stroke_lesions_starting_kit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<table>\n",
    "  <tr>\n",
    "    <td style=\"background-color:#FFFFFF;\">\n",
    "        <a href=\"https://chan.usc.edu/npnl/\">\n",
    "            <img src=\"https://github.com/ramp-kits/stroke_lesions/raw/master/figs/usc-identity-desktop.png\" width=\"500px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "    <td style=\"background-color:#FFFFFF;\">\n",
    "        <a href=\"https://dataia.eu\">\n",
    "            <img src=\"https://github.com/ramp-kits/stroke_lesions/raw/master/figs/DATAIA-h.png\" width=\"500px\"/>\n",
    "        </a>\n",
    "    </td>\n",
    "  </tr>\n",
    "</table>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<div style=\"text-align: center\">\n",
    "<img src=\"https://raw.githubusercontent.com/ramp-kits/stroke/master/figs/stroke_logo.png\" width=\"400px\" />\n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> RAMP: segmentation of the brain lesions </center>\n",
    "\n",
    "<b>Authors</b>: Alexandre Hutton $^{1}$, Maria Teleńczuk $^{2}$, Swetha Shanker $^{2}$, Guillaume Lemaitre $^{2}$, François Caud $^{2}$, Alexandre Gramfort $^{2}$, Sook-Lei Liew $^{1}$\n",
    "\n",
    "$^{1}$: <a href=\"https://chan.usc.edu/npnl/\">Neural Plasticity & Neurorehabilitation Lab</a> (Univ. of Southern California), USA  \n",
    "$^{2}$: <a href=\"http://www.datascience-paris-saclay.fr\">Paris-Saclay Center for Data Science</a> (Dataia - Univ. Paris Saclay - Inria), France"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "1. [Introduction](#Introduction)\n",
    "    1. [Motivation](#Motivation)\n",
    "    2. [Setup](#Setup)\n",
    "2. [Data Exploration](#data_exp)\n",
    "    1. [Visualize Data](#visualize)\n",
    "    1. [Paired Loading](#paired)\n",
    "    2. [Lesion Analysis](#lesion)\n",
    "3. [Creating Estimators](#prediction)\n",
    "4. [Scores](#scores)\n",
    "3. [Submission](#Submission)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction <a class=\"anchor\" id=\"Introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clinical/research motivation <a name=Motivation></a>\n",
    "\n",
    "Stroke is the leading cause of adult disability worldwide, with up to two-thirds of affected individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g. measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but require anatomical expertise and are labor-intensive. Further, manual segmentation is subjective with raters producing different results. While algorithms have been developed to automate this process, the resulting lesion masks often lack the accuracy needed to make them reliable information. Newer algorithms that employ machine-learning and deep learning techniques are promising avenues, but they require large, diverse datasets for training and testing and developing generalizable models. In this challenge, training can be performed on our public ATLAS 2.0 dataset, and testing is done with a private dataset comprised of multi-site data from the same sites as ATLAS 2.0. <BR>\n",
    "\n",
    "For more information refer to:\n",
    "<a href=\"http://fcon_1000.projects.nitrc.org/indi/retro/atlas.html\">Anatomical Tracings of Lesions After Stroke</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.sciencedirect.com/science/article/abs/pii/S1361841516301268\">1. Maier, Oskar, et al. \"ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.\" Medical image analysis 35 (2017): 250-269.</a><br>\n",
    "\n",
    "<a href=\"https://www.nature.com/articles/sdata201811\">2. Liew, Sook-Lei, et al. \"A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.\" Scientific data 5 (2018): 180011.</a><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective of the challenge\n",
    "\n",
    "In this challenge you will be given 3D medical images (T1 MRI scans in `nii.gz` format) of stroke patients and the files with the corresponding lesions (binary masks) traced manually by experts. Your algorithm will be scored on the overlap between your prediction and the segmentation mask."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup <a name=Setup></a>\n",
    "### Prerequisites\n",
    "The following cell will install the required package dependencies, if necessary. You can examine the file, `requirements.txt`, included in the repo to view the list of dependencies.\n",
    "NOTE: Due to the structure of the challenge, libraries not included in `requirements.txt` will need to be added via a pull request to the [GitHub repo](https://github.com/ramp-kits/stroke_lesions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get this notebook running and test your models locally using `ramp-test` (from ramp-workflow), we recommend that you use the Anaconda or Miniconda Python distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data exploration <a name=data_exp></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data download and reformat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need four things to be able to get a local copy of the data:\n",
    "1. Downloaded archive.\n",
    "2. Encryption password.\n",
    "3. Decryption + tar extraction.\n",
    "4. Data reformatting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Archive download\n",
    "\n",
    "You can get a local (encrypted) copy of the data by going to the [NITRC website](http://fcon_1000.projects.nitrc.org/indi/retro/atlas_download.html) or running the following cell. The relevant code is commented to prevent accidentally downloading the (large) data. Make sure to uncomment the last line.\n",
    "\n",
    "Note that the data is approx. 15GB; ensure that you have enough space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import download_data\n",
