bovine_embryo_survival_prediction_starting_kit.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "  <tr style=\"background-color:transparent;\">\n",
    "    <td align=\"left\"; style=\"background-color:transparent; width: 33%;\"><a href=\"https://www.inrae.fr\"><img src=\"https://www.inrae.fr/themes/custom/inrae_socle/logo.svg\" width=\"40%\"></a></td>\n",
    "    <td align=\"center\"; style=\"background-color:transparent; width: 33%;\"><a href = \"https://dataia.eu/\"><img src=\"https://github.com/ramp-kits/bovine_embryo_survival_prediction/raw/main/DATAIA-h.png\" width=\"80%\"></a></td>\n",
    "    <td align=\"right\"; style=\"background-color:transparent; width: 33%;\"><a href=\"https://www.vet-alfort.fr/\"><img src=\"https://www.vet-alfort.fr/images/logo-enva.svg\" width=\"55%\"></a></td>\n",
    "  </tr>\n",
    "</table> \n",
    "</div>\n",
    "\n",
    "<center><h1>Bovine Embryos Movies challenge</h1></center>\n",
    "\n",
    "<center><h3>A data challenge on early prediction of the fate of bovine embryos</h3></center>\n",
    "<br/>\n",
    "<center><i>Julien Chiquet (MIA Paris-Saclay, Inrae), Pierre Gloaguen (MIA Paris-Saclay, AgroParisTech), Nicolas Jouvin (MIA Paris-Saclay), Patrick Bouthemy (SERPICO, Inria), Alain Truibil (MaiAGE, Inrae), Alline Reis (PASP, ENVA), François Caud, Jacobo Ruiz Ocampo, Alexandre Gramfort (DATAIA, Univ. Paris-Saclay) </i></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This challenge consists in predicting the developmental state of bovine embryos seen at 192 hours after fertilisation (HAF). There are 8 different classes (denoted from \"A\" to \"H\" in this challenge) corresponding to biological states from alive (\"A\") to dead (\"H\").\n",
    "\n",
    "The known labels are the developmental state of the embryos at 192 HAF. The goal of this challenge is to predict the future embryo state at 192 HAF using only information available between 27 and 94 HAF (at latest). The  dataset consists in 277 videos from INRAE's database. Each video is a timelapse of an embryo's development consisting in 300 frames taken every fifteen minutes. We want to predict the 192 HAF's label at 10 different times, namely 27, 32, 37, 40, 44, 48, 53, 58, 63, 94 hours after fertilization. \n",
    "\n",
    "The biological labels at 192 HAF are expert-based and use both the final state of the embryos (*i.e* the final frame) but also the whole video dynamics. Therefore, it might be important to take into account the succession of frames for good classification performances.\n",
    "\n",
    "**Note about the prediction times:** Due to experimental conditions, the first frame is recorded at a time t0 ($t_0$) that might vary between videos. The timestamp information of each frame in each video is available and accessible. The predicting model will aim at predicting at specific *times* (and not frame numbers).\n",
    "\n",
    "**Note about the diffusion of the data:** Any communication from this challenge must be authorized by the owners of the Database before publication."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<table style=\"width:100%; background-color:transparent;\">\n",
    "  <tr style=\"background-color:transparent;\">    \n",
    "    <td align=\"center\"; style=\"background-color:transparent; width: 33%;\"><a href = \"https://dataia.eu/\"><img src=\"https://github.com/ramp-kits/bovine_embryo_survival_prediction/raw/main/logos/challenge_image.png\" width=\"80%\"></a></td>\n",
    "    \n",
    "</table> \n",
    "</div>\n",
    "\n",
    "<center><b>Fig.1 - Description of two video data for this challenge. The green part covers from 24 to 94 HAF while the whole video (green & yellow) ends at 192 HAF and was watched by an expert to design the label (blue). The challenge consists in predicting blue having only access to the green part for training and testing.  </b></center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "### Prerequisites\n",
    "\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."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **NOTE:** Due to the structure of the challenge, libraries not included in `requirements.txt` will need to be added to be added via a pull request to the [GitHub repo](https://github.com/ramp-kits/bovinsembryos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the `ramp-workflow` package from PyPI using the following command in you dedicated python environment.\n",
    "\n",
    "```pip install ramp-workflow```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The problem.py file\n",
    "\n",
    "This file contains the definition of the data-challenge according to the RAMP framework. In addition, it contains some useful methods and class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from problem import get_train_data, get_test_data, WeightedClassificationError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading the data\n",
    "\n",
    "The public data are stored in a public OSF repository, you only need to run the following script which create the `data/` repository locally.\n",
    "\n",
    "> Note that, in order to be registered to the RAMP studio event, partiticipants must fill the [following form](https://framaforms.org/access-request-to-the-bovmovies2pred-data-challenge-osf-dataset-1646386323) which contain an agreement on the use of the data.\n",
