ML_Regression_Neural activity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's keep our notebook clean, so it's a little more readable!\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Machine learning to predict age from rs-fmri\n",
    "\n",
    "The goal is to extract data from several rs-fmri images, and use that data as features in a machine learning model. We will integrate what we've learned in the previous machine learning lecture to build an unbiased model and test it on a left out sample.\n",
    "\n",
    "Link to slides: https://figshare.com/s/4d9953055c9ed36301ad#/articles/9162428"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "<img src=\"Imgs/SampFeat.png\" alt=\"terms\" width=\"300\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change this to the location where you downloaded the data\n",
    "wdir = '/users/isabellesimard2/Documents/BrainHack_School_2019/osfstorage-archive/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now fetch the data\n",
    "\n",
    "from glob import glob\n",
    "import os\n",
    "data = sorted(glob(os.path.join(wdir,'*.gz')))\n",
    "confounds = sorted(glob(os.path.join(wdir,'*regressors.tsv')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many individual subjects do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#len(data.func)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract features\n",
    "\n",
    "![feat_xtrct](https://ars.els-cdn.com/content/image/1-s2.0-S1053811919301594-gr1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to do our machine learning, we will need to extract feature from our rs-fmri images.\n",
    "\n",
    "Specifically, we will extract signals from a brain parcellation and compute a correlation matrix, representing regional coactivation between regions.\n",
    "\n",
    "We will practice on one subject first, then we'll extract data for all subjects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve the atlas for extracting features and an example subject"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're using rs-fmri data, it makes sense to use an atlas defined using rs-fmri data\n",
    "\n",
    "This paper has many excellent insights about what kind of atlas to use for an rs-fmri machine learning task. See in particular Figure 5.\n",
    "\n",
    "https://www.sciencedirect.com/science/article/pii/S1053811919301594?via%3Dihub\n",
    "\n",
    "Let's use the MIST atlas, created here in Montreal using the BASC method. This atlas has multiple resolutions, for larger networks or finer-grained ROIs. Let's use a 64-ROI atlas to allow some detail, but to ultimately keep our connectivity matrices manageable\n",
    "\n",
    "Here is a link to the MIST paper: https://mniopenresearch.org/articles/1-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isabellesimard2/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atlas ROIs are located in nifti image (4D) at: /Users/isabellesimard2/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n"
     ]
    }
   ],
   "source": [
    "from nilearn import datasets\n",
    "\n",
    "parcellations = datasets.fetch_atlas_basc_multiscale_2015(version='sym')\n",
    "atlas_filename = parcellations.scale064\n",
    "\n",
    "\n",
    "print('Atlas ROIs are located in nifti image (4D) at: %s' %\n",
    "       atlas_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at that atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x1c24197e48>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn import plotting\n",
    "\n",
    "plotting.plot_roi(atlas_filename, draw_cross=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, let's load an example 4D fmri time-series for one subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/isabellesimard2/Documents/BrainHack_School_2019/osfstorage-archive/sub-pixar001_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz\n"
     ]
    }
   ],
   "source": [
    "fmri_filenames = data[0]\n",
    "print(fmri_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the image! Because it is a 4D image, we can only look at one slice at a time. Or, better yet, let's look at an average image!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x110827dd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EAOhHbTaFsHrPUlGz1z9Hlzjj3I5I1SBjxbKzkMjRK6yevIzafzX23deIntUiDD8fnHSLnyc2ozDDqF1ojFlrtBnOSM1jOlEA/xvAKYQ/B/ydYEXBsImEo6S6Gm1rX/HtYdMCm39Z2e18n/H3hZcoJy+LU8sE4wU+FpuleRw2i1i/U27Z0IGoiEHO12Hs68yBv//HULvhQaaxqUPgM9gK4XNtA+D55wPhMbfddhvefPNNLFy4EK1bt0bbtm2xbNky+Hw+XHHFFdi+fTsuuihgaM/Ly8OiRYtctRTRzJw5E2PHjsVLL72Ec845B6+//joAmMeKS5LybDSr0NfB1LaEjXJqexE2LFPVr+k47Yw+/GWQqLCxgNoSNtILKwKPnzPORcShzlOpXWiMmaiwwSGxqRA2LNu6hI3kw9d6KbV5kdTUwgY/27cEX2ch8ruMF2mWsMHzZ6zv305GH0vYcNr8uXyD2nc10ffnEfsnNy0pyvVh44AY7/u9hb5mlGbDccbj1eRN9H4htfkBY+dMS9vA2+M9vEDY0ac17A+lpfGw+vA4N1N7IrXZ8epFug6bJHgkHXb+/B5dU/5S5XvGzr780WPBl7UQ/Pzxl7yVB4O/wPnHpZzajlPyKQSe0dYu/S2nUD6vImrzKtXtWEDkjwife6XydQiR2cTTbHiUIjJK2BBCiOYGJ++6iSJQWJhzWwCxgPcxtVmIZViw5PE6Gtu9LJaYA0abYaGXj2VpllljVm6MaflDOFo+FtJ5EXoVCbzvQCnNTeJFo7iVX3JBwoYQQggh3HHybDSQtBc2WJVdEJREKfusaSdkcwNL0JZNkiVry5bIfRyrWy0i7wOPySsJ615ZUjnD82E1PI//K5LSZT9vGM5KswutcArpffbR4e18P9iuzSvHw0abFw7W88TPOsPjlAdfTwA4g/bn1aJlurMWL1buGM4HwivWIdT+Kz2X