KwkJhzdxbml2FAj84p9OycTLe0ZHI2lfKbN1fzXt52+qanctfZo9lcVsm/Fm7mhdwtZPTqzIUTBpLZP52MXp3J6NWZHp2TD3mQva3llVz+8Ifsqqrlt5dO4PzxAzVwn3R4CguJqdq6eu76xxJm5hTsM98M3KFP1xS+f+4Yrpg69NOnwn33nDG8unQbzy3I53dz8vZZLz0tiYxenRnWuwtDe4f+O6xPF0YPSKdbC8ZXKt5VxZUPf0R5RQ1P33gs4wbrTmoRCDgszOws4LdAIvCwu9/XRLsvATOBY9w9OzzvLuB6oA641d1fC7JWaX1VtXV885lcXl22ja+fPJJjhvWitKKa0ooayiuq6d01lS9nDaZzyr5/pmnJiVw4cRAXThzE7qpa8ksr2LS9gk0lFeSXVLCxpILlW3fw2rJt1Nb/95zciD5dGDe4O+MGdSdrWC/GD+6+zx5DeWUNVz0yn81llTxx3VQFhUiEwMLCzBKBGcDpQAGwwMxmu/vyBu3SgVuBjyLmjQUuBY4EBgJvmtkod68Lql5pXburavmfJ3N4N6+YH3xhLNedOPyg3qdLahKjD+vG6MO6fWZZbV09m8sqWVe0m6Wby1m8uZz560t4IXcLAIN6dOLsow7j3KMHMKp/Otf+dT5rCnfy8NXHMGW4LmcViRTknsUUIM/d1wGY2bPABcDyBu3uBe4Hbo+YdwHwrLtXAevNLC/8fh8EWK+0krKKaq756wKWbC7nl18ev89dzdGUlJjA0N5dGNq7C6eM7vfp/MKde3h3TTEvL97K4x9s4OF319MpOZGq2jr+cMUkPjdKT6ETaSjIsBgE5EdMFwBTIxuY2URgiLu/ZGa3N1j3wwbrDgqqUGk9az7ZyU1P5ZBfUskfrpjEmUce1uo19EtP44uTBvPFSYMpr6zhzeWf8MbyTzjn6AGcddSAVq9HpC0IMiwau3zk0wPIZpYAPABcc6DrRrzHdGA6QEZGxkEVKa3nnwsL+O4/ltIlNZEnrp/CsSN6x7okundK5uLJg7k4oL0bkfYiyLAoAIZETA8GtkRMpwNHAXPDJxkPA2ab2fktWBcAd38IeAhCN+VFs3iJnj01dfzoxeU8M38TU4b14sHLJ2osJJE2JsiwWABkmtlwYDOhE9aX713o7uVAn73TZjYXuN3ds82sEnjazH5N6AR3JjA/wFolSnZX1bK1vJLiXdVs31VNye4qnl2Qz7ItO7jpcyO5/YxRJCVq/EqRtiawsHD3WjO7GXiN0KWzj7r7MjO7B8h299n7WXeZmT1P6GR4LfANXQkV/97PK+bGJ7LZXb3vr6pn52QeuTqL08b0j1FlInKoNDaURMUHa7dz7WPzyejVmW+ccjh9uqbSu2sKvbuk0rNzsvYmROKUxoaSVvPRuu1c99gChvTszNM3HkufrqnNryQibYq+7skhmb++hGsfW8Cgnp0UFCLtmPYspFl7aup49L31PLcgn/S0JPqnp9GvWxq9uiTz1/c2MKB7Gk/fOPUzDwQSkfZDYdFGLMovY/Hmcq6cmtFqI6C6O28s/4Qfv7yCTSUVHD+yNylJCWwp30Nufhnbd1czqn9Xnrp+Kv3SdSmsSHumsGgDindVcf3jCyjeVU3Z7mpuOS0z8G3mFe7kRy8uZ96aYjL7deXJ66d85jkO1bX1JCVYYM+UEJH4obCIc+7OnX9fwo7KWk45oi+/emM1/bul8ZVjhjS/8kFaX7ybC2e8T4LBD88by5XHDiW5kauZUpJ0ykuko1BYxLnnFuTz5opP+P65Y7j6+GFc99gC7vrnEvqmp+4zOF607Kmp42tP5ZCUaLx0y4kM7tk56tsQkbZHXw3j2Ibi3dzz0nJOOLw3150wnOTEBP545WTGDEjn63/7mEX5ZVHf5g9eWMrKbTt54JIJCgoR+VSHD4vK6jr+58lsFhdE/4P3UNTW1XPbc7kkJRi//PL4T88LdE1N4tFrjqFPegrXPbaAOSs/YVdVbVS2+Xx2Ps9nF3DLqYdzyhHR32sRkbarw4fF9t1VLNuyg8se+pD31xbHupxPzXhrLbn5ZfzkonEM6N5pn2X90tN4/NopmBnXPZbN+B+9zgW/f5efvrKCt1cXcTB35S+WCtIkAAAORElEQVTfsoP/+9dSjh/Zm9s+Pypa3RCRdkLDfQDbyvfw1Uc+YmNJBQ9eNjEmz1iIlJtfxsV/fJ/zxw/kgUsmNNmuorqWnI2lfLSuhPnrS8jNL6O6rp5JGT340flHtfixoDv31HD+799jd1UtL996ku6XEOlAWjrch8IirHR3Ndc+toDFBWX8/OKj+XJWcFcb7U9ldR3n/m4ee2rq+Pdt0+jeKbnF6+6pqWN27hbuf20l23dXc0nWEG4/84j93lX9/tpivv+vpWzcXsEzNx6rx4mKdDAtDYsOfxhqr55dUvjbDVM5fmQf7pi1mIfnrYtJHff9ewXrinfzyy+PP6CgAEhLTuQrxwxhzu0nc/0Jw5mVU8Apv5zLr15fRc7GUurq//vFYPuuKr79fC6X/+Ujauucx67Vc6dFpGnas2igqraObz6Ty6vLtvHvb57EmAHdolBdy8xbU8RXH5nPdScM5wfnjT3k98sr3MVPXl7O3NVFuIeeCnfi4X3I7N+Vx97fwO6qWqZPG8Etp2aSlpwYhR6ISFujw1CHoLyihhN+PoeTj+jL7y+fFJX3bMk2z/zNO3RNS+KlW06M6od36e5q3s0r5p3VRbyzpohPdlRxzLCe/PSicWT2T4/adkSk7dEQ5Yege+dkrjpuKH98ey23Fe7i8H5dA9/m/72wlOJdVfzlqqyof8vv2SWF88YP5LzxA3F3PtlRRb/0VA3TISItpnMWTbj+xOGkJiXwh7l5gW/rxUVbmL1oC7eeltniK5gOlplxWPc0BYWIHBCFRRN6d03liqlDeSF3C5u2VwS2ndq6en76ygqOHtydr588MrDtiIgcCoXFfkyfNoLEBOOPbwe3d/HmikK2lu/h5lMO16NHRSRuBfrpZGZnmdkqM8szszsbWX6TmS0xs1wze9fMxobnDzOzyvD8XDP7U5B1NqV/tzQuyRrCrJwCtpRVBrKNpz7cyKAenThtTP9A3l9EJBoCCwszSwRmAGcDY4HL9oZBhKfdfZy7TwDuB34dsWytu08Iv24Kqs7m/M/nRuAOf3577QGvW1Fdy9m/ncfz2fmNLl9btIt384q5fGoGiTqHICJxLMg9iylAnruvc/dq4FnggsgG7r4jYrILEHfX8Q7u2ZkvThrEMwvyKdy554DWnZldwIqtO7j3peUU76r6zPKnPtxIcqLxlRjdLS4i0lJBhsUgIPIrdUF43j7M7BtmtpbQnsWtEYuGm9lCM3vbzE4KsM5mff3kw6mtq+eXr61q8SB9tXX1PPzuOkb27cKemjp+/u+V+yyvqK5lVk4BZx81QGMxiUjcCzIsGjuu8plPWnef4e4jgf8HfD88eyuQ4e4TgW8DT5vZZ26lNrPpZpZtZtlFRUVRLH1fw/p0Yfq0kTyfXcCDc1p2svvVZdvIL6nkjjNHc92Jw5mZU8DHm0o/XT47dws799Ty1eOGBlW2iEjUBBkWBUDk8ZXBwJb9tH8WuBDA3avcfXv45xxgLfCZcbPd/SF3z3L3rL59+zZcHFX/e+YRfHHSIH79xmqe/HDjftu6O395Zx3Denfm9LH9ufXUTPp3S+UHLyylrt5xd574YCOjD0sna2jPQOsWEYmGIMNiAZBpZsPNLAW4FJgd2cDMMiMmzwXWhOf3DZ8gx8xGAJlAbEb2C0tIMH5+8dGcNrofP3hhKS8tbjr3PlpfwqKCcm44KXTpbZfUJL537liWbt7BM/M3sTC/jOVbd3DlsUMx04ltEYl/gYWFu9cCNwOvASuA5919mZndY2bnh5vdbGbLzCyX0OGmq8PzpwGLzWwRMAu4yd1Lgqq1pZITE5hxxSSyhvbkW8/l8s7qxg99PfTOOnp1SeFLkwd/Ou+8owdw7Ihe/OK1VcyYk0fX1CQumviZUzgiInFJAwkehPLKGi758wdsKqngpxeN44IJAz/dQ1jzyU5Of+Adbvt85meeOLdq207O+d086uqdq48byo8uOKpV6hURaYqeZxGg7p2SeeK6KRxxWDq3PZfLjU9ks608dFntX+atIy05gauOG/aZ9Y44LJ1rjx+GGVx5rE5si0jboT2LQ1BX7/z1vfX88vVVJCcm8M3TMrn/1VVccswQ7r2w8b2G2rp61hXvZpSGBheROKA9i1aQmGDccNIIXv3mNMYO6MaPX15BTX091584vMl1khITFBQi0uboeRZRMKxPF5658Vhm5uRTXVvPsD5dYl2SiEhUKSyiJCHBuOSYjFiXISISCB2GEhGRZiksRESkWQoLERFplsJCRESapbAQEZFmKSxERKRZCgsREWmWwkJERJrVbsaGMrMiIPKpRN2B8kaaNpx/INN7f+4DFB9iyfur8UDaNbasJfMa61fDn6PRz2j0sanl0ehnvP8uG5vf0ulY/S6ba9sR/mZb+rtsOC8Wnz9D3b35p8e5e7t8AQ+1ZP6BTO/9GcgOssYDadfYspbMa6xfjfx8yP2MRh+D7Ge8/y6b69P+pmP1uzyYfra3v9mW/i7315emlrX23+zeV3s+DPViC+cfyHRT73mwWvp++2vX2LKWzGuqX/HYx6aWt7d+RvtvNlZ9bK6tfpdNz4vHzx+gHR2Gak1mlu0tGNK3resI/ewIfQT1sz2JVR/b855FkB6KdQGtpCP0syP0EdTP9iQmfdSehYiINEt7FiIi0qwOHxZm9qiZFZrZ0oNYd7KZLTGzPDP7nZlZxLJbzGyVmS0zs/ujW/WBC6KfZna3mW02s9zw65zoV35AdQbyuwwvv93