    "\n",
    "```python download_data.py```"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!python download_data.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration <a name=data_exp></a>\n",
    "\n",
    "### Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we load the data using the utility function designed for the challenge in `problem.py`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "videos_train, labels_train = get_train_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`labels_trains` simply consists in the set of labels for the training set, stored as a `numpy.darray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['H' 'H' 'A' 'F' 'H' 'A' 'A' 'F' 'F' 'A']\n",
      "(177,)\n",
      "Number of videos in the training set: 177\n"
     ]
    }
   ],
   "source": [
    "print(labels_train[:10])\n",
    "print(labels_train.shape)\n",
    "print('Number of videos in the training set: {}'.format(labels_train.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand `videos_train` is a list of objects from the class `VideoReader`. This class is fully described below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'problem.VideoReader'>\n"
     ]
    }
   ],
   "source": [
    "print(type(videos_train)) # List\n",
    "print(type(videos_train[0])) # VideoReader object (see below)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data visualization \n",
    "\n",
    "A video is timelapse of the embryos development, with one image every 15 minutes. One may plot a video image at a specific time (expressed in hour) via the `read_frame` helper method (described below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Here, we use the read_frame method to extract a specific time\n",
    "# See below for the description of this method\n",
    "plt.imshow(videos_train[20].read_frame(frame_time=24), cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionaly, one can plot the video's dynamics between two given times with the `plot_sequence` method. However, in order to better visualize  the dynamics we recommand using a dedicated video software such as VLC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "videos_train[20].plot_sequence(27., 31.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One may also inspect the class distribution among the 8 possible categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labs, counts = np.unique(labels_train, return_counts=True)\n",
    "plt.bar(labs, counts)\n",
    "plt.title(\"Barplot of class distribution in the train dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the class are imbalanced on this dataset, with few examples per class. Although the starting kit does not use them, data augmentation as well as oversampling might be advised. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The VideoReader class\n",
    "\n",
    "The `VideoReader` class aims at manipulating the videos. It is basically a wrapper for some openCV features of the library `cv2`, and its main aim is to ease the manipulation of videos for the participants. It is implemented in the `problem.py` file, still we copy its definition here for the sake of self-completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can import the VideoReader class from problem.py via\n",
    "from problem import VideoReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "class VideoReader:\n",
    "    def __init__(self, video_filename, frame_times, img_size=[250, 250]):\n",
    "        self.video = cv2.VideoCapture(video_filename)\n",
    "        self.nb_frames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "        self.img_size = img_size\n",
    "        self.frame_times = frame_times\n",
    "\n",
    "    def read_frame(self, frame_time):\n",
    "        \"\"\"Return the frame of a VideoReader object at the specified `frame_time`\n",
    "\n",
    "        Args:\n",
    "            frame_time (float): the specified time in hours (allowing quarter hours, e.g. 25.75 or 26.50)\n",
    "\n",
    "        Raises:\n",
    "            ValueError: If the specified time does not exist for the selected video\n",
    "\n",
    "        Returns:\n",
    "            np.ndarray: A 2-D array containing the grayscale image. \n",
    "        \"\"\"\n",
    "        if frame_time not in self.frame_times:\n",
    "            raise ValueError('The specified frame time must me within the time '\n",
    "                             'interval of the video.')\n",
    "\n",
    "        frame_nb = np.where(self.frame_times == frame_time)[0][0]\n",
    "        if frame_nb is not None:\n",
    "            self.video.set(cv2.CAP_PROP_POS_FRAMES, frame_nb)\n",
    "        _, frame = self.video.read()\n",
    "\n",
    "        # always reset video's frame counter to 0 to avoid unexpected behavior\n",
    "        self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\n",
    "\n",
    "        return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    def read_sequence(self, begin_time=None, end_time=None):\n",
    "        \"\"\"Extract the sequence of consecutive frames from begin_time to end_time (included)\n",
    "\n",
    "        Args:\n",
    "            begin_time (float, optional): The time where the extraction begins. Defaults to None.\n",