1mdSCJHGKM+GEEI0B8LiXLHRw00Y5UWF5fNj5XawnDatkiOWg7vTPgBbiLUisyxhm7EWh5ZfndtCkcfgc+WAgnfiZsFowSQpGkXChhBCCCHccfJsNJC0FDbYYep8F2edQmpvTnBsy3RimVpyjHYd9WX1Oa82LGndqohowfPhEDhWY3P0yiyXCAB5/8eGn7kLg8+cFS0ywNjOZoJjRpsd+fi54WfCWkR4CQnkOdfR/LzkNGB4bta5WPk32IzCob5cONIxecqcIkSaI82GEEJkJpG+aPtC7b5unREpjJYHX70skBjLP8cSYq08RNw+EXxlfznArvvDWGkIrBwaFpZJyC3/Efdlv6o+dA8SjcJp7iRJsSFhQwghhBDuJEmxkZ7CxsVkOuGiugtd+loVAi1pF0YfbkekxTXap4Kv0THtLE1bEQZenLYshy/rWKOoXRg0mfyKtilCJTYF9Mw515UTW1mrME54xZFCfP/aGdsPGH0sco3tbpkVWyPwjDpztZz0rGeR58MrQJ6zlRyMV7Wc14CrbzrJ+KrQLbStPa0uhRDpgTQbQgiRobAPVSFtZ3+XcmonsrJMNALFSiHO23lRx+M4PyAdYUegWPVQrMUh48Wk4mUx6baNQ8m9CPstlWat2RBCCCFE05OkNBvpI2ywBPs2td0cmawoAS+R0lYflo5ZJZxo9LUV4cJYKnbGilm36nJw/RfHyey3tG2SaqfEhFduzuqOzSJWFAbD94yfGx7bivKAsT3RAoFOn1qE6/cAkWY0K/LFel7ZLOLFHMOrYx6TTX3PBF//SqYTjtSxqoYKIRqXeiQlgWj6CBtCCNFSYIGPS8mzwGrVETng0tcyczBezBZWXRWer/XDY0WdsKDL+1o+bV6yLSeyyLR84RhO8BUdWdPSaXaaDSGEEEKkF83CZ4M90f9E6tTO5P6+h8RMR9JnFWuiaW6t7QxL8V6iOCzJ2nJu8hL5wlhqaevcnTE5imA6mU6ebcEVDquMkucT6Po4K03rWlurKivG3zL7Wc54lmnNqrvDOOP7jPcBb+mseZ4djbZVTdOK4PLRiU0MZkabyB22hZu30gfjhRb8vArR1CgaRQghMpSfU/tnI8PtD1eG24XUx813pyGrTS++OiwHsnDL5h1HqM2J6s+LKMuXyfJHYnKMPlaRS6tuiwMLy/wD+iNqs8+gaCaaDSGEEEKkLxmr2WA19hVkOrnuonCfPetij8GRF15U3Za5wUscuZWky7lwnaL6W5K4lzosluRupQrmufFqw9mXzU23gwmrop9tAVEqbnVPgMhrfxm1nYifz43xrMRWlvRvPR9ePLwTjYZy5pMd9X+iK1lr1WmZbxjr89aZH9ieLh2Whpt/fC7cPvB2+J595OE4QojkIc2GEEI0B0jq7UWbrYWOIwhavjGMl1B8S+i1zBM8L86kbGXKtSJNEv0B4/Gt83UrGugldLuQ2rw46UKLk5Yagq1oFCGEEEKkFD8yNs9GWCZ9gDeTCG6VgXfMAlbSIy+qYst04kW65/5+Oo6XNMCMF9W4ZSOzzD1ubT4ORw5wjYqNZDqhYIBmRR+XuicAcAe12RziXCtr5WiVdE/0+fPS3wtu49Qj4Lzn9iww1nPGY7I5ifuPoTZfB7aQWBVIQx9mnth71C4Nry7/dUD4/l26PdyFn2lV6hQiNUizIYQQGUpvYzur+cuozcKU88VvJdHysgq1fNq8CL3cn80oPB+3+UbjJWWAF9hM4pYpN8d4n4ViHmMELU7ebwE+bfGoQ4Y6iAohhBAiM8hYB1FWaQ9n0ZJ0tSzZutURsSI4Er0glkrbkvR51eF3eR+wk4BZKvmG3ETL2cnBy3XiKJUZDZhLusFRT11pRcJpid1KswPuZhS+ll5SQzNeTCeWKc5LhItbErB6AK2on5WG2oo0sZ57/mxwZEgxtQuNY+Va4WJuB/1PSt71eLg5jHJSsBnlDeOYQoiGITOKEEJkEGY0BX2T51Knw7Q9XuSJl4VTolih0AybUSy/GS+ZlL0swKz+1rEc4bmjy7ZoOAqIE3y9n7BRp/mRwQ6iQgghhMgEMlazwR7sGEBt0sm2IxHWTZp1i6WO7stYUlmi0QA8zsng6x7Y5gzWFMczeUTPwYukb5Wqd+Zp7VdO7b5Gn0zESt7FUSd8TazkcIXBV8spyjJPWVUsE3XA82JqYdyqf9YF+zvnyCu6di79o+HVYK2xnU2G7MzIzzpbSrvShfC5deaL9ldq05fGw/TA/opMr6Op+5/k1CdE0shYnw0hhGiJsFw1it8gibWGBDJei7m5u1iLKC/FKROFBUs3/6K9sKNjWLi1TDzc5v4sJHtJYsbX2O0H0hKceT/260o8Nqb5kbHpyoUQQgiRGWSsZiMivtwo9HGMxFw3SdgqE28l4LKwPO4tRyfeztEovN0a08v4XmRoHt8ax81kY60oiox2JsaXF5DphBOXbaY2ryh5BbXXaDtYKy+rXHuNsZ0feS8rUMtMY5Whd/atC77WRm3nbdHjM/w55fLxfG16Gtu3UJvPt5DauU4oiVVchttso3k13LyJ6im9RF3Opef1syQ8x8mqwfIFtS+ntu/v4baftp+iNs/8JGJzymjXRXcMUs9zMdq8byuXeW0C8JkxPo/D51cf3dFl/CxjO7etfZ1j8Y8cz4W3/4HaFAcV8V9LrcUjzYYQQqQ5dfRzmU0/2SyMtqFfSP4xZEHB7ceV+/IPtz+6owte+jD8o89zd8Y5FTUHtx/9aCwhxBJIrPPlucFlO7/fltosz56gtiWUtVQyVrMhhBCZhKVF8kIV7T2I9BNU4Ba9SKXop5TtrNBhzZGznbW3vPK0tGeWdsvSdFlaL57Ln4Ov/QAMoe1erpmXOXjx34jn18HKM9bSsWaYNXkbqT2JnM7bUZXylkS8aBQvVgSgkYQNTrAUoRCkJ6zmYLjND7NbbRTGUl0zVg0UK+KDzRD8AeXjO1J266jx+cG2kiF5wYuq26oh48AfQp6XpRLLxPhyjkAZRipPjjS5jtqW6eSASx/LPGbV5mGslQDvy3O0okW8rChyXNpZCHxJOHfRKnNv4fZZA+yqpIwVecU/nkOciVkHYvhAV4eb3yXbSeup7t0zxQQoRGOxatUq3Hnnnairq8PNN9+MmTNnxuxfj9h5NtJK2BBCiJYOy1Vcc24iyfUsFFoZXZ2FAwvDifp7Wb5GXvbl/o7JIXoxlWiItxcsbUa8/rwgeJHavOiiJLURUSrNjbq6OkyfPh3vvvsuCgoKUFxcjNGjR2PAgAHmPvE0G3kej2352QghhBCiGbFhwwb07dsXffr0QU5ODsaPH4/S0tKY+zg+G9afVxpFs8He4YX8BomWOYaLv1sNCMuumKitkuF92eTAqxGWprNd+sba1zquZcrhy2HFr8eztbLqy0rk5FUFlq50IdMJr0jY27+QTn4/PRjftsYOryh5DC7FbgRaRZBj9LHua1djO+Ps2yr41ylqezT8bFlRNny+/BngaBSGn3vrszogmLgh4vnjyXDhEz4o22LI1+EB2sz5wKoyMKpKiFSxZ88enH322aH/CwoKsH79+pj7KBpFCCHSHBZwviDBpzcLPiSFsZDKAiJ/2bMc5gYLcJa5JFHTieXz48iBA2D7w8HDdkvAtpw/a4w+fJ0cuZSdQodRm4V3HsMqptkc8PtPjw3y+XwuPcPUIwtVSTAuSdgQQgghWgAFBQX4+uuvQ//v3r0bPXtaOkqHVvDmJRObRhE2WCK9jN8g0bKGdNAchuSmsrakY0uit+biJSGTFSXg7LsNkWmFLfW5lxL2XmpWWKsTN2cyq46FJaNmYppevvacsKvQsIvwdbIc8A64bGN49cTPZzm1+Tnge8mJ0yzTDW/n6A+GrQnbqO08Q0cQ+HA77/HXCVsrvKwiGctEYtVDYdyu1XetE+cbyx+Mnu59cunCDqPYxcOkQYinERCiuVNcXIyysjJ8+eWX6NWrF5YuXYrFixfH2cuHZPwexBQ2lDlPmfOA9M6ct5Xav6X2C6eiewbg+3mc2ty9Nuo1er+TLn0BoNoYj+/N+9S2ng/e3gbu8NxZ+HGem2PBcTYF/6ePWsT9t9pWIiY+rzOo7eWriPd1ogLacjalHdSuoDZfQA7jYCGE9v2SNlNEvfn90QqR55Iq2lu+aySZcvZVK8eEI2OxkOnF98vyUUrUtMHbnSRZ/RAZyp3omBZeFpZWJuXi4CvLrbzQYqE7l95oHWFHSUY6q/QhOzsb8+bNQ0lJCerq6jBlyhQMHDgwzl5ZyBjNhhBCCCGanlGjRmHUqFHxO4ZoBM1GQzLnMTzNG6g9jVLO1awNt63MeU6bzRxWYisWTvn4Rz30sc6b+78ZfP0eos0P8bE0BZZ5w1J7WzJ3zzjvF1KbzVoLqH1LGmfO42twE7WLqX0dP9m0wyG6+LwydIu+OOyyDYhcfXK7M7XZrMDjsLOaBVsKeF+eb8SqzGWMjxDQJHwv+D8/C9ZKj/GSEI7HjKh55AFnZTqB1Hw5rM47j9p8w/nDRgm+sCTc9D8dbt9DXRZSu3mtV4VIJa0QWxo4HuO9MNJsCCFEI8AyU4Rw1tV9Oy9uWNh13NssIZbNGYmGuXsxc3hZFLHwzv1ZiLUWjZZfHY9jRb6woO70P2z0Zc6iidVGmFEyw3cttfgQ++mRsCGEEEKIBhFPs+EtWDhlwgYn0+lGjlHrqM80spFY5oF4KXm9FPPxgjUOw5fbSdE6GLYkbp2TFe1i1XCxpHvGbQ5WpAFL/+XUZpNAuimauQbKL8l59bd0U97lC9U33KzZHm7zWXF0gpupgPuyOe9jYz8241hmlDJjO8OmCh7/R9EdXXDu+SYEnDwdq4OVs8Eq5uVmvgQiV948N8tJr5DabmYpvh4D+f4ZybsiJsMmFbopvnw6PnmI8ue3OedSECK5NILPhhBCiOTAwllEkiwSoKxEU25f9VaWV8ZL5kcvGYqt7MxOhM8eRAquXkKqeeFkLTBzjD5esum6+cbxGBG+dnRQGU6iyaA8G0IIIYTIRNJcs8Ex5fvIpHLAqE/AWlO3ct8A4OTq4dP2EqnhxdTipSIiz4VLzHspJ85YrjY1Rh8vkrtbWW9rFWGZpigXEtLBjBJ53uEz/O1tlW6bcflK6k6rRT4Tfs4YtxLK5dTm9A4s47T48AEAABr0SURBVLNpg00MfG/4GWUTFt8z617yfbNMHtYzzV8RVg0Uy7nOekaPGm2eg3WO3Hbql0SsyK2kXnzBR1N7m9GWjUSIJCLNhhBCZAwR5gHDhsBf6eye0sloO1jue5YZhQU/y7zD8pslWPpdtgGRciOfN0fNWEIvYy3YeLs1jtPmc+XjR8yZdqxt+nVWmpHmmg0hhBBCZDrxolG80ejCBkvI/s/j92GNqCOhWs5QXo5pScpuanTAjjSxsLz+eRzL6emY0cca33KScvrweNbYLOmz5np92rlJkXL+DkoQTwmcIjJ8/Trc5HvLq8Wexnbn+aI8URErzpHUtlaFVvVOvg+Dje3Wvvzce3GQY6zFmjU3L32sz4w1f6Zn1OtpndnexZ3Y1sc3rZDalKnuMJnWeO4qPS+EV1rBW5aW2EizIYQQjQ1Li4ZExgJuObUdocmLtt+qk2IdnuU3KxlWtF+Qs80Stvk8OOFYoTEmby+ntuWfx7IoFy7s6LKNF4C9DB+hdyOc19K5MlRjkaGaDSGEEEJkChnks2FVO/SRsJRLAqTlpe9Izl5kLMtD34pAsTz9rQRLThmHXESqt72k4E006oRJpI9VVpyvL680klULJ1lUoluo/SzXS+ULaxXxoAvr9gwBthObU0ODrx+XLeLrZJkYrIRXltmFx7TMPnyqloOcc9qtEPiKcPaxVsFW2mcvnx/GqlBqmVqcBGh8PQ7RQXPIxJrLB+XwH14+8w0iz0a+fpUynQjxLVA0ihBCZCR7SJjqRU5AOeSr0tPQ4DsLBCvSxEtCLV7kuPkrRe/LsGDJ0SgsnFsCPvvgcbQLL4DYl8mafyJ+SiyT9qLsspzy9+/k28Nh7hJKgYzSbAghhBAiE8lQzQaHl/83ib+9TusZwE21b5k8LE95q/IhS/Es/Vrpe5k6Or6XSBMYfaxaJ5b62cItqZK1KrBi6bkEd3uqP5IORNQR4RvNmbQii7u47st1TcqpzasZ55kaRtssE5O12rKSwTFW5JIXs0u8pF6nEPiKcFv98nPB71tOfVb0F5WfMc10DK+gnSgoyyKWyytQ/sBYYTg8GTopvvZapQrxbZBmQwghMpJyavdiiYikthxqd2VJzQUvhSTdig0Cts+ZlUiMx3fEt42IjDy3BFprbgeMtgWPn1AOLiMM5w3arPiTaBSNIoQQQoiUkqFmFC5fMYbabEZhKZodihxFjuUY5aXCoVUvpNzob0XGsGNUop77Vr0Ly3RiRbhw2y0GnY/Dc+eK3WxWSD/Cs36Jtv5iPP1zB7U/ojZdfC4lv47aLKtzH0fFb0XzeElaZSVRs/a17qWV7MtK0eyM4w/+Of9bXxWWIx8fixfV5FPnKfGclQ/BGdM6v55UGr4zH4hzIPBF4MFpAiqTIkRDkRlFCCEyEs5O+2N+gwUoo+y502YBkhdFVk0VGH0YXvRZfl48xfrgaw3sApZWAjErOzML/ta+PAfe1y3LboQPEZ8grbSeg7DJQkZqNoQQQgiRKWSoZuMzStSUi32h9h63znD36md1qxdnHktCZ1mtkNpWCXbe15Hoj8F2nWGJ2ou5hLHU81bUTLxHgffzkX3lAOmZ061eBM/hC3puHtkVfm4m/C/3ffnabHPvYqZjduL849We8Yp1v61VHmOZBq1cZjxeLcKfDyupHD+7fJ2sSp1Mos8xm/UOR70C9kq3iJbhPu7EkUgcTkQXJ90S1QmRechBVAghMhL2XQMXpORqiCT9taZFQbuo12gswdXyFyqktmWesKJUsmmbFcbPZhEef0B0xyB8OVjYZrccXghYkS9ONHRnPihN8lG6pvvQJdROt7D/pscHFWITQgghRArJUM1GAZlOWAv6D6PtZjKx1NiWC4vVn6VgSyq3VN2tgq/tYKf45TG9RJpYyccs4pU0tyIc9tMFtlYX6UZ7em5mkblnJZlaOM0xr3y4WrllNrid2s5982Iuscx4XswWVsl4a0ze97DRxzluHeyvCMuUyNvZdMJz43NxixSLBe/rjGlFw/A943xdubRMzrFsW7QEbrilWYiWTob6bAghREvnCxKYn9oSFpjvHE2dKMzXxxlVg2HBLKxakRpWH8skwUKsF5+cvODr4KjtHO3C47sVDQTsjLvWginH2M4L2JAgfTVtpOxdkREoiXpitSB8PiBbZhQhhBBCpIrkKDZSJ2xYkQ1DqE/ni8LtXMq2FC9pkpdS2NZ2y7vf6s/qXLeY7+jUulYCLiYVql23Y1nnatXeSIcIFC/wPPn+sNnqBXL4su6ENY6zOrLKuFs1Zyzzh2Wq4JB/LxU8rSRgvK9j2jiBQHS8c6/ZLML3n/flPpajoJW22pqPZSZ0sK4N30s2d/E1O0yehL2pzQ6BSuolRANJTgJRaTaEEKIpeZHad7J0NIHapPN3vvdZOLTCuy3lt1X40ioHby2iHGE4uvgem2ksgdLL4tAKP7dMPJ1ZSnUOTJ1voAPtVgSKN9JdsyGEEEKIDCdTNRvswMO6a/YsryXpk9W5TpvVtJbA5SVhkqUG5v5W5UOnxPwxRHrL82rAyxwShSV6HtMtyZPldMVqcTYfZCKVZK5bTWec6ErlLRrHPYYjfNXOJ/OLZVLhe8+1aG6mdim1vdRMscxyvL08+HoCwBkIz9oKXLNMalbqaf7seYn+ihf5YpW1t+ZordS5ZApP/q9G/3RLYCdE2iLNhhBCZCYs4HxK6vxxT4eF5GV/oB3IZpITlNDOIkmtE61yuB4hmyGs0u1W8UirkCP3aRN87QVbKOXIFF6YWeHbPGcvESg8Tz9dB99lgdf9b4a3vU19ZTrxSLw0G/4Y7xEpEzas1QJnznuKKjtyngPLqSwRZ1HGSzVYxgoR47Zz4Toico5WGJk1vhc7pLWa4/Nq57LdSpfOx7TmmCmkYlXKOT3c2BThfGoRvssf0N1ZZ+SZ4WfLSwp+9yOF720rBJ5R54vYuv9eqhRb+VxYi2dVMmbiaTG8OFSzb4KVXZK1GV8YGgxpM4TwSLwEoie9DdMqfhchhBBCJEItAgKx81dn9Js6dSoGDx6MQYMGYcyYMaiurm7wsVetWoVzzz0Xffv2xezZsxt2LEezYf15RGYUIYRoQlidz5rfQ1RksDMXHPzz6WMcJjWTpSWzNFduWlHAW2n4LBrDi/+S5RdkYfmd8Tl2tn7wgvabaz0cJxU4v9E+AKcQ8KNy0+g98cQT6NChAwDgnnvuwbx58zBz5kxPx5g0aRImTZqE4cOHh7bV1dVh+vTpePfdd1FQUIDi4mKMHj0aAwYM+HbHylSfDX6Yt1A7hx6YWnoK3ZwgvVREtbLQJWra4CJCz5JKtkNQDbsLkR/KQuNYVrXWZOHmlGd9mFkVvcvo09JIRK2euK03PPZ6eobWU4+rqI8V7mcVnIJLn1YIfLntitoePaaVJp3t7Py8WJknrUJdfNxjLm3rO8zKQFlujM3PMReAFaKpyIpqW64Nzo+/3+/H8ePH4fP5AAAHDhzAbbfdhq+++goA8OSTT+LHP/5x3ONu2LABffv2RZ8+fQAA48ePR2lpKQYMGGAeK+6JJCEaRWYUIYQQIoXUIvbKfvLkyejevTt27NiBGTNmAADuvPNO3H333fj444/x5ptv4uabb44xQpg9e/bg7LPPDv1fUFCAPXv2xDxWTBzNhvXnEZlRhBCiCbHCcH9NfZ5g9Y6TkYvi1s/i4nOkFmKNDw9hhTBbGl7LHGMl9WKH4Z5wx3KmZ82YZSGptf6hRGj/Z2Hg9YMmDnM+hcAUY7k3vPLKK6irq8OMGTOwbNkyTJ48Ge+99x62bQvrFY8cOYKqqip89NFHuP/++wEAX331FT744APk5eWhTZs2WL9+Pfz+03UorMFwO1ZMMjXPBsPe+OX0NHNmPM4D4SZEeclCx1ipl/lDyfPaA3eOBC/dB+iAIlKrR3/oHDjsrJDaiebisM7XTU1uVbXlAkSZnmcj07C+7N6hL8RuRiVbvsfWF5fzTGcDqEQ2PkBAbfqBYXw8l47FFYA/Nsa3fhT2Gm3+PFimnFjbYvXhEE8+phWBIkSqqUH4e7ktAqaTEwj7bsQiKysL48aNw9y5czF58mTU19dj3bp1aNu2bUS/kpISlJSUAHD32SgoKMDXX38d+n/37t3o2TNS5Is+Vkwy1WdDCCFEGEsgepKEpodmhvt0vinY4BhfyhyXS5KX5R9m/XZYAq1Vx4c1G14S0HmpucMCKi+SCq3xLws3P14Ybjs+tY0pcOYgHCVaD+A4AkKH5a/g9/uxc+dO9O3bF36/HytWrED//v0BAFdccQXmzZuHe++9FwCwefNmDBkyxBgpTHFxMcrKyvDll1+iV69eWLp0KRYvXhzzWDGJl2fDIxI2hBBCiCRTg7Bmw8ER5kaNGoUXX3wR3bt3x8SJE3HkyBH4/X4MHjwYzz0X0D0//fTTmD59OgYNGoRTp05h6NCheP755+MeNzs7G/PmzUNJSQnq6uowZcoUDBw4EPX19eaxYhIvz4ZHfH43A0+QDl48VROEJeofGX24/hCrcx2B3bIfcl+W0NksQsVlTTjL3SZ0C7U52ROngeY5kJAd4cXPc+bxvaR8tpKc8XFZSdYp6hWINAeR8I9KFSNKa6qMBGLn0r3i5yCcjfIIsnAq7oKkKiJNu3u698g+TLh/N2M+/LzyXJw+nBjMMkG6HzHy81XZDE0nfN0r4aLZWEKdSbNRQ19ybF5irGgmL5WOed/rg69PomGaDSZRzUbuyHD7Y4od/h/G+E3NEfsnNy3x9SoCbt9ovn/B8iJs3Gi/7yDNhhBCpCEsNPEC5f855hPOvUHxvjn54XYhddlDGZst50y3RUs0LAA4PyCd4C31gJU3wzLrFFI7h1aNOXwCZE76qTGOaADy2RBCCCFESsnUaBRWd75jiEvvRMjUbESojXqNxn37ZwlWA90UoZKNXScj+qivRai93efzBcn0PH6kutrL2sBdEemsiHiEfTR2l4gaETKdpDPW/fnMMG04/b3WV/FicvDSZ59paok9Dj/zXyS4fOJr01xMJ0KkHdJsCCFE86XKWCD4dgUyQ/pXfhHuPIx2ZFsIxdz3ojVLDTnLWCaVXPqBqaF9eRHjBGX2i9qXQ/29FMtkrLwcEbHZlH66hCTr3c3Qd6fJUTSKEEIIIVJKKyQlGqXRhQ1v0mbTqvYbIhF7M0u4jx953IZL5fsMKb85eu63NNLtvn3b+aTbeQghopBmQwghmi8siNVGbA+YT/qTKWEH2y04dwDvSGaIHE6ZTG5xndkRkMbM4RAUWuVmVQRec7OBXIoW6UXjH6U5WCG3bGrpzMei/AjlFNZ6JXX5TIun1CKfDSGEEEKklEyNRhGNhyXlS/oXQgjhCWk2hBCi5cLmg3/aG15ArPtDuI/vNtqBMwpwbDRX+ztgtHlfNnP8a7i5+PNw+zoKKckl20nEAtlKNUvhMX8n08n3DHOJFk8pRpoNIYQQQqQUaTaEEEIIkVIUjSKEEC0XNh+sJxNDX9q+lIqEFo+mnTlihbN6bTG2c8YuihDZ/2bgdXFtVDIu/nHiwi4cHcM70HE3U/t/Uhc+3ypFoDQejWFGKauoQLfu3Rt+lATY98036NejR6MeUzQOqX6e9OwIIUSSaQwzSmMLGk11TNE4pPre6tkRQogkk4XMdRD1+/149NFH8cc//hGHDx/GqFGj8MILL6BDhw5NMR2RQZw8eRK333473njjDbRr1w733Xcf7rnnnqaelhBNCpsSviATww/JFnL+2+FaKle/Hd6XrRkcpHInmz+4nD1Fu+wJvj4XNc5KikyZQ2YXjnD585vh9q+oCxfCZBSB0nRkZTV8jFYNHyJxFi5ciFdffRUffvgh9u7di+PHj2PGjBlNMRWRYcyaNQtlZWXYtWsX3n//ffz+97/HqlWrmnpaQgjRLMlCIPLZ+vNKXGFj7ty5uPbaayO2zZgxA3fddVcCh4lkxYoVmDp1Ks4++2zk5eXh/vvvx7Jly3DsmNfC2CIT2blzJ7p06YJPPvkEALB3717k5+djzZo1nsdYuHAhHnzwQXTu3BnnnXcepk2bhvnz56dmwkII0cJpBeCMGH9eiWtGueGGGzBr1iwcPnwYnTp1wqlTp7Bs2TKsXLkSP/vZz7B48WLX/c455xxs3brV9T2/3w+/3x/x/8mTJ1FWVobBgwe77iMyn+985zuYM2cOrr/+evztb3/D5MmTMWnSJAwfPtzTs3To0CHs3bs34hkZPHgwli9f3linIETaYxV0JMsGW0JMHuQdbnfv4ywPtyAykIVZsPD0/rGQiSS9yEJkHrdvS1zNRo8ePTB06FC8/vrrAIBVq1YhPz8fF1xwAf7lX/4Fhw8fdv2zBA0AGDlyJF588UWUl5fjH//4B+bMmQMAGaXZaIUmskFlONOmTUO/fv1w4YUXoqKiAr/73e8AwNOzVF1dDQDo2DH86Hfs2BFVVfpyikbPpxAiGSRLs+Hp+2jixIlYtGgRAGDRokW48cYbPR/gP/7jP5CXl4e8vDwMHDgQADBlyhRMmDABw4cPx8CBAzFixAgAQEFBQQJTb1oSvdAizLRp0/Dpp59ixowZaNOmjef98vLyAABHjhwJbTty5Ajat3d3KGvJ6PkUQiSDRvPZAICf/OQn2Lp1Kz799FO88847uP766wEAt912W0iQiP5zBItLLrkE1dXVqK6uxt///vfAQVu1wiOPPILy8nLs3r0bAwcORK9evdCrV68Epi4ykerqatx1112YOnUqZs2ahcrKSgDenqXOnTujR48e2LIlrLDdsmVL6H0hhDeq0D7uH9Oa/ryMydTSn5dxRHrhQ3I0Gz4/O0/EYNq0aVi/fj3y8/Px7//+74nON4LKykocOnQIffr0wfbt2zF27FjccccduOWWWwAAHXy+Bo0v0pMjfj+mTp2KqqoqvPbaa7jllltw+PBhvPbaa57HmDlzJtatW4fly5dj3759GDFiBF555RVceeWVAPTsCOGFaIHADfadYOGAk4A6hu92sLN6WoJFrbG9uXPE209u2nBmURGu3LjRfP+zoiJsjPG+g2ez7sSJE/Ff//VfCZlQLA4ePIhRo0YhNzcXI0eOxJQpU0KChmi+lJaWYtWqVXj++UAO5ccffxyffPIJ/vSnP3ke45FHHsF3vvMd9O7dG8OGDcO9994bEjSEEEIkFx+ANjH+PI/jVbPx1VdfoX///vjmm29SnnxLq9PmSWNI9Hp2hEg+ltaCNRvCG5mm2ehWVITrY2gu1nrUbHjKIFpfX4/HH38c48ePV5ZPIYQQooXgRKM0lLjCxtGjR9GtWzf07t270TI1ZprkJ9IHPTsi2ez75psmrbujAoOiKXGiURpKXGEjNzc3lN9ACCFaGk1d4K+pjw8o0VZLJlmaDeX9EUKIFPP+++9jxIgR6NixIwoLC097f8SIEejatSs6dOiAwYMHo7S0tPEnKYQLTgZR688rEjaEECLF5ObmYsqUKZg7d67r+0899RQqKipw5MgRvPDCC7jhhhtQUVHRyLMU4nQaNYOoEEK0ZJYtWxaRaK5NmzYYPny45/1/+MMf4sYbb0SfPn1c3x80aBCyswNWbZ/Ph9raWnz99dfJmLoQDaJRM4gKIURLZty4caFMyHv37kWfPn0wYcIEzJ49G506dTL/EuGqq67CGWecgQsvvBDDhw9HUVFRis4meagGT/MnWRlEPYW+CiGECKQBuO666zB8+HDceuutAAJZbZPBO++8g9raWrz33nvYsWMHWrVK/59x1d9p/mQjOdEo6f80CyFEmvDLX/4SVVVVePrpp1MyfuvWrTFy5Ej85S9/wdtvv52SY4jGpQ5AFdzTs1dVVWHIkCGhv/z8fNx1110NPuaqVatw7rnnom/fvpg9e3Zo+9SpUzF48GAMGjQIY8aM8RRpmizNhoQNIYTwwNKlS7FkyRK88cYbaN06UPHj0UcfNQsIOlWKvw2nTp3Czp07kzV10UT4AZxEwO/Bjfbt22Pz5s2hv969e+OnP/2p5/EnTZqENWvWRGyrq6vD9OnTsXLlSmzbtg1LlizBtm3bAABPPPEEtmzZgq1bt+Kcc87BvHnz4h5D0ShCCNFIbNq0CTNmzMDy5cvRtWvX0PZf/OIXIV8Otz+H+vp6nDhxArW1tfD7/Thx4gRqamoAADt27MDKlStx/Phx1NbWYtGiRVi7di2GDRvW6OcpkkstAmYIL0UUysrKsH//flxyySUAgAMHDuDaa69FcXExiouL8eGHH3o65oYNG9C3b1/06dMHOTk5GD9+fCiU2skA7vf7cfz4cfg8lHdIVjSKfDaEECIOpaWlOHToEC6++OLQtksuuQQrV670tP/atWsxYsSI0P9t27bFsGHDsGbNGvj9fsyaNQvbtm1DVlYW+vXrh2XLluEHP/hB0s9DNB71AE4BaIuAKSUeS5Yswbhx40ICwJ133om7774bF198Mb766iuUlJRg+/btccfZs2cPzj777ND/BQUFWL9+fej/yZMn489//jMGDBiAP/zhD3HH65afj8tiOCvn5+fHHQNIoBCbEEKIpkEFBjOP4wByEDBDHEdgZd8adkmFAQMG4NVXX8UFF1wAADjrrLPQs2fP0PsHDhzAjh078NFHH+H+++8HECiQ2qVLl1A49vr16/H666/jL3/5C1588UUAwKuvvooNGzbgmWeeCY1VV1eHGTNmoLi4GJMnT07+ybsgzYYQQgiRBGoQdgT1IyBkOO1Y2o0tW7bg1KlTIUEDCJje1q1bh7Zt20b0LSkpQUlJCYCAz8akSZMicr4UFBRE5GjZvXt3hNACAFlZWRg3bhzmzp0rYUMIIUQAFRjMbCZNmoSrrroKY8aMcX1/yZIlmDBhQsS2K664AvPmzcO9994LANi8eTOGDBkS91jFxcUoKyvDl19+iV69emHp0qVYvHgx/H4/du7cib59+8Lv92PFihXo379/w0/OI3IQFUIIIRqRaKHhtddeO03YePrpp7Fx40YMGjQIAwYMwPPPP+9p7OzsbMybNw8lJSU477zzMHbsWAwcOBB+vx8TJ07E97//fXz/+99HRUUFHnrooaSdUzzksyGEEEKIlCLNhhBCCCFSioQNIYQQQqQUCRtCCCGESCkSNoQQQgiRUiRsCCGEECKlSNgQQgghREqRsCGEEEKIlCJhQwghhBApRcKGEEIIIVKKhA0hhBBCpBQJG0IIIYRIKf8fnHzio1CM8MYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 525.6x187.2 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn import image \n",
    "\n",
    "averaged_Img = image.mean_img(image.mean_img(fmri_filenames))\n",
    "plotting.plot_stat_map(averaged_Img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract signals on a parcellation defined by labels\n",
    "Using the NiftiLabelsMasker\n",
    "\n",
    "So we've loaded our atlas and 4D data for a single subject. Let's practice extracting features!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NiftiLabelsMasker.fit_transform] loading data from /Users/isabellesimard2/nilearn_data/basc_multiscale_2015/template_cambridge_basc_multiscale_nii_sym/template_cambridge_basc_multiscale_sym_scale064.nii.gz\n",
      "Resampling labels\n"
     ]
    }
   ],
   "source": [
    "from nilearn.input_data import NiftiLabelsMasker\n",
    "\n",
    "masker = NiftiLabelsMasker(labels_img=atlas_filename, standardize=True, \n",
    "                           memory='nilearn_cache', verbose=1)\n",
    "\n",
    "# Here we go from nifti files to the signal time series in a numpy\n",
    "# array. Note how we give confounds to be regressed out during signal\n",
    "# extraction\n",
    "conf = confounds[0]\n",