M3Mz6RK/igxPQ7/JeM1sc/j2+bmYDo1/5AdcaRD9/YWYrw339p5n1iH7lB1RnEH38cvhzp97MonduIxqXYLXlFzANmAQsPYh15wPHAQb8Gzg7PP8U4E0gNTzdr532827g9lj3Lcg+hpcNAV4jdB9Pn/bYT6BbRJtbgT+1036eASSFf/458PN22McxwBHAXCArWrV2+D0Ld38HKImcZ2YjzexVM8sxs3lmNrrhemY2gNA/sA889Bt6ArgwvPhrwH3uXhXeRmGwvWheQP2MKwH28QHgf4G4OMEXRD/dfUdE0y7EQV8D6ufr7l4bbvohMDjYXuxfQH1c4e6rol1rhw+LJjwE3OLuk4HbgT800mYQUBAxXRCeBzAKOMnMPjKzt83smECrPXiH2k+Am8O79I+aWc/gSj1oh9RHMzsf2Ozui4Iu9BAd8u/SzH5iZvnAFcAPAqz1UETjb3av6wh9I4830exj1OgZ3A2YWVfgeGBmxGHr1MaaNjJv77exJKAncCxwDPC8mY0IfwOIC1Hq5x+Be8PT9wK/IvQPMC4cah/NrDPwPUKHLuJWlH6XuPv3gO+Z2V3AzcAPo1zqIYlWP8Pv9T2gFvhbNGs8VNHsY7QpLD4rAShz9wmRM80sEcgJT84m9EEZuQs7GNgS/rkA+Ec4HOabWT2h8VyKgiz8AB1yP939k4j1/gK8FGTBB+FQ+zgSGA4sCv/DHQx8bGZT3H1bwLUfiGj8zUZ6GniZOAsLotRPM7sa+AJwWjx9gQuL9u8yemJ5cideXsAwIk4wAe8DXw7/bMD4JtZbQGjvYe8JpnPC828C7gn/PArIJ3xPSzvr54CINt8Cnm1vfWzQZgNxcII7oN9lZkSbW4BZse5jQP08C1gO9I1134LqY8TyuUTxBHfM/0fF+gU8A2wFagjtEVxP6Nvkq8Ci8B/WD5pYNwtYCqwFfr83EIAU4Knwso+BU9tpP58ElgCLCX3bGdBa/WmtPjZoExdhEdDv8u/h+YsJjRk0qJ32M4/Ql7fc8CumV30F1MeLwu9VBXwCvBaNWnUHt4iINEtXQ4mISLMUFiIi0iyFhYiINEthISIizVJYiIhIsxQW0q6Z2a5W3t7DZjY2Su9VFx4FdqmZvdjcCKlm1sPMvh6NbYs0pEtnpV0zs13u3jWK75fk/x2ILlCRtZvZ48Bqd//JftoPA15y96Naoz7pWLRnIR2OmfU1s7+b2YLw64Tw/Clm9r6ZLQz/94jw/GvMbKaZvQi8bmYnm9lcM5sVfjbC3yKeJTB37zMEzGxXeHC+RWb2oZn1D88fGZ5eYGb3tHDv5wP+O7hhVzP7j5l9bKHnGVwQbnMfMDK8N/KLcNs7wttZbGY/iuL/RulgFBbSEf0WeMDdjwEuBh4Oz18JTHP3iYRGXf1pxDrHAVe7+6nh6YnAbcBYYARwQiPb6QJ86O7jgXeAGyO2/9vw9psdzyc8LtBphO6SB9gDXOTukwg9O+VX4bC6E1jr7hPc/Q4zOwPIBKYAE4DJZjatue2JNEYDCUpH9HlgbMSont3MLB3oDjxuZpmERvBMjljnDXePfO7AfHcvADCzXELj+7zbYDvV/HdwxRzg9PDPx/Hf52U8DfyyiTo7Rbx3DvBGeL4BPw1/8NcT2uPo38j6Z4RfC8PTXQmFxztNbE+kSQoL6YgSgOPcvTJyppk9CLzl7heFj//PjVi8u8F7VEX8XEfj/5Zq/L8nBZtqsz+V7j7BzLoTCp1vAL8j9LyJvsBkd68xsw1AWiPrG/Azd//zAW5X5DN0GEo6otcJPa8BADPbOxx0d2Bz+OdrAtz+h4QOfwFc2lxjdy8n9KjT280smVCdheGgOAUYGm66E0iPWPU14LrwMxIws0Fm1i9KfZAORmEh7V1nMyuIeH2b0AdvVvik73JCQ8oD3A/8zMzeAxIDrOk24NtmNh8YAJQ3t4K7LyQ0CumlhB7Yk2Vm2YT2MlaG22wH3gtfavsLd3+d0GGuD8xsCTCLfcNEpMV06axIKws/ga/S3d3MLgUuc/cLmltPJJZ0zkKk9U0Gfh++gqmMOHoUrUhTtGchIiLN0jkLERFplsJCRESapbAQEZFmKSxERKRZCgsREWmWwkJERJr1/wHp5iw0HSD71wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc(learn.loss_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results by image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 320\n",
    "bs = 8\n",
    "\n",
    "np.random.seed(42)\n",