    "            end_time (float, optional):  The time where the extraction ends. Defaults to None.\n",
    "\n",
    "        Returns:\n",
    "            np.ndarray: A 3-D numpy array with first axis corresponding to the frame index and the remaining dimension to image size.\n",
    "        \"\"\"\n",
    "        if begin_time is None:\n",
    "            begin_time = self.frame_times[0]\n",
    "        elif begin_time not in self.frame_times:\n",
    "            raise ValueError('The specified begin_time must me within the time'\n",
    "                             ' interval of the video.')\n",
    "\n",
    "        if end_time is None:\n",
    "            end_time = self.frame_times[-1]\n",
    "        elif end_time not in self.frame_times:\n",
    "            raise ValueError('The specified end_time must me within the time '\n",
    "                             'interval of the video.')\n",
    "\n",
    "        if begin_time > end_time:\n",
    "            raise ValueError(\"begin_time must be smaller than end_time.\")\n",
    "\n",
    "        begin_nb = np.where(self.frame_times == begin_time)[0][0]\n",
    "        end_nb = np.where(self.frame_times == end_time)[0][0]\n",
    "        self.video.set(cv2.CAP_PROP_POS_FRAMES, begin_nb)\n",
    "\n",
    "        my_frames = list(range(begin_nb, end_nb + 1))\n",
    "        video_array = np.empty(\n",
    "            shape=(len(my_frames), self.img_size[0], self.img_size[1])\n",
    "        )\n",
    "        for t, _ in enumerate(my_frames):\n",
    "            _, frame = self.video.read()\n",
    "            video_array[t, :, :] = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # always reset video's frame counter to 0 to avoid unexpected behavior \n",
    "        self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\n",
    "\n",
    "        return video_array\n",
    "\n",
    "    def read_samples(self, selected_times=None):\n",
    "        \"\"\"Read several frames of the video at once corresponding to the selected times.\n",
    "\n",
    "        Args:\n",
    "            selected_times (list, optional): The list of of desired extraction times, in hours (allowing quarter hour). Defaults to None, the whole 300 frames are returned.\n",
    "\n",
    "        Returns:\n",
    "            np.ndarray: A 3-D numpy array with of shape (size len(selected_times), 250, 250).\n",
    "        \"\"\"\n",
    "        if selected_times is None:\n",
    "            selected_times = self.frame_times\n",
    "\n",
    "        res = np.empty([len(selected_times), self.img_size[0], self.img_size[1]])\n",
    "        frame_nbs = np.where([t in selected_times for t in self.frame_times])[0]\n",
    "        for i, f in enumerate(frame_nbs):\n",
    "            self.video.set(cv2.CAP_PROP_POS_FRAMES, f)\n",
    "            _, frame = self.video.read()\n",
    "\n",
    "            res[i, :, :] = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\n",
    "\n",
    "        return res\n",
    "    \n",
    "    def plot_sequence(self, begin_time=None, end_time=None, fig_width=20.0):\n",
    "        \"\"\"Plots the result of read_sequence.\n",
    "\n",
    "        Args:\n",
    "            begin_time (float, optional): The time where the extraction begins.\n",
    "            Defaults to None.\n",
    "            end_time (float, optional):  The time where the extraction ends.\n",
    "            Defaults to None.\n",
    "            fig_width (float, optional): The total figure width, height is\n",
    "            adapted automatically.\n",
    "\n",
    "        Returns:\n",
    "            None (but displays the matplotlib figure).\n",
    "        \"\"\"\n",
    "        vid_arr = self.read_sequence(begin_time, end_time)\n",
    "        n_vids = vid_arr.shape[0]\n",
    "\n",
    "        # Create subplots of 10 columns\n",
    "        num_cols = 10\n",
    "        num_rows = int(n_vids // num_cols)\n",
    "        num_rows = num_rows + 1 if n_vids % num_cols != 0 else num_rows\n",
    "\n",
    "        fig_height = fig_width * num_rows / num_cols\n",
    "\n",
    "        fig = plt.figure(figsize=(fig_width, fig_height))\n",
    "        for i in range(1, num_rows * num_cols + 1):\n",
    "            if i - 1 >= n_vids:\n",
    "                break\n",
    "            img = vid_arr[i - 1]\n",
    "            fig.add_subplot(num_rows, num_cols, i)\n",
    "            plt.imshow(img, cmap=\"gray\")\n",
    "            plt.axis(\"off\")\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes\n",
    "\n",
    "The `VideoReader` class has 3 attributes illustrated in the next cell :\n",
    "- `video`: the video object, which is as cv2.VideoCapture (see [the documentation](https://docs.opencv.org/3.4/d8/dfe/classcv_1_1VideoCapture.html))\n",
    "- `nb_frames`: the number of frames. This should be 300 for each video.\n",
    "- `frame_times`:  a 1-D `numpy.array` containing the times at which the frames where recorded. These times are expressed in hours since the fecondation. **All frames** are separated by 15 mns (0.25 hours). However, due to different experimental conditions, videos **might start at different times**!\n",
    "- `img_size` returns the dimensions of the square images composing the video. This should be normalized to $250 \\times 250$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A video is stored as a <class 'cv2.VideoCapture'> Python object.\n",