    "\n",
    "# This will take some time; you can skip data validation by setting check_hash=False, but that is not recommended.\n",
    "### Uncomment this line to download the data:\n",
    "# download_data.data_fetch(check_hash=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Encryption password\n",
    "\n",
    "The data is encrypted; to obtain the password to decrypt the data, you'll need to agree to the terms of use [found on this form](https://docs.google.com/forms/d/e/1FAIpQLSclH8padHr9zwdQVx9YY_yeM_4OqD1OQFvYcYpAQKaqC6Vscg/viewform).\n",
    "Please be careful not to close the tab before acquiring the decryption key."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. OpenSSL + tar extraction\n",
    "\n",
    "If you are running either __Linux or MacOS__, you can run the following cell. You will be prompted for the password obtained in step 2; you can copy+paste it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*** WARNING : deprecated key derivation used.\n",
      "Using -iter or -pbkdf2 would be better.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import getpass, subprocess\n",
    "## Decrypt the data.\n",
    "subprocess.call(['openssl', 'aes-256-cbc', '-md', 'sha256', \n",
    "                 '-d', '-a', '-in',\n",
    "                 'ATLAS_R2.0_encrypted.tar.gz', '-out', 'ATLAS_R2.0.tar.gz',\n",
    "                 '-pass', f'pass:{getpass.getpass(\"Enter password\")}'])\n",
    "subprocess.call(['tar', '-xzf', 'ATLAS_R2.0.tar.gz'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For __Windows__, you'll need to install OpenSSL, open the terminal, then run the following from that window:  \n",
    "`openssl aes-256-cbc -md sha256 -d -a -in ATLAS_R2.0_encrypted.tar.gz -out ATLAS_R2.0.tar.gz`  \n",
    "Next, extract the .tar.gz archive. You should be left with a directory called ATLAS_2 once it is extracted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Data reformatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the data provided via INDI is not compatible with PyBIDS, which is used throughout the challenge. You can run the following cell to correct the formatting; update the values as appropriate.\n",
    "WARNING: This will move the data files on your disk. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stroke import indi_reformat\n",
    "## Uncomment the following lines to correct the formatting.\n",
    "atlas_2_path = 'ATLAS_R2.0/ATLAS_2'  # Assumes that it is in the current directory. Set this to the location on your drive.\n",
    "destination = 'data'  # Assumes that we're in the stroke/ directory.\n",
    "indi_reformat.bidsify_indi_atlas(atlas_path=atlas_2_path, destination_path=destination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the data files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In your `data` folder you should now be able to find the `test` and `train` directories, each containing BIDS directories. Subject directories contain two files:\n",
    " - `sub-X_ses-Y_space-MNI152NLin2009aSym_T1w.nii.gz`\n",
    "     - Structural T1 that has been defaced, resampled, and normalized to MNI152NLin2009\n",
    " - `sub-X_ses-Y_space-MNI152NLin2009aSym_label-L_desc-T1lesion_mask.nii.gz`\n",
    "     - Lesion mask. '1' indicates the presence of a lesion, '0' otherwise.\n",
    "\n",
    "If you wish to view any of those files from outside of Python, you can use applications like [ITK-snap](http://www.itksnap.org/pmwiki/pmwiki.php). There, you can load the structural file as the main image and the lesion mask as a segmentation to overlap the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the images to Python, we will be using [PyBIDS](https://github.com/bids-standard/pybids). See this [link](https://bids.neuroimaging.io/) to read details about BIDS.\n",
    "If you want to use a libarary which is not listed in `requirements.txt` or `extra_libraries.txt`, please make a pull request to the the [Stroke Lesion Segmentation Challenge repository on GitHub](https://github.com/ramp-kits/stroke_lesions) by adding the required library to [extra_libraries.txt](https://github.com/ramp-kits/stroke_lesions/blob/master/extra_libraries.txt) file)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize data <a name=visualize></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data provided with this challenge are 3D images. In this section, we'll show you how to view slices of the data.\n",
    "Since the data is in [BIDS](https://bids-specification.readthedocs.io/en/stable/), we can access the data fairly easily using [PyBIDS](https://github.com/bids-standard/pybids)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIDS Layout: ...s/data/train/derivatives/ATLAS | Subjects: 436 | Sessions: 436 | Runs: 0\n"
     ]
    }
   ],
   "source": [
    "import bids\n",
    "from bids import BIDSLayout\n",
    "bids.config.set_option('extension_initial_dot', True)  # Warning suppression\n",
    "data = BIDSLayout('data/train', derivatives=True).derivatives['ATLAS']\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are 436 subjects in the training set with a total of 436 sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub-r031s022_ses-1_space-MNI152NLin2009aSym_T1w.nii.gz\n",
      "sub-r031s022_ses-1_space-MNI152NLin2009aSym_label-L_desc-T1lesion_mask.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# Pick random sample\n",
    "import random\n",
    "subjects = data.entities['subject'].unique()\n",
    "sample = random.choice(subjects)\n",
    "\n",
    "structural = data.get(subject=sample, suffix='T1w')[0]\n",
    "mask = data.get(subject=sample, suffix='mask')[0]\n",
    "print(structural.filename)\n",