    "time_series = masker.fit_transform(fmri_filenames, confounds=conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what did we just create here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(time_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168, 64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series.shape\n",
    "#time_series.mean(0).shape\n",
    "#to compute mean activity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are these \"confounds\" and how are they used? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c241085c0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(time_series.T)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>csf</th>\n",
       "      <th>white_matter</th>\n",
       "      <th>global_signal</th>\n",
       "      <th>std_dvars</th>\n",
       "      <th>dvars</th>\n",
       "      <th>framewise_displacement</th>\n",
       "      <th>t_comp_cor_00</th>\n",
       "      <th>t_comp_cor_01</th>\n",
       "      <th>t_comp_cor_02</th>\n",
       "      <th>t_comp_cor_03</th>\n",
       "      <th>...</th>\n",
       "      <th>cosine00</th>\n",
       "      <th>cosine01</th>\n",
       "      <th>cosine02</th>\n",
       "      <th>cosine03</th>\n",
       "      <th>trans_x</th>\n",
       "      <th>trans_y</th>\n",
       "      <th>trans_z</th>\n",
       "      <th>rot_x</th>\n",
       "      <th>rot_y</th>\n",
       "      <th>rot_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>818.550141</td>\n",
       "      <td>875.069943</td>\n",
       "      <td>1262.020140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020679</td>\n",
       "      <td>-0.033857</td>\n",
       "      <td>0.095524</td>\n",
       "      <td>-0.022131</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109104</td>\n",
       "      <td>0.109090</td>\n",
       "      <td>0.109066</td>\n",
       "      <td>0.109033</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>-0.235811</td>\n",
       "      <td>0.033243</td>\n",
       "      <td>-0.000900</td>\n",
       "      <td>-9.976860e-24</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>815.643544</td>\n",
       "      <td>873.886244</td>\n",
       "      <td>1260.829698</td>\n",
       "      <td>1.320145</td>\n",
       "      <td>37.240650</td>\n",
       "      <td>0.124271</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>-0.045004</td>\n",
       "      <td>-0.032265</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109066</td>\n",
       "      <td>0.108937</td>\n",
       "      <td>0.108723</td>\n",
       "      <td>0.108423</td>\n",
       "      <td>0.030943</td>\n",
       "      <td>-0.238651</td>\n",
       "      <td>0.088048</td>\n",
       "      <td>-0.001747</td>\n",
       "      <td>-1.329770e-04</td>\n",
       "      <td>0.000154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>820.769909</td>\n",
       "      <td>872.997093</td>\n",
       "      <td>1262.678653</td>\n",
       "      <td>1.299640</td>\n",
       "      <td>36.662231</td>\n",
       "      <td>0.052546</td>\n",
       "      <td>-0.005027</td>\n",
       "      <td>-0.089152</td>\n",
       "      <td>-0.052859</td>\n",
       "      <td>-0.083313</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108990</td>\n",
       "      <td>0.108632</td>\n",
       "      <td>0.108038</td>\n",
       "      <td>0.107207</td>\n",
       "      <td>0.012759</td>\n",
       "      <td>-0.230284</td>\n",
       "      <td>0.097429</td>\n",
       "      <td>-0.001644</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.000250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>816.815767</td>\n",
       "      <td>872.913170</td>\n",
       "      <td>1261.592396</td>\n",
       "      <td>1.540568</td>\n",
       "      <td>43.458698</td>\n",
       "      <td>0.100117</td>\n",
       "      <td>0.053507</td>\n",
       "      <td>-0.081439</td>\n",
       "      <td>0.029216</td>\n",
       "      <td>-0.002027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108875</td>\n",
       "      <td>0.108176</td>\n",
       "      <td>0.107012</td>\n",
       "      <td>0.105391</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>-0.213517</td>\n",
       "      <td>0.143186</td>\n",
       "      <td>-0.001455</td>\n",
       "      <td>9.294130e-05</td>\n",
       "      <td>0.000665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>818.287589</td>\n",
       "      <td>872.019839</td>\n",
       "      <td>1249.983704</td>\n",
       "      <td>2.743071</td>\n",
       "      <td>77.380722</td>\n",
       "      <td>0.272004</td>\n",
       "      <td>0.025015</td>\n",
       "      <td>-0.061691</td>\n",
       "      <td>0.023856</td>\n",
       "      <td>-0.159531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108723</td>\n",
       "      <td>0.107567</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>0.102986</td>\n",
       "      <td>0.059444</td>\n",
       "      <td>-0.259451</td>\n",
       "      <td>0.083982</td>\n",
       "      <td>-0.000378</td>\n",
       "      <td>3.686370e-04</td>\n",
       "      <td>0.001661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          csf  white_matter  global_signal  std_dvars      dvars  \\\n",