      "It has 300 frames of size [250, 250].\n",
      "The frame are recorded at the following times (expressed as time in hour since the fecondation)\n",
      "[23.75 24.   24.25 24.5  24.75 25.   25.25 25.5  25.75 26.   26.25 26.5\n",
      " 26.75 27.   27.25 27.5  27.75 28.   28.25 28.5  28.75 29.   29.25 29.5\n",
      " 29.75 30.   30.25 30.5  30.75 31.  ]\n"
     ]
    }
   ],
   "source": [
    "example_video = videos_train[0]\n",
    "print(\"A video is stored as a \" + str(type(example_video.video)) + \" Python object.\")\n",
    "print(\"It has \" + str(example_video.nb_frames) + \" frames of size \" + str(example_video.img_size) + \".\")\n",
    "print(\"The frame are recorded at the following times (expressed as time in hour since the fecondation)\")\n",
    "print(example_video.frame_times[0:30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methods\n",
    "The `VideoReader` class has methods illustrated in the next cell :\n",
    "- The `read_frame` method returns the frame of the video at a specified time.  The user must provide the `frame_time` argument, which is a time present in the `frame_times` attribute. This method returns a 2-D `numpy.ndarray`.\n",
    "- The `read_sequence` method returns a sequence of **consecutive frames**. The user must provide the `begin_time` argument (the frame time at which the sequence begins) and the `end_time` (the frame time at which the sequence ends). By default, these two arguments are set to `None`, which results in starting at first time and ending at last time. The method returns a 3-D ($n \\times 250 \\times 250$) `numpy.ndarray`, where $n$ is the number of consecutive times between `begin_time` and `end_time` (included).\n",
    "- The `read_samples` method transforms the video to a 3-D ($300 \\times 250 \\times 250$) `numpy.ndarray`. Optionally, a `selected_times` argument (provided as a list) may be specified to only select frames corresponding to these times.\n",
    "- The `plot_sequence` method plots the output of `read_sequence`, *i.e.* the consecutive frames to display video dynamics. However, the video are best visualized in a dedicated software like VLC. Agai, the user must provide the `begin_time` argument (the frame time at which the sequence begins) and the `end_time` (the frame time at which the sequence ends)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot the videos frames corresponding to times: [23.75 73.75 98.5 ] hours.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_times = example_video.frame_times[[0, 200, 299]] \n",
    "print(\"Plot the videos frames corresponding to times: \" + str(selected_times) + \" hours.\")\n",
    "for frame_time in selected_times:\n",
    "    plt.imshow(example_video.read_frame(frame_time), cmap=\"gray\")\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 250, 250)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_array = example_video.read_sequence()\n",
    "video_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 250, 250)\n",
      "(2, 250, 250)\n"
     ]
    }
   ],
   "source": [
    "video_array = example_video.read_sequence(end_time = 29)\n",
    "print(video_array.shape)\n",
    "\n",
    "video_array = example_video.read_sequence(begin_time = 28.75, end_time=29)\n",
    "print(video_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 250, 250)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_array = example_video.read_samples(selected_times=[23.75, 62.25, 96])\n",
    "video_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Caution:** the VideoReader class does not load all the videos frame into memory, which is useful in order not to overload you local machine or the RAMP servers. However, the `read_sample` or `read_sequence` may quickly lead to memory issues when trying to load every videos. You code should take this issue into account and treat videos sequentially or, at least, batch-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DO NOT TRY TO RUN (memory will collapse)\n",
    "# data = []\n",
    "# for video in videos_train:\n",
    "#     data.append(data.read_samples())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Designing a submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation\n",
    "\n",
    "For the moment, we only consider the overall classification accuracy of your classifiers.\n",
    "\n",
    "Locally, the RAMP platform use a 3-fold cross-validation scheme implemented for you in the `get_cv` method. Moreover, the classifier's performance is evaluated on a separate test set which can be loaded just as the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "videos_test, labels_test  = get_test_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subjects in the test set: 100\n"
     ]
    }
   ],
   "source": [
    "print('Number of subjects in the test set: {}'.format(labels_test.size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling several prediction times\n",
    "\n",
    "Remember that the challenge's objective is to design classifier(s) for different prediction times between 1 and 4 days (24 to 96 hours). Your submission should account for that constraints with a `pred_time` argument for the model fit and prediction, ensuring that only frames up to `pred_times` are used for model training and testing.\n",