    "print(mask.filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n",
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n",
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from stroke.nii_slice import qc_slice\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [14, 6]\n",
    "qc_slice(structural.path, mask_path=mask.path, nslices=5)\n",
    "\n",
    "# Beautify plot\n",
    "plt.text(0,0, f'{sample}', bbox={'facecolor': 'white'});  # Label which subject\n",
    "plt.tick_params(axis='both', labelbottom=False, labelleft=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above image, we see slices for each plane. The masked stroke is denoted in red, although the slices may not intersect the lesion mask for every subject (re-run the last two cells to display a new subject). Some preprocessing has already been done on the images; you can review the preprocessing steps [here](https://www.nature.com/articles/sdata201811).\n",
    "\n",
    "Due to the unreliable performance of non-linear registration with subjects with pathologies, the data was registered linearly. If you examine multiple subjects, you may notice that although the image dimensions are the same and the brains are roughly in the right area, there are differences between subjects (e.g., dimensions of the head, location of specific structures). This is typical of neuroimaging datasets.\n",
    "\n",
    "Before we look closer at the lesions, let's look at some of the image properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of structural scan: (197, 233, 189)\n",
      "<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'\n",
      "sizeof_hdr      : 348\n",
      "data_type       : b''\n",
      "db_name         : b''\n",
      "extents         : 0\n",
      "session_error   : 0\n",
      "regular         : b''\n",
      "dim_info        : 0\n",
      "dim             : [  3 197 233 189   1   1   1   1]\n",
      "intent_p1       : 0.0\n",
      "intent_p2       : 0.0\n",
      "intent_p3       : 0.0\n",
      "intent_code     : none\n",
      "datatype        : float32\n",
      "bitpix          : 32\n",
      "slice_start     : 0\n",
      "pixdim          : [1. 1. 1. 1. 0. 1. 1. 1.]\n",
      "vox_offset      : 0.0\n",
      "scl_slope       : nan\n",
      "scl_inter       : nan\n",
      "slice_end       : 0\n",
      "slice_code      : unknown\n",
      "xyzt_units      : 10\n",
      "cal_max         : 0.0\n",
      "cal_min         : 0.0\n",
      "slice_duration  : 0.0\n",
      "toffset         : 0.0\n",
      "glmax           : 0\n",
      "glmin           : 0\n",
      "descrip         : b'FreeSurfer Jan 18 2017'\n",
      "aux_file        : b''\n",
      "qform_code      : scanner\n",
      "sform_code      : scanner\n",
      "quatern_b       : 0.0\n",
      "quatern_c       : 0.0\n",
      "quatern_d       : 0.0\n",
      "qoffset_x       : -98.0\n",
      "qoffset_y       : -134.0\n",
      "qoffset_z       : -72.0\n",
      "srow_x          : [  1.   0.   0. -98.]\n",
      "srow_y          : [   0.    1.    0. -134.]\n",
      "srow_z          : [  0.   0.   1. -72.]\n",
      "intent_name     : b''\n",
      "magic           : b'n+1'\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape of structural scan: {structural.get_image().shape}')\n",
    "print(structural.get_image().header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cell displays some of the metadata about the file. You can [read here](https://brainder.org/2012/09/23/the-nifti-file-format/) for more information, but the shape of the data is most relevant.\n",
    "Every image contains 197x233x189 (8.6M) voxels. Consequently, it is not usually possible to load the entire dataset into memory at once. We will see the effects of this in later sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paired Loading <a name=paired></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading individual files is fine for examining the data, but we need to ensure that our data and masks are paired, even if new sessions or modalities are added later. We've included a loader for this in `bids_loader.py`, as `BIDSLoader`. We can look at its docstrings to see the expected input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class BIDSLoader in module stroke.bids_loader:\n",
      "\n",
      "class BIDSLoader(builtins.object)\n",
      " |  BIDSLoader(root_dir: str, data_entities: list, target_entities: list, batch_size: int = 1, data_derivatives_names: list = None, target_derivatives_names: list = None, root_list: list = None, label_names: list = None)\n",
      " |  \n",
      " |  BIDS-compatible data loader used for classifying BIDS datasets.\n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  root_dir : str\n",
      " |      BIDS root directory; subject directories should be immediately below this (e.g. root_dir/sub-123)\n",
      " |  data_entities : list [dict]\n",
      " |      List of dictionaries, where each dictionary contains BIDS entities that will uniquely match data. Multiple\n",
      " |      dictionaries should be used to if multiple files will be used for prediction.\n",
      " |      Empty entries ({'subject': ''}) indicate that that entry should match across samples (e.g., [{'session': ''}]\n",
      " |      would ensure that entities from a returned sample are from the same session, but that any value is valid.\n",
      " |      For example: ({'subject': '', 'session': '1', 'desc': 'Normalized'}, {'session': '1', 'desc': 'defaced'}) would\n",
      " |      return samples of two images: The first could be 'sub-123_ses-1_desc-Normalized.nii.gz', and the second would be\n",