       "0  818.550141    875.069943    1262.020140   0.000000   0.000000   \n",
       "1  815.643544    873.886244    1260.829698   1.320145  37.240650   \n",
       "2  820.769909    872.997093    1262.678653   1.299640  36.662231   \n",
       "3  816.815767    872.913170    1261.592396   1.540568  43.458698   \n",
       "4  818.287589    872.019839    1249.983704   2.743071  77.380722   \n",
       "\n",
       "   framewise_displacement  t_comp_cor_00  t_comp_cor_01  t_comp_cor_02  \\\n",
       "0                0.000000      -0.020679      -0.033857       0.095524   \n",
       "1                0.124271      -0.007043      -0.045004      -0.032265   \n",
       "2                0.052546      -0.005027      -0.089152      -0.052859   \n",
       "3                0.100117       0.053507      -0.081439       0.029216   \n",
       "4                0.272004       0.025015      -0.061691       0.023856   \n",
       "\n",
       "   t_comp_cor_03  ...  cosine00  cosine01  cosine02  cosine03   trans_x  \\\n",
       "0      -0.022131  ...  0.109104  0.109090  0.109066  0.109033  0.013422   \n",
       "1      -0.000439  ...  0.109066  0.108937  0.108723  0.108423  0.030943   \n",
       "2      -0.083313  ...  0.108990  0.108632  0.108038  0.107207  0.012759   \n",
       "3      -0.002027  ...  0.108875  0.108176  0.107012  0.105391  0.010014   \n",
       "4      -0.159531  ...  0.108723  0.107567  0.105651  0.102986  0.059444   \n",
       "\n",
       "    trans_y   trans_z     rot_x         rot_y     rot_z  \n",
       "0 -0.235811  0.033243 -0.000900 -9.976860e-24  0.000152  \n",
       "1 -0.238651  0.088048 -0.001747 -1.329770e-04  0.000154  \n",
       "2 -0.230284  0.097429 -0.001644 -0.000000e+00  0.000250  \n",
       "3 -0.213517  0.143186 -0.001455  9.294130e-05  0.000665  \n",
       "4 -0.259451  0.083982 -0.000378  3.686370e-04  0.001661  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "conf_df = pandas.read_table(conf)\n",
    "conf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168, 28)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute and display a correlation matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from nilearn.connectome import ConnectivityMeasure\n",
    "\n",
    "# correlation_measure = ConnectivityMeasure(kind='correlation')\n",
    "# correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n",
    "# correlation_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    " import numpy as np\n",
    "# # Mask the main diagonal for visualization:\n",
    "# np.fill_diagonal(correlation_matrix, 0)\n",
    "\n",
    "# # The labels we have start with the background (0), hence we skip the\n",
    "# # first label\n",
    "# plotting.plot_matrix(correlation_matrix, figure=(10, 8), \n",
    "#                      labels=range(time_series.shape[-1]),\n",
    "#                      vmax=0.8, vmin=-0.8, reorder=False)\n",
    "\n",
    "# # matrices are ordered for block-like representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract features from the whole dataset\n",
    "\n",
    "Here, we are going to use a for loop to iterate through each image and use the same techniques we learned above to extract rs-fmri connectivity features from every subject.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is a really simple for loop\n",
    "\n",
    "# for i in range(10):\n",
    "#     print('the number is', i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# container = []\n",
    "# for i in range(10):\n",
    "#     container.append(i)\n",
    "\n",
    "# container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets construct a more complicated loop to do what we want"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we do some things we don't need to do in the loop. Let's reload our atlas, and re-initiate our masker and correlation_measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.input_data import NiftiLabelsMasker\n",
    "#from nilearn.connectome import ConnectivityMeasure\n",
    "from nilearn import datasets\n",
    "\n",
    "# load atlas\n",
    "multiscale = datasets.fetch_atlas_basc_multiscale_2015()\n",
    "atlas_filename = multiscale.scale064\n",
    "\n",
    "# initialize masker (change verbosity)\n",
    "masker = NiftiLabelsMasker(labels_img=atlas_filename, standardize=True, \n",
    "                           memory='nilearn_cache', verbose=0)\n",
    "\n",
    "# initialize correlation measure, set to vectorize\n",
    "#correlation_measure = ConnectivityMeasure(kind='correlation', vectorize=True,\n",
    "                                      #   discard_diagonal=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay -- now that we have that taken care of, let's run our big loop!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: On a laptop, this might a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "all_features = [] # here is where we will put the data (a container)\n",
    "#So I could put the metrics in different containers or append them\n",
    "for i,sub in enumerate(data):\n",
    "    # extract the timeseries from the ROIs in the atlas\n",
    "    time_series = masker.fit_transform(sub, confounds=confounds[i])\n",
    "    flattened_TA = time_series.flatten ()\n",