    "\n",
    "The predefined and fixed prediction times are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_times = [27, 32, 37, 40, 44, 48, 53, 58, 63, 94]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `None` defaults to the use of the whole 300 frame for each video.\n",
    "\n",
    "> **Important:** You are freed to opt for having one model for each prediction time, or one model for every time. However, note that for the second option your model will be fully retrained at each prediction time, and that it must handle videos with a varying number of frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mandatory structure of a submission\n",
    "\n",
    "\n",
    "A submission (usually stored in `./subsmissions/<submission_foldername>/`) must contain on file named `videoclassifier.py`.\n",
    "\n",
    "This python script must itself implement at least a `VideoClassifier` class with\n",
    " * A `fit(videos, y, pred_time)` method.\n",
    " * A `pred(videos, pred_time)` method.\n",
    "\n",
    "The two arguments must be understood as follow:\n",
    " * `videos` is a list of `VideoReader` object with videos object cut at `pred_times`.\n",
    " * `y` is a 1-D numpy array containing the associated training labels.\n",
    " * `pred_time` is the time of development for which the prediction shall be made. \n",
    "\n",
    "We illustrate this below with a simple example.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Illustration : a dummy random classifier\n",
    "\n",
    "This classifier does not use (nor even load into memory) the videos or the prediction time, and just predict random labels. Still, it is a valid (albeit unuseful) submission regarding the RAMP workflow.\n",
    "\n",
    "It is implemented in the `./subsmissions/starting_kit/` folder and we copy its code here for illustration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class VideoClassifier(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def fit(self, videos: list, y, pred_time: float):\n",
    "        classes = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]\n",
    "        self.n_classes = len(classes)\n",
    "        pass\n",
    "\n",
    "    def predict(self, videos: list, pred_time: float):\n",
    "        proba = np.random.rand(len(videos), self.n_classes)\n",
    "        proba /= proba.sum(axis=1)[:, np.newaxis]\n",
    "        return proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_pred_time = 94\n",
    "my_model = VideoClassifier()\n",
    "my_model.fit(videos_train, labels_train, pred_time=my_pred_time)\n",
    "y_pred = my_model.predict(videos_test, pred_time=my_pred_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Score function\n",
    "\n",
    "For a a given prediction time (in HAF) your prediction score will be a classification **error**: the LOWER the better. Moreover, not all mistakes costs the same for this problem, as some classes are more \"distinct\" than others. Thus, we use a weight matrix $W$ where the weights were determined by an expert and we give the matrix $W$ below.\n",
    "\n",
    "The formula of the weighted classification error for a hard label $y_i \\in \\{1, ..., K\\}$ and a hard-prediction $\\hat{y}_i \\in \\{ 1, ..., K \\}$ is:\n",
    "\n",
    "$$\n",
    "    WCE(y, \\hat{y}) = \\frac{1}{n} \\sum_{i=1}^n W_{y_i,\\hat{y}_i} \n",
    "$$\n",
    "\n",
    "In practice, your model may output a probability of each label for observation $i$: $\\hat{p}_i = (\\hat{p}_{ik})_{k=1, \\ldots, K} $. The prediction $\\hat{y}_i$ is then obtained as $\\arg\\max_k \\hat{p}_{ik}$.\n",
    "\n",
    "##### Weight matrix $W$\n",
    "\n",
    "> Note: The weight $W$ could be used to implement your own objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = np.array(\n",
    "    [\n",
    "        [0, 1, 6, 10, 10, 10, 10, 10],\n",
    "        [1, 0, 3, 10, 10, 10, 10, 10],\n",
    "        [6, 3, 0, 2, 9, 10, 10, 10],\n",
    "        [10, 10, 2, 0, 9, 9, 10, 10],\n",
    "        [10, 10, 9, 9, 0, 8, 8, 8],\n",
    "        [10, 10, 10, 9, 8, 0, 9, 8],\n",
    "        [10, 10, 10, 10, 8, 9, 0, 9],\n",
    "        [10, 10, 10, 10, 8, 8, 9, 0],\n",
    "    ]\n",
    ")\n",
    "\n",
    "labels = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(W, cmap=\"GnBu\")\n",
    "\n",
    "# Show all ticks and label them with the respective list entries\n",
    "ax.set_xticks(np.arange(len(labels)), labels=labels)\n",
    "ax.set_yticks(np.arange(len(labels)), labels=labels)\n",
    "\n",
    "# Rotate the tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(labels)):\n",
    "    for j in range(len(labels)):\n",
    "        text = ax.text(j, i, W[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"w\")\n",
    "\n",
    "# space between tiles\n",
    "ax.set_xticks(np.arange(8+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(8+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "ax.set_title(\"Missclassification weight between each class\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Score of the dummy random classifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['A' 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['A' 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['G' 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0]\n",