      " |      'sub-123_ses-1_desc-defaced.nii.gz'. The subject entity matches, session is restricted to \"1\", and the\n",
      " |      description entitiy is used to differentiate between them.\n",
      " |  target_entities : list [dict]\n",
      " |      Same as data_entities, but for the prediction target.\n",
      " |  batch_size : int\n",
      " |      Optional. Size of the batch to train the estimator. Default: 1.\n",
      " |  data_derivatives_names : list [str]\n",
      " |      Optional. If an entry in data_entities is BIDS derivatives data, its name should be listed here. Entries\n",
      " |      that don't correspond to derivatives should be listed as None. Default: [None for _ in data_entities]\n",
      " |  target_derivatives_names : list [str]\n",
      " |      Optional. If an entry in target_entities is BIDS derivatives data, its name should be listed here. Entries\n",
      " |      that don't correspond to derivatives should be listed as None. Default: [None for _ in target_entities]\n",
      " |  root_list : list\n",
      " |      Reserved. Not yet implemented. List of BIDS root directories, if data must be loaded from different BIDS\n",
      " |      directories. There must be exactly len(data_entities) + len(target_entities) entries in the list, with the\n",
      " |      order corresponding to the order of the data_entities, followed by the target_entities.\n",
      " |  label_names : list [str]\n",
      " |      Names of the values of the target, if any.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, root_dir: str, data_entities: list, target_entities: list, batch_size: int = 1, data_derivatives_names: list = None, target_derivatives_names: list = None, root_list: list = None, label_names: list = None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |  \n",
      " |  load_batch(self, indices: list)\n",
      " |      Loads a batch of N images and returns the data/target in arrays.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      indices : list [int]\n",
      " |          List of indices to load.\n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.array\n",
      " |          Array of shape (len(indices), num_data, *image.shape) containing data.\n",
      " |      np.array\n",
      " |          Array of shape (len(indices), num_target, *image.shape) containing data.\n",
      " |  \n",
      " |  load_sample(self, idx: int)\n",
      " |      Loads the sample at idx.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      idx : int\n",
      " |          Index of the sample to load. Max valid value is len(BIDSClassifier)-1\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.array\n",
      " |          Array of shape (num_data, *image.shape) containing the data.\n",
      " |      np.array\n",
      " |          Array of shape (num_target, *image.shape) containing the target.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  get_matching_images(image_to_match: bids.layout.models.BIDSImageFile, bids_dataset: bids.layout.layout.BIDSLayout, matching_entities: list = None, required_entities: dict = None)\n",
      " |      Returns a list of images from the BIDS dataset that has the specified required_entities and has the same\n",
      " |      value for entities listed in matching_entities as the image_to_match.\n",
      " |      Example: for an image \"sub-123_ses-1_T1w.nii\" with matching_entities ['ses'] and required_entities\n",
      " |      {'suffix': 'FLAIR'}, the image \"sub-123_ses-1_FLAIR.nii\" would match, but \"sub-123_ses-2_FLAIR.nii\" would not.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      required_entities: dict\n",
      " |          Entity-value dictionary that are required.\n",
      " |      matching_entities: list\n",
      " |          List of entities that must match, if present, between the previous image and the one to fetch.\n",
      " |      image_to_match: BIDSImageFile\n",
      " |          Image to use as reference for matching_entities.\n",
      " |      bids_dataset: BIDSLayout\n",
      " |          BIDS dataset from which to fetch the new image.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      list [BIDSImageFile]\n",
      " |          BIDS image file matching the input specifications. Empty if there are no matches.\n",
      " |  \n",
      " |  load_image_tuple(image_tuple: tuple, dtype=<class 'numpy.float32'>)\n",
      " |      Loads the tuple and returns it in an array\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      image_tuple : tuple (BIDSImageFile,)\n",
      " |          Tuple of BIDSImageFile to be loaded and returned in an array\n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.array\n",
      " |          Loaded data\n",
      " |  \n",
      " |  load_image_tuple_list(image_list: list, dtype=<class 'numpy.float32'>)\n",
      " |      Loads each image in the tuple and returns in a single array; different tuples in the list are assumed to be\n",
      " |      batches. The returned array will be of shape (len(image_list), len(image_tuple), *image.shape\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      image_list : list [tuple]\n",
      " |          List of tuples containing BIDSImageFile\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.array\n",
      " |          Loaded data.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from stroke.bids_loader import BIDSLoader\n",
    "help(BIDSLoader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = BIDSLoader(root_dir='data/train/',\n",
    "                          data_entities={'subject': '',\n",
    "                                         'session': '',\n",
    "                                         'suffix': 'T1w'},\n",