    "data = ImageDataBunch.from_folder(path/'data2', ds_tfms=get_transforms(max_rotate=30.0, max_warp=0.), \n",
    "                                  size=size, bs=bs, \n",
    "                                  ).normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa = KappaScore()\n",
    "kappa.weights = \"quadratic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.densenet169, metrics=[error_rate, accuracy, kappa], wd=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-9-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "\n",
    "losses,idxs = interp.top_losses()\n",
    "\n",
    "len(data.valid_ds)==len(losses)==len(idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first interpretation\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1', '0', 357), ('0', '1', 160)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp.most_confused(min_val=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.plot_top_losses(9, figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy and kappa by study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source for kappa score calculation: [Cohen's kappa in plain English](https://stats.stackexchange.com/questions/82162/cohens-kappa-in-plain-english)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('densenet169-stage-9-n2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jupyter/tutorials/data/MURA-v1.1/data2/valid/1/HAND_patient11538_study1_positive_image1.png\n"
     ]
    }
   ],
   "source": [
    "for img_url in data.valid_ds.x.items:\n",
    "    print(img_url)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions on validation\n",
    "preds_val, y_val = learn.get_preds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file to save df\n",
    "file = path / 'results_densenet169_320.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          accuracy  kappa\n",
      "elbow        0.869  0.737\n",
      "finger       0.779  0.561\n",
      "forearm      0.870  0.741\n",
      "hand         0.807  0.583\n",
      "humerus      0.889  0.778\n",
      "shoulder     0.821  0.641\n",
      "wrist        0.859  0.711\n",
      "overall      0.838  0.674\n",
      "CPU times: user 552 ms, sys: 0 ns, total: 552 ms\n",
      "Wall time: 141 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "probs_of_images_by_studies = dict()\n",
    "num_img_by_studies = dict()\n",
    "labels_of_studies = dict()\n",
    "num_studies_by_label = dict()\n",
    "list_studies_by_label = dict()\n",