      " ['A' 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['F' 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0]\n",
      " ['C' 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['H' 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0]\n",
      " ['C' 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['C' 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0]\n",
      " ['H' 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# we need to convert labels (str) to 1-hot encoding (n, 8) \n",
    "labels_test = labels_test.reshape(-1, 1)\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(labels_test)\n",
    "y_true = enc.transform(labels_test)\n",
    "\n",
    "# Example of 1-hot encoding for the 10 first test points\n",
    "print(np.hstack([labels_test[:10], y_true.todense()[:10, :]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sparse csr matrix -> numpy.matrix\n",
    "y_true = y_true.todense()\n",
    "# np.matrix -> np.array\n",
    "y_true = np.asarray(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7490000000000001"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wce = WeightedClassificationError(time_idx=my_pred_time)\n",
    "wce.compute(y_true=y_true, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WeightedClassificationError[1]  at time 27 is: 0.7490000000000001\n",
      "WeightedClassificationError[2]  at time 32 is: 0.726\n",
      "WeightedClassificationError[3]  at time 37 is: 0.7170000000000001\n",
      "WeightedClassificationError[4]  at time 40 is: 0.72\n",
      "WeightedClassificationError[5]  at time 44 is: 0.796\n",
      "WeightedClassificationError[6]  at time 48 is: 0.762\n",
      "WeightedClassificationError[7]  at time 53 is: 0.7630000000000001\n",
      "WeightedClassificationError[8]  at time 58 is: 0.746\n",
      "WeightedClassificationError[9]  at time 63 is: 0.7659999999999999\n",
      "WeightedClassificationError[10]  at time 94 is: 0.708\n"
     ]
    }
   ],
   "source": [
    "err = np.zeros((len(pred_times),))\n",
    "all_preds = []\n",
    "for time_idx, my_pred_time in enumerate(pred_times):\n",
    "    my_model = VideoClassifier()\n",
    "    my_model.fit(videos_train, labels_train, pred_time=my_pred_time)\n",
    "    y_pred = my_model.predict(videos_test, pred_time=my_pred_time)\n",
    "    all_preds.append(y_pred)\n",
    "    wce = WeightedClassificationError(time_idx=time_idx)\n",
    "    err[time_idx] = wce.compute(y_true=y_true, y_pred=y_pred)\n",
    "    print(wce.name, ' at time', my_pred_time, 'is:', err[time_idx])\n",
    "\n",
    "all_preds = np.concatenate(all_preds, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The ranking method on RAMP-board\n",
    "\n",
    "The error is computed for a given time $t$ and a given prediction $\\hat{p}_i^{(t)}$. A submission will then have $10$ different scores that need to be aggregated in order to rank participants. The chosen method is the *Area Under the Curve* (AUC) of the loss evolution over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Evolution of the error with the prediction time (in HAF)')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(pred_times, err) \n",
    "plt.ylim(0, 1)\n",
    "plt.title(\"Evolution of the error with the prediction time (in HAF)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7434402985074626"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from problem import AreaUnderCurveError\n",
    "\n",
    "auc = AreaUnderCurveError(score_func_name=\"classification\", prediction_times=pred_times)\n",
    "\n",
    "auc.compute(y_true=y_true, y_pred=all_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pred_times are normalized in the AUC computation, in order to have a loss between 0 and 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of a deterministic classifier\n",
    "\n",
    "To compare to the performance of a (dummy) classifier, returning only one of the 8 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying all videos in class 0 gives a score of: 0.78\n",
      "Classifying all videos in class 1 gives a score of: 0.746\n",
      "Classifying all videos in class 2 gives a score of: 0.677\n",
      "Classifying all videos in class 3 gives a score of: 0.768\n",
      "Classifying all videos in class 4 gives a score of: 0.7070000000000002\n",
      "Classifying all videos in class 5 gives a score of: 0.831\n",
      "Classifying all videos in class 6 gives a score of: 0.778\n",
      "Classifying all videos in class 7 gives a score of: 0.762\n"
     ]
    }
   ],
   "source": [
    "n_test = y_true.shape[0]\n",
    "for k in range(8):\n",
    "    y_k = np.zeros(y_true.shape)\n",
    "    y_k[range(n_test), k] = 1 \n",
    "    print(\n",
    "        'Classifying all videos in class', k, \n",
    "        'gives a score of:', wce.compute(y_true=y_true, y_pred=y_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting to RAMP\n",
    "\n",
    "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 <subm_folder> --quick-test`  \n",
    "The `!` signals that the command should be run on the command line instead of this notebook.  \n",
    "`ramp-test` is the command to be executed. It signals `ramp` to perform a local test. \n",