    "                          target_entities={'label': 'L',\n",
    "                                          'desc': 'T1lesion',\n",
    "                                          'suffix': 'mask'},\n",
    "                          data_derivatives_names=['ATLAS'],\n",
    "                          target_derivatives_names=['ATLAS'],\n",
    "                          label_names=['not lesion', 'lesion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object, `training_set` now has the data list paired with the target list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: (<BIDSImageFile filename='/home/frcaud/ramp-board/ramp_deployment/ramp-kits/stroke_lesions/data/train/derivatives/ATLAS/sub-r001s002/ses-1/anat/sub-r001s002_ses-1_space-MNI152NLin2009aSym_T1w.nii.gz'>,)\n",
      "Mask: (<BIDSImageFile filename='/home/frcaud/ramp-board/ramp_deployment/ramp-kits/stroke_lesions/data/train/derivatives/ATLAS/sub-r001s002/ses-1/anat/sub-r001s002_ses-1_space-MNI152NLin2009aSym_label-L_desc-T1lesion_mask.nii.gz'>,)\n"
     ]
    }
   ],
   "source": [
    "print(f'Image: {training_set.data_list[0]}')\n",
    "print(f'Mask: {training_set.target_list[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the data/target into separate arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (1, 197, 233, 189)\n",
      "target shape: (1, 197, 233, 189)\n"
     ]
    }
   ],
   "source": [
    "data, target = training_set.load_sample(0)\n",
    "print(f'data shape: {data.shape}')\n",
    "print(f'target shape: {target.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also load multiple images as a batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (3, 1, 197, 233, 189)\n",
      "target shape: (3, 1, 197, 233, 189)\n"
     ]
    }
   ],
   "source": [
    "data, target = training_set.load_batch([0,1,2])\n",
    "print(f'data shape: {data.shape}')\n",
    "print(f'target shape: {target.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Along with `load_image_tuple` and `load_image_tuple_list`, these methods are used during RAMP's training to feed your estimator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesion analysis <a name=lesion></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, that we understand better the anatomical T1 images that we will work with, let's look at the lesion masks. We will visualize overlap of the two:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n"
     ]
    }
   ],
   "source": [
    "from nilearn.image import load_img\n",
    "from nilearn import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/numpy/ma/core.py:2831: UserWarning: Warning: converting a masked element to nan.\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x250 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_overlap(path_to_t1, path_to_lesion, title='overlap'):\n",
    "    fig = plt.figure(1, figsize=(14, 5), frameon=False, dpi=50)\n",
    "    ax = plt.gca()\n",
    "    plotting.plot_roi(path_to_lesion, path_to_t1, title=title,\n",
    "                       axes=ax,\n",
    "                       draw_cross=False,\n",
    "                       annotate=False, \n",
    "                       cmap='autumn',  # the lesions will be shown in red\n",
    "                       cut_coords=(0, 0, 0)\n",
    "                       )\n",
    "plot_overlap(path_to_t1=structural.path,\n",
    "             path_to_lesion=mask.path,\n",
    "             title=f'overlap, {sample}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that lesions are heterogeneous: they appear in different places, have different sizes, and are different shapes.\n",
    "\n",
    "We can examine a number of subjects and get the overall lesions for each subject. Note that some subjects have multiple lesions, so the following value is the _lesion load_ rather than the lesion size. Even using the aggregate measure, we'll see a wide distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stroke.stroke_config import bids_loader_train, bids_loader_test  # Default loaders for starting kit\n",
    "import numpy as np\n",
    "\n",
    "train_fraction = np.floor(len(bids_loader_train.target_list)/10)\n",
    "test_fraction = np.floor(len(bids_loader_train.target_list))\n",
    "subset_size = np.min([50, train_fraction, test_fraction])  # number of subjects to examine; reduce if it takes too long to load\n",
    "subset_size = int(subset_size)\n",
    "lesion_load_train = np.zeros((subset_size,))  # lesion load per subject\n",
    "lesion_load_test = np.zeros((subset_size,))\n",
    "\n",
    "# Load the data, compute the lesion load, store\n",
    "for i in range(subset_size):\n",
    "    target_tuple = bids_loader_train.target_list[i]\n",
    "    target = bids_loader_train.load_image_tuple(target_tuple)\n",
    "    lesion_load_train[i] = np.sum(target)\n",
    "for i in range(subset_size):\n",
    "    target_tuple = bids_loader_test.target_list[i]\n",
    "    target = bids_loader_test.load_image_tuple(target_tuple)\n",
    "    lesion_load_test[i] = np.sum(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3e29c498b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution!\n",
    "from matplotlib import pyplot as plt\n",
    "plt.hist(np.log10(lesion_load_train), bins=10)  # Note the log scale\n",
    "plt.hist(np.log10(lesion_load_test), bins=10, alpha=0.5)\n",
    "plt.xlabel('log10 Lesion Load')\n",
    "plt.ylabel('Frequency of lesion load')\n",
    "plt.title('Distribution of lesion load in sets')\n",