    "    #time_series.reshape(-1)\n",
    "    #A = np.squeeze(np.asarray(time_series)[i])\n",
    "    #don't run the next line and replace \"correlation matrix\" with \"time_series\"\n",
    "    # create a region x region correlation matrix\n",
    "    #correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n",
    "    # add to our container\n",
    "    all_features.append(flattened_TA)\n",
    "    # keep track of status\n",
    "    print('finished %s of %s'%(i+1,len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's save the data to disk\n",
    "import numpy as np\n",
    "\n",
    "np.savez_compressed('MAIN_BASC064_subsamp_features_Neural_Activity',a = all_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you do not want to run the full loop on your computer, you can load the output of the loop here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_file = 'MAIN_BASC064_subsamp_features_Neural_Activity.npz'\n",
    "X_features = np.load(feat_file)['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(155, 10752)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.06764824, -0.09106132, -1.17865657, ...,  1.08802921,\n",
       "        3.20724999, -2.23727303])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_features.reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Imgs/SampFeat.png\" alt=\"terms\" width=\"300\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay so we've got our features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize our feature matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'subjects')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(X_features, aspect='auto')\n",
    "plt.colorbar()\n",
    "plt.title('feature matrix')\n",
    "plt.xlabel('features')\n",
    "plt.ylabel('subjects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Y (our target) and assess its distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's load the phenotype data\n",
    "\n",
    "pheno_path = os.path.join(wdir, 'participants.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>Child_Adult</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Handedness</th>\n",
       "      <th>ToM Booklet-Matched</th>\n",
       "      <th>ToM Booklet-Matched-NOFB</th>\n",
       "      <th>FB_Composite</th>\n",
       "      <th>FB_Group</th>\n",
       "      <th>WPPSI BD raw</th>\n",
       "      <th>WPPSI BD scaled</th>\n",
       "      <th>KBIT_raw</th>\n",
       "      <th>KBIT_standard</th>\n",
       "      <th>DCCS Summary</th>\n",
       "      <th>Scanlog: Scanner</th>\n",
       "      <th>Scanlog: Coil</th>\n",
       "      <th>Scanlog: Voxel slize</th>\n",
       "      <th>Scanlog: Slice Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sub-pixar001</td>\n",
       "      <td>4.774812</td>\n",
       "      <td>4yo</td>\n",
       "      <td>child</td>\n",
       "      <td>M</td>\n",
       "      <td>R</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>6.0</td>\n",
       "      <td>pass</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3T1</td>\n",
       "      <td>7-8yo 32ch</td>\n",
       "      <td>3mm iso</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sub-pixar002</td>\n",
       "      <td>4.856947</td>\n",
       "      <td>4yo</td>\n",
       "      <td>child</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>4.0</td>\n",
       "      <td>inc</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3T1</td>\n",
       "      <td>7-8yo 32ch</td>\n",
       "      <td>3mm iso</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sub-pixar003</td>\n",
       "      <td>4.153320</td>\n",
       "      <td>4yo</td>\n",
       "      <td>child</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>3.0</td>\n",
       "      <td>inc</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3T1</td>\n",
       "      <td>7-8yo 32ch</td>\n",
       "      <td>3mm iso</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sub-pixar004</td>\n",
       "      <td>4.473648</td>\n",
       "      <td>4yo</td>\n",
       "      <td>child</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>2.0</td>\n",
       "      <td>fail</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3T1</td>\n",
       "      <td>7-8yo 32ch</td>\n",
       "      <td>3mm iso</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sub-pixar005</td>\n",
       "      <td>4.837782</td>\n",
       "      <td>4yo</td>\n",
       "      <td>child</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>4.0</td>\n",
       "      <td>inc</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3T1</td>\n",
       "      <td>7-8yo 32ch</td>\n",
       "      <td>3mm iso</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participant_id       Age AgeGroup Child_Adult Gender Handedness  \\\n",
       "0   sub-pixar001  4.774812      4yo       child      M          R   \n",
       "1   sub-pixar002  4.856947      4yo       child      F          R   \n",
       "2   sub-pixar003  4.153320      4yo       child      F          R   \n",
       "3   sub-pixar004  4.473648      4yo       child      F          R   \n",
       "4   sub-pixar005  4.837782      4yo       child      F          R   \n",