    "`--submission <subm_folder>` 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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;178m\u001b[1mTesting Bovine embryos survival prediction\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/starting_kit ...\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 0\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  AUC[classification]  WeightedClassifErr[1]  WeightedClassifErr[2]  WeightedClassifErr[3]  WeightedClassifErr[4]  WeightedClassifErr[5]  WeightedClassifErr[6]  WeightedClassifErr[7]  WeightedClassifErr[8]  WeightedClassifErr[9]  WeightedClassifErr[10]      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                 \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.68\u001b[0m\u001b[0m                   \u001b[38;5;150m0.70\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.60\u001b[0m\u001b[0m                   \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.68\u001b[0m\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.60\u001b[0m\u001b[0m                   \u001b[38;5;150m0.65\u001b[0m                   \u001b[38;5;150m0.80\u001b[0m                   \u001b[38;5;150m0.71\u001b[0m                   \u001b[38;5;150m0.77\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.67\u001b[0m\u001b[0m                    \u001b[38;5;150m\u001b[38;5;150m0.67\u001b[0m\u001b[0m  \u001b[38;5;150m0.002093\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                 \u001b[38;5;12m\u001b[1m0.79\u001b[0m                   \u001b[38;5;105m0.98\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.93\u001b[0m\u001b[0m                   \u001b[38;5;105m0.82\u001b[0m                   \u001b[38;5;105m0.77\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.80\u001b[0m\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.80\u001b[0m\u001b[0m                   \u001b[38;5;105m0.72\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.93\u001b[0m\u001b[0m                   \u001b[38;5;105m1.00\u001b[0m                    \u001b[38;5;105m0.45\u001b[0m  \u001b[38;5;105m0.001507\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                  \u001b[38;5;1m\u001b[1m0.72\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.75\u001b[0m\u001b[0m                   \u001b[38;5;218m0.79\u001b[0m                   \u001b[38;5;218m0.80\u001b[0m                   \u001b[38;5;218m0.78\u001b[0m                   \u001b[38;5;218m0.81\u001b[0m                   \u001b[38;5;218m0.62\u001b[0m                   \u001b[38;5;218m0.66\u001b[0m                   \u001b[38;5;218m0.76\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.75\u001b[0m\u001b[0m                    \u001b[38;5;218m0.65\u001b[0m  \u001b[38;5;218m0.001463\u001b[0m\n",
      "\u001b[38;5;178m\u001b[1mCV fold 1\u001b[0m\n",
      "\t\u001b[38;5;178m\u001b[1mscore  AUC[classification]  WeightedClassifErr[1]  WeightedClassifErr[2]  WeightedClassifErr[3]  WeightedClassifErr[4]  WeightedClassifErr[5]  WeightedClassifErr[6]  WeightedClassifErr[7]  WeightedClassifErr[8]  WeightedClassifErr[9]  WeightedClassifErr[10]      time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m                 \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.7\u001b[0m3\u001b[0m                   \u001b[38;5;150m0.82\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m5\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m4\u001b[0m\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m4\u001b[0m\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m0\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m9\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m6\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m8\u001b[0m                   \u001b[38;5;150m\u001b[38;5;150m0.7\u001b[0m1\u001b[0m                     \u001b[38;5;150m0.7\u001b[0m  \u001b[38;5;150m0.001846\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                 \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.78\u001b[0m\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.8\u001b[0m0\u001b[0m\u001b[0m                   \u001b[38;5;105m0.95\u001b[0m                   \u001b[38;5;105m0.77\u001b[0m                   \u001b[38;5;105m0.60\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.8\u001b[0m0\u001b[0m\u001b[0m                   \u001b[38;5;105m0.92\u001b[0m                   \u001b[38;5;105m0.62\u001b[0m                   \u001b[38;5;105m0.67\u001b[0m                   \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.78\u001b[0m\u001b[0m                     \u001b[38;5;105m0.8\u001b[0m  \u001b[38;5;105m0.002419\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;218m0.8\u001b[0m1\u001b[0m\u001b[0m                   \u001b[38;5;218m0.66\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.77\u001b[0m\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.79\u001b[0m\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.8\u001b[0m3\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.8\u001b[0m2\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.79\u001b[0m\u001b[0m                   \u001b[38;5;1m\u001b[1m\u001b[38;5;218m\u001b[38;5;218m0.8\u001b[0m1\u001b[0m\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.77\u001b[0m\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.8\u001b[0m5\u001b[0m                     \u001b[38;5;218m0.8\u001b[0m  \u001b[38;5;218m0.001709\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 AUC[classification] WeightedClassifErr[1] WeightedClassifErr[2] WeightedClassifErr[3] WeightedClassifErr[4] WeightedClassifErr[5] WeightedClassifErr[6] WeightedClassifErr[7] WeightedClassifErr[8] WeightedClassifErr[9] WeightedClassifErr[10]       time\u001b[0m\n",