    "plt.legend(['Training set', 'Testing set'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the distribution that even among the 100 subjects, the lesion load varies quite a bit across patients. The test set is a random subset from the same sites as the training set, but we can (qualitatively) observe different distribution between the two sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample prediction algorithms <a name=prediction></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will show you the expected format for your estimators. We'll first go through a sample estimator that defines the required properties and methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy solution (predict only 1s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The starter kit has a sample estimator in `submissions/sample/`. There, you'll find a file, `estimator.py`, which RAMP expects to have BIDSEstimator defined. The class is intentionally barebones, and is a good starting place for your own estimators.\n",
    "Let's take a look at the sample class and its methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fit', 'fit_partial', 'get_params', 'predict', 'predict_proba', 'set_params']\n"
     ]
    }
   ],
   "source": [
    "from submissions.sample.estimator import BIDSEstimator  # Load estimator\n",
    "methods = [d for d in dir(BIDSEstimator) if not d.startswith('_')]  # Get methods; remove private ones.\n",
    "print(methods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two of these methods, `get_params` and `set_params` are from `sklearn` as `BIDSEstimator` is a custom estimator built from `BaseEstimator`. You don't need to implement these.\n",
    "  \n",
    "The fitting methods, `fit` and `fit_partial` are related to fitting your estimator to the data.  \n",
    "`fit` expects to be given the entirety of the data to fit. You won't be able to fit all the data in memory, and as such `fit` is not required.  \n",
    "`fit_partial` behaves similarly, but instead assumes that not all data is being presented at once. It is used in iterative fitting (e.g. stochastic gradient descent). This method is required.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function fit_partial in module submissions.sample.estimator:\n",
      "\n",
      "fit_partial(self, X, y)\n",
      "    Fit the estimator using the input data (X) and target (y). Assumes that the inputs represent only a fraction\n",
      "    of the data and that it will be called multiple times while using the dataset. I.e., learning rates and adaptive\n",
      "    parameters should not be entirely recalculated with each call to this method. Required.\n",
      "    This estimator in particular does nothing.\n",
      "    Parameters\n",
      "    ----------\n",
      "    X : np.array\n",
      "        Data of the form (n_samples, n_channels, *image.shape)\n",
      "    y : np.array\n",
      "        Target (labels) of the form (n_samples, n_channels, *image.shape)\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(BIDSEstimator.fit_partial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, the prediction methods, `predict` and `predict_proba` use input data and return a prediction.  \n",
    "Broadly, `predict` is expected to return the predicted class (i.e. argmax instead of softmax). In the case of our binary masks, we would expect predictions of either '0' or '1'.\n",
    "`predict_proba` is expected to return continuous values (i.e. softmax instead of argmax), which can be useful for model evaluation and calibration.\n",
    "\n",
    "Scoring functions will call the relevant prediction method for their requirements, but will cast the predictions into the right type (e.g. binary) before scoring. You can implement only `predict` and then have `predict_proba` simply return the results of `predict` without issue. Having your own implementation allows you to customize parameters such as thresholds, but is not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function predict_proba in module submissions.sample.estimator:\n",
      "\n",
      "predict_proba(self, X)\n",
      "    Applies the data to the estimator to produce a prediction. The output can be continuous to represent the\n",
      "    relative confidence the estimator has in the prediction. Optional.\n",
      "    Typically, correct but uncertain predictions are rewarded less. Similarly, incorrect but uncertain predictions\n",
      "    are punished less severely.\n",
      "    This estimator always returns 1.\n",
      "    Parameters\n",
      "    ----------\n",
      "    X : np.array\n",
      "        Data of the form (n_samples, n_channels, *image.shape)\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    np.array\n",
      "        Prediction made by the estimator.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(BIDSEstimator.predict_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submitting to RAMP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before submitting to RAMP, you can test your solution locally to ensure that trivial errors (e.g. typos, path issues, etc.) are resolved. We can test a given submission using the `ramp` command that was installed in the virtual environment.  \n",
    "We'll use the following command:  \n",
    "`!ramp-test --submission sample --quick-test`  \n",
    "The `!` signals that the command should be run on the command line instead of this notebook.  \n",
    "`ramp` is the command to be executed.  \n",
    "`test` is the first argument; it signals `ramp` to perform a local test.  \n",
    "`--submission sample` specifies which submission to run. You can have multiple potential submissions in the `submissions/` directory; this prevents `ramp` from running all of them.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n",