    "idx_by_studies = dict()\n",
    "results_by_label = dict()\n",
    "acc_by_label = OrderedDict()\n",
    "kappa_score = OrderedDict()\n",
    "\n",
    "pat_label = re.compile(r'/([^/]+)_patient[^/]+.png$')\n",
    "pat_study = re.compile(r'/([^/]+)_[^_]+.png$')\n",
    "\n",
    "for idx, src in enumerate(data.valid_ds.x.items):\n",
    "    # get label name\n",
    "    label = pat_label.search(str(src))\n",
    "    label = label.group(1) \n",
    "    # get study name\n",
    "    study = pat_study.search(str(src))\n",
    "    study = study.group(1) \n",
    "    # sum probabilities by study\n",
    "    if study in probs_of_images_by_studies:\n",
    "        probs_of_images_by_studies[study] += preds_val[idx,:].clone()\n",
    "        num_img_by_studies[study] += 1\n",
    "        idx_by_studies[study].append(idx)\n",
    "    else:\n",
    "        probs_of_images_by_studies[study] = preds_val[idx,:].clone()\n",
    "        num_img_by_studies[study] = 1\n",
    "        labels_of_studies[study] = label\n",
    "        idx_by_studies[study] = [idx]\n",
    "        \n",
    "for m in mura:\n",
    "    num_studies_by_label[m] = sum([1 for k,v in labels_of_studies.items() if v.lower() == m])\n",
    "    list_studies_by_label[m] = [k for k,v in labels_of_studies.items() if v.lower() == m]\n",
    "    \n",
    "for m in mura:\n",
    "    list_studies = list_studies_by_label[m]\n",
    "    TN, FP, TP, FN = 0, 0, 0, 0\n",
    "    for study in list_studies:\n",
    "        preds_val2 = preds_val.clone()\n",
    "        idxs = idx_by_studies[study]\n",
    "        preds = preds_val2[idxs]\n",
    "        y_hat = np.argmax(preds, axis=1)\n",
    "        y = y_val[idxs]\n",
    "        if 'negative' in study:\n",
    "            TN += sum(y_hat.numpy() == y.numpy())\n",
    "            FP += sum(y_hat.numpy() != y.numpy())\n",
    "        else:\n",
    "            TP += sum(y_hat.numpy() == y.numpy())\n",
    "            FN += sum(y_hat.numpy() != y.numpy())\n",
    "    results_by_label[m] = [TN, FP, TP, FN]\n",
    "    # accuracy\n",
    "    acc_by_label[m] = (TN + TP) / (TN+FP+FN+TP)\n",
    "    # kappa\n",
    "    ground_truth_neg = TN+FN\n",
    "    ground_truth_pos = FP+TP\n",
    "    classifier_neg = TN+FP\n",
    "    classifier_pos = FN+TP\n",
    "    total = classifier_neg + classifier_pos\n",
    "    observed_accuracy = (TN + TP) / total\n",
