{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Info mode\n",
    "\n",
    "This notebook documents the capabilities of the MML info mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T13:38:33.101361Z",
     "start_time": "2024-01-17T13:38:28.657328Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# giving no tasks will deliver no output information\n",
    "# side note: this is the default configuration when entering solely 'mml' allowing to test a successful installation\n",
    "!mml info tasks=none"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "For further explanation we need some installed tasks, you may download and store them automatically by yourselves when running\n",
    "`mml create tasks=fake`. See the `create` mode tutorial for more details on this feature. Assume these tasks have been installed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T13:38:55.202197Z",
     "start_time": "2024-01-17T13:38:50.614625Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# giving some tasks will automatically present task information and create a sample grid\n",
    "!mml info tasks=fake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The output provides us with an overview on the most important features of the tasks we used. Further it generated a sample grid with one image per task. Let's inspect that image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T13:40:05.880972Z",
     "start_time": "2024-01-17T13:40:05.823832Z"
    },
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   "outputs": [],
   "source": [
    "# the grid was placed here: /home/scholzpa/Documents/exp/mml_results/default/PLOTS/sample_grid/grid_0079.png. as can be seen by the log\n",
    "from IPython import display\n",
    "\n",
    "display.Image(\"/home/scholzpa/Documents/exp/mml_results/default/PLOTS/sample_grid/grid_0079.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Be aware that the default preprocessing configuration has been applied!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Info mode further support inspecting\n",
    "  - trained models,\n",
    "  - study results of a (persistent!) hyperparameter optimization\n",
    "\n",
    "Use the respective `mode.subroutines` when calling `mml` and either specify the study or reuse some models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T13:45:58.620828Z",
     "start_time": "2024-01-17T13:45:53.976626Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!mml info mode.subroutines=[hpo] hpo=sql mode.study_name=pami2_fed_hpo_03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T13:51:32.860491Z",
     "start_time": "2024-01-17T13:51:28.385345Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!mml info mode.subroutines=[models] reuse.models=test tasks=fake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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