      "/home/frcaud/anaconda3/envs/ramp-stroke/lib/python3.8/site-packages/bids/config.py:39: FutureWarning: Setting 'extension_initial_dot' will be removed in pybids 0.16.\n",
      "  warnings.warn(\"Setting 'extension_initial_dot' will be removed in pybids 0.16.\",\n",
      "\u001b[38;5;178m\u001b[1mTesting ATLAS Stroke Lesion Segmentation\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading train and test files from ./data/ ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mReading cv ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mTraining submissions/sample ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 0\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                      \u001b[38;5;10m\u001b[1m0.012\u001b[0m  \u001b[38;5;150m1.257557\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                      \u001b[38;5;12m\u001b[1m0.004\u001b[0m  \u001b[38;5;105m0.000009\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                       \u001b[38;5;1m\u001b[1m0.017\u001b[0m  \u001b[38;5;218m0.000004\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 1\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                      \u001b[38;5;10m\u001b[1m0.012\u001b[0m  \u001b[38;5;150m1.323619\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                      \u001b[38;5;12m\u001b[1m0.009\u001b[0m  \u001b[38;5;105m0.000010\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                       \u001b[38;5;1m\u001b[1m0.017\u001b[0m  \u001b[38;5;218m0.000005\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 2\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                      \u001b[38;5;10m\u001b[1m0.006\u001b[0m  \u001b[38;5;150m1.301342\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                      \u001b[38;5;12m\u001b[1m0.018\u001b[0m  \u001b[38;5;105m0.000007\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                       \u001b[38;5;1m\u001b[1m0.017\u001b[0m  \u001b[38;5;218m0.000004\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 3\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                      \u001b[38;5;10m\u001b[1m0.018\u001b[0m  \u001b[38;5;150m1.360942\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                      \u001b[38;5;12m\u001b[1m0.001\u001b[0m  \u001b[38;5;105m0.000010\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                       \u001b[38;5;1m\u001b[1m0.017\u001b[0m  \u001b[38;5;218m0.000005\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 4\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                      \u001b[38;5;10m\u001b[1m0.012\u001b[0m  \u001b[38;5;150m1.287049\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                      \u001b[38;5;12m\u001b[1m0.009\u001b[0m  \u001b[38;5;105m0.000006\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                       \u001b[38;5;1m\u001b[1m0.017\u001b[0m  \u001b[38;5;218m0.000005\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mMean CV scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore Sørensen–Dice Coefficient        time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m            \u001b[38;5;10m\u001b[1m0.012\u001b[0m \u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m \u001b[38;5;150m0.0036\u001b[0m  \u001b[38;5;150m1.3\u001b[0m \u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m \u001b[38;5;150m0.03\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m            \u001b[38;5;12m\u001b[1m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m08\u001b[0m \u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m058\u001b[0m   \u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                \u001b[38;5;1m\u001b[1m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m\u001b[0m17\u001b[0m \u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m\u001b[0m   \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mBagged scores\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1m----------------------------\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  Sørensen–Dice Coefficient\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[\u001b[38;5;12m\u001b[1m0\u001b[0mm                          \u001b[38;5;12m\u001b[1m0\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[\u001b[38;5;1m\u001b[1m0\u001b[0mm                           \u001b[38;5;1m\u001b[1m0\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!ramp-test --quick-test --submission sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the results are not very good, but that is expected: our estimator completely ignores the data!\n",
    "\n",
    "RAMP will automatically perform 5-fold cross-validation and report the Sørensen–Dice for each of the folds and report the mean across the folds. Bagging of the results has been disabled; the output can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Scores used <a name=scores></a>\n",
    "- [__Sørensen–Dice__ coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient): measures the overlap between the prediction and the ground truth. The measure is bounded between 0 and 1, with 0 indicating no overlap and 1 indicating perfect overlap.\n",
    "\n",
    "You can find the scoring metrics in the `scoring.py` file. If you want to use the score to evaluate your own results, use the `calc_score` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class DiceCoeff in module stroke.scoring:\n",
      "\n",
      "class DiceCoeff(rampwf.score_types.base.BaseScoreType)\n",
      " |  DiceCoeff(name='Sørensen–Dice Coefficient', precision=3)\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      DiceCoeff\n",
      " |      rampwf.score_types.base.BaseScoreType\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, y_true: tuple, y_pred: <built-in function array>)\n",