    "    expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "    kappa_score[m] = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "# overall accuracy and kappa\n",
    "[TN, FP, TP, FN] = [0,0,0,0]\n",
    "for m in mura:\n",
    "    results = results_by_label[m]\n",
    "    TN += results[0]\n",
    "    FP += results[1]\n",
    "    TP += results[2]\n",
    "    FN += results[3]\n",
    "# overall accuracy \n",
    "acc_by_label['overall'] = (TN + TP) / (TN+FP+FN+TP)\n",
    "# overall kappa\n",
    "ground_truth_neg = TN+FN\n",
    "ground_truth_pos = FP+TP\n",
    "classifier_neg = TN+FP\n",
    "classifier_pos = FN+TP\n",
    "total = classifier_neg + classifier_pos\n",
    "observed_accuracy = (TN + TP) / total\n",
    "expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "kappa_score['overall'] = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "# Create DataFrame\n",
    "for k, v in acc_by_label.items():\n",
    "    acc_by_label[k] = round(v, 3)\n",
    "for k, v in kappa_score.items():\n",
    "    kappa_score[k] = round(v, 3)\n",
    "    \n",
    "df = pd.DataFrame([acc_by_label, kappa_score], index =['accuracy', 'kappa']) \n",
    "df.name = 'Results on validation at a study level'\n",
    "df = df.transpose()\n",
    "\n",
    "# save df\n",
    "df.to_csv(file,header=True)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check overall accuracy and kappa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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btmhYm55/nMCc7LX87epO1K+9L1WqGFO/WMlt//yYbZ6WQyp3YUFjZs8APYFl7t46aj8VUdWsLJ57ZRQ1atRky5Yt9Ot9Oief1hWAm267l+69zinU/6qfXs9VP70egH+NGcXfBj+skKlAXpmykOcmfMUf+7fLb/ts8Tp+/Mx07r3g6EJ9X5+RzeszsgFo3rAWT17ZkTnZawEYMGQm6zblAvDYZe3p0a4hI2cuLqejSK/IqdPfgO6B41dYZkaNGjUByN2yhdzcLZhZqbYd+dpLnHXOBZHlyU6a8sVKVq/fUqht/rJ1fLF8fYnb9TqmESOS0AHyQyYzw6iamYHvJWczEBg07v4esDJq/Ipu69at9D7tOL7f+lBOOKkLbdt3AuDPg+6k16md+d1tN7F506ZC22z49lvGj32bbmednY6SpYz1PKYhw2csKtQ25JrOTLv7DNZtzOX/Ptw7zmagAlwMNrOrzWyamU1buTIn3eWUmSpVqjD8ncm8N3Mus2ZOZ+6c2fzy1jsZPWEmr4wez+pVq3jykT8V2uZfY0bRvtNxmjZVAu0OrsOGzVuZu2RdofZLB0+h8+1vUzUzg+Ob1UtTdeUv7UHj7k+6e0d371i3buX7wdferw6djz+R8WPfov5BDTEzqmZlcV7fi5k1c1qhvm+8/k96nnN+miqVstSzfUNGzMwuct3m3G28PXspZ7Q+qJyrSp+0B01ltDJnOWvXrAZg44YNvD9+LIc3bc6ypalTZXfn7dEjaNaiZf4236xdw9RJEzitW8+01Cxlxwx6tC0cNNWrVuHA2lkAVMkwTj2qPvOXrStuiEpHt7cDLFu2hJt/fjXbtm5l27ZtnNn7PE7teiaXnHcmK1fk4O4c1boNd97/l/xt3ho1nBNOPo3qNWqksXIpykMXt+O4pgewf42qvH97Fx4cPY/V327mjnNbUbdmVZ75USc+WbSWSwdPAaDz4XVZsnojX6/YkD9G9apVeOrKjmRlZpCRYUyat4K/v/+fdB1SuTMPuvRtZi8CpwD1gKXA7e7+15K2Obpte391zISQeiS9ug4am+4SJED2P65j09J5O7ylGnZG4+79osYWkT2LrtGISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDgFjYiEU9CISDhz93TXkM/MlgML0l1HOakH5KS7CClze9vneoi7H7ijThUqaPYmZjbN3Tumuw4pW/pci6apk4iEU9CISDgFTfo8me4CJIQ+1yLoGo2IhNMZjYiEU9CISDgFjYiEy0x3AXsDM2sBnA00BhzIBoa7+5y0FiZSTnRGE8zMbgaGAgZMAaYm7180s4HprE2kvOiuUzAzmwu0cvct27VXBWa7e7P0VCaRzOxyd3823XVUFDqjibcNaFREe8NknVROd6a7gIpE12jiXQe8Y2bzgK+TtoOBpsCAtFUlu83MZhW3CjioPGup6DR1KgdmlgF0JnUx2ICFwFR335rWwmS3mNlSoBuwavtVwPvuXtSZ7F5JZzTlwN23AZPTXYeUuZFATXf/YPsVZjau/MupuHRGIyLhdDFYRMIpaEQknIJGADCzrWb2gZl9bGYvm1n13RjrFDMbmbzvXdKDiWZWx8x+sgv7uMPMbtzVGqV8KWgkzwZ3b+furYHNwLUFV1rKTv95cffh7j6ohC51gJ0OGtmzKGikKOOBpmZ2qJnNMbPHgBnA98ysq5lNMrMZyZlPTQAz625mn5rZBODcvIHM7DIzeyR5f5CZvWZmHyav44FBwBHJ2dQDSb9fmdlUM5tlZncWGOtWM/vMzN4GmpfbT0N2m4JGCjGzTOBM4KOkqTnwnLsfA6wHfgOc7u7tgWnADWa2L/AU0As4EWhQzPB/Ad5197ZAe2A2MBCYn5xN/crMugLNSD131A7oYGYnmVkHoC9wDKkg61TGhy6B9ByN5KlmZnnPg4wH/krqVycWuHveM0DHAS2BiWYGUBWYBLQAvnT3eQBm9gJwdRH76AJcApA8rLjGzPbfrk/X5DUzWa5JKnhqAa+5+7fJPobv1tFKuVLQSJ4N7t6uYEMSJusLNgFvuXu/7fq1I/X1F2XBgPvcffB2+7iuDPch5UxTJ9kZk4ETzKwpgJlVN7MjgU+Bw8zsiKRfv2K2fwf4cbJtFTOrDXxD6mwlz5vAFQWu/TQ2s/rAe8A5ZlbNzGqRmqbJHkJBI6Xm7suBy0h9l84sUsHTwt03kpoqvZFcDC7u/zb6C+BUM/sImE7q6zNWkJqKfWxmD7j7GOAfwKSk3z+BWu4+AxgGfAC8Qmp6J3sI/QqCiITTGY2IhFPQiEg4BY2IhFPQiEg4BY2IhFPQiEg4BY2IhPt/RSB9HcQSVqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first interpretation\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = interp.confusion_matrix()\n",
    "\n",
    "TN = cm[0,0]\n",
    "FP = cm[0,1]\n",
    "FN = cm[1,0]\n",
    "TP = cm[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.838, 0.674)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy\n",
    "acc = (TN + TP) / (TN+FP+FN+TP)\n",
    "\n",
    "# kappa\n",
    "ground_truth_neg = TN+FN\n",
    "ground_truth_pos = FP+TP\n",
    "classifier_neg = TN+FP\n",
    "classifier_pos = FN+TP\n",
    "total = classifier_neg + classifier_pos\n",
    "observed_accuracy = (TN + TP) / total\n",
    "expected_accuracy = ((ground_truth_neg * classifier_neg / total) + (ground_truth_pos * classifier_pos / total)) / total\n",
    "kappa_score = (observed_accuracy - expected_accuracy) / (1 - expected_accuracy)\n",
    "\n",
    "round(acc,3), round(kappa_score,3)"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}