      "\t\u001b[38;5;10m\u001b[1mtrain\u001b[0m        \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.71\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m24\u001b[0m           \u001b[38;5;150m0.76\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m6\u001b[0m          \u001b[38;5;150m0.68\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m71\u001b[0m          \u001b[38;5;10m\u001b[1m\u001b[38;5;150m0.71\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m27\u001b[0m          \u001b[38;5;150m\u001b[38;5;150m0.67\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m6\u001b[0m7          \u001b[38;5;150m\u001b[38;5;150m0.67\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m23\u001b[0m          \u001b[38;5;150m0.79\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m02\u001b[0m          \u001b[38;5;150m0.74\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m25\u001b[0m          \u001b[38;5;150m0.78\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m04\u001b[0m          \u001b[38;5;150m\u001b[38;5;150m0.69\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m19\u001b[0m           \u001b[38;5;150m\u001b[38;5;150m0.69\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m15\u001b[0m  \u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m\u001b[38;5;150m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;150m\u001b[38;5;150m0.0\u001b[0m\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m        \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.79\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m\u001b[0m07\u001b[0m          \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.8\u001b[0m\u001b[0m9\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m\u001b[0m92\u001b[0m          \u001b[38;5;105m0.94\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m\u001b[38;5;105m0.0\u001b[0m\u001b[0m\u001b[0m08\u001b[0m          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      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m          \u001b[38;5;1m\u001b[1m\u001b[38;5;218m0.76\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m4\u001b[0m          \u001b[38;5;218m\u001b[38;5;218m0.71\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m±\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m \u001b[38;5;218m\u001b[38;5;218m\u001b[38;5;218m0.0\u001b[0m\u001b[0m4\u001b[0m3           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      "\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  AUC[classification]  WeightedClassifErr[1]  WeightedClassifErr[2]  WeightedClassifErr[3]  WeightedClassifErr[4]  WeightedClassifErr[5]  WeightedClassifErr[6]  WeightedClassifErr[7]  WeightedClassifErr[8]  WeightedClassifErr[9]  WeightedClassifErr[10]\u001b[0m\n",
      "\t\u001b[38;5;12m\u001b[1mvalid\u001b[0m                 \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.79\u001b[0m\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.89\u001b[0m\u001b[0m                   \u001b[38;5;105m0.94\u001b[0m                   \u001b[38;5;12m\u001b[1m\u001b[38;5;105m0.79\u001b[0m\u001b[0m                   \u001b[38;5;105m0.68\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.80\u001b[0m\u001b[0m                   \u001b[38;5;105m0.86\u001b[0m                   \u001b[38;5;105m0.67\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.80\u001b[0m\u001b[0m                   \u001b[38;5;105m\u001b[38;5;105m0.89\u001b[0m\u001b[0m                    \u001b[38;5;105m0.62\u001b[0m\n",
      "\t\u001b[38;5;1m\u001b[1mtest\u001b[0m                  \u001b[38;5;1m\u001b[1m0.74\u001b[0m                   \u001b[38;5;218m0.84\u001b[0m                   \u001b[38;5;218m0.78\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.73\u001b[0m\u001b[0m                   \u001b[38;5;218m0.81\u001b[0m                   \u001b[38;5;218m\u001b[38;5;218m0.73\u001b[0m\u001b[0m                   \u001b[38;5;218m0.80\u001b[0m                   \u001b[38;5;218m0.72\u001b[0m                   \u001b[38;5;218m0.71\u001b[0m                   \u001b[38;5;218m0.76\u001b[0m                    \u001b[38;5;218m0.69\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!ramp-test --quick-test --submission starting_kit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the results are not very good, but that is expected: our estimator in starting_kit completely ignores the data and returns a random prediction!\n",
    "\n",
    "RAMP will automatically perform 3-fold cross-validation and report the WeightedClassificationError for each of the folds and each prediction times, along with the mean across the folds. Bagging of the results has been disabled; the output can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More information\n",
    "\n",
    "You can find more information in the [README](https://github.com/paris-saclay-cds/ramp-workflow/blob/master/README.md) of the [ramp-workflow library](https://github.com/paris-saclay-cds/ramp-workflow)."
   ]
  }
 ],
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