      " |      Call self as a function.\n",
      " |  \n",
      " |  __init__(self, name='Sørensen–Dice Coefficient', precision=3)\n",
      " |      Scoring class for RAMP workflows. When called, returns the Sørensen–Dice coefficient. Note that this\n",
      " |      implementation allows for continuous values in the prediction.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : str\n",
      " |          Name of the score; used for creating column headers.\n",
      " |      precision : str\n",
      " |          Numerical precision.\n",
      " |  \n",
      " |  score_function(self, Y_true: <built-in function array>, Y_pred: <built-in function array>)\n",
      " |      Returns the Sørensen–Dice coefficient for the input images. If multiple samples are given, the mean score\n",
      " |      is returned.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      true_bids : BIDSPrediction\n",
      " |          BIDSPrediction with true_bids.y_true set as an array.\n",
      " |      pred : np.array\n",
      " |          Array containing the predicted labels of an image.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      dice_coefficient : float\n",
      " |          Sørensen–Dice coefficient.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  calc_score(array_0: <built-in function array>, array_1: <built-in function array>)\n",
      " |      Performs the calculation to get the Sørensen–Dice coefficient.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      array_0 : np.array\n",
      " |          First array to score.\n",
      " |      array_1 : np.array\n",
      " |          Second array to score.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      float\n",
      " |          Sørensen–Dice coefficient\n",
      " |  \n",
      " |  calc_score_parts(array_0: <built-in function array>, array_1: <built-in function array>)\n",
      " |      Computes the three parts of the Sørensen–Dice coefficient: overlap and 2 positives\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      array_0\n",
      " |      array_1\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      tuple\n",
      " |          Tuple containing (overlap, sum(array_0), sum(array_1)\n",
      " |  \n",
      " |  check_y_pred_dimensions(array_0: <built-in function array>, array_1: <built-in function array>)\n",
      " |      Checks that the dimensions of the inputs are consistent.\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      array_0 : np.array\n",
      " |          First array to check.\n",
      " |      array_1 : np.array\n",
      " |          Second array to check\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      bool\n",
      " |  \n",
      " |  unpack_data(array_0: <built-in function array>, output_shape: <built-in function array>)\n",
      " |      Unpacks boolean data packed via np.packbits into appropriate shape, discarding excess bytes\n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      array_0 : np.array\n",
      " |          np.uint8 array to unpack\n",
      " |      output_shape : tuple\n",
      " |          Expected shape of output.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.array\n",
      " |          Unpacked, reshape array\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from rampwf.score_types.base.BaseScoreType:\n",
      " |  \n",
      " |  worst\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from rampwf.score_types.base.BaseScoreType:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from stroke.scoring import DiceCoeff\n",
    "help(DiceCoeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calc_score in module stroke.scoring:\n",
      "\n",
      "calc_score(array_0: <built-in function array>, array_1: <built-in function array>)\n",
      "    Performs the calculation to get the Sørensen–Dice coefficient.\n",
      "    Parameters\n",
      "    ----------\n",
      "    array_0 : np.array\n",
      "        First array to score.\n",
      "    array_1 : np.array\n",
      "        Second array to score.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    float\n",
      "        Sørensen–Dice coefficient\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(DiceCoeff.calc_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the reference, if you wish to view previous medical image segmentation challenges:\n",
    "    \n",
    "- [Cada (Cerebral Aneurysm Detection) grand-challenge](https://cada.grand-challenge.org/)\n",
    "- [Augmented Segmentation of Coronary Arteries](https://asoca.grand-challenge.org/)\n",
    "- [TN (Thyroid Nodule Segmentation and Classification) SCUI2020](https://tn-scui2020.grand-challenge.org/)\n",
    "- [Pneumothorax Segmentation SIIM-ACR](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation)\n",
    "- [Brats brain tumor segmentation Challenge 2020](https://www.med.upenn.edu/cbica/brats2020/data.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When your estimator in `estimator.py` is ready, place it in the `submissions/` directory in its own, unique directory (e.g., the path should be `submissions/my_estimator/estimator.py`).\n",
    "\n",
    "You can then test your submission locally using the command:\n",
    "\n",
    "`ramp-test --submission <your submission folder name>`  \n",
    "Example:\n",
    "`ramp-test --submission my_estimator`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on how to submit your code on [ramp.studio](https://ramp.studio/), refer to the [online documentation](https://paris-saclay-cds.github.io/ramp-docs/ramp-workflow/stable/using_kits.html)."
   ]
  }
 ],
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