       "\n",
       "   ToM Booklet-Matched  ToM Booklet-Matched-NOFB  FB_Composite FB_Group  \\\n",
       "0                 0.80                  0.736842           6.0     pass   \n",
       "1                 0.72                  0.736842           4.0      inc   \n",
       "2                 0.44                  0.421053           3.0      inc   \n",
       "3                 0.64                  0.736842           2.0     fail   \n",
       "4                 0.60                  0.578947           4.0      inc   \n",
       "\n",
       "   WPPSI BD raw  WPPSI BD scaled  KBIT_raw  KBIT_standard  DCCS Summary  \\\n",
       "0          22.0             13.0       NaN            NaN           3.0   \n",
       "1          18.0              9.0       NaN            NaN           2.0   \n",
       "2          15.0              9.0       NaN            NaN           3.0   \n",
       "3          17.0             10.0       NaN            NaN           3.0   \n",
       "4          13.0              5.0       NaN            NaN           2.0   \n",
       "\n",
       "  Scanlog: Scanner Scanlog: Coil Scanlog: Voxel slize Scanlog: Slice Gap  \n",
       "0              3T1    7-8yo 32ch              3mm iso                0.1  \n",
       "1              3T1    7-8yo 32ch              3mm iso                0.1  \n",
       "2              3T1    7-8yo 32ch              3mm iso                0.1  \n",
       "3              3T1    7-8yo 32ch              3mm iso                0.2  \n",
       "4              3T1    7-8yo 32ch              3mm iso                0.2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "pheno = pandas.read_csv(pheno_path, sep='\\t').sort_values('participant_id')\n",
    "pheno.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there is a column labeling age. Let's capture it in a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_age = pheno['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we should have a look at the distribution of our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c28954400>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.distplot(y_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for machine learning\n",
    "\n",
    "Here, we will define a \"training sample\" where we can play around with our models. We will also set aside a \"validation\" sample that we will not touch until the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to be sure that our training and test sample are matched! We can do that with a \"stratified split\". This dataset has a variable indicating AgeGroup. We can use that to make sure our training and testing sets are balanced!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8-12yo    34\n",
       "5yo       34\n",
       "Adult     33\n",
       "7yo       23\n",
       "3yo       17\n",
       "4yo       14\n",
       "Name: AgeGroup, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_class = pheno['AgeGroup']\n",
    "age_class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training: 93 testing: 62\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the sample to training/validation with a 60/40 ratio, and \n",
    "# stratify by age class, and also shuffle the data.\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "                                                    X_features, # x\n",
    "                                                    y_age, # y\n",
    "                                                    test_size = 0.4, # 60%/40% split  \n",
    "                                                    shuffle = True, # shuffle dataset\n",
    "                                                                    # before splitting\n",
    "                                                    stratify = age_class,  # keep\n",
    "                                                                           # distribution\n",
    "                                                                           # of ageclass\n",
    "                                                                           # consistent\n",
    "                                                                           # betw. train\n",
    "                                                                           # & test sets.\n",
    "                                                    random_state = 123 # same shuffle each\n",
    "                                                                       # time\n",
    "                                                                       )\n",
    "\n",
    "# print the size of our training and test groups\n",
    "print('training:', len(X_train),\n",
    "     'testing:', len(X_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the distributions to be sure they are matched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2a245a20>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_train)\n",
    "sns.distplot(y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run your first model!\n",
    "\n",
    "Machine learning can get pretty fancy pretty quickly. We'll start with a fairly standard regression model called a Support Vector Regressor (SVR). \n",
    "\n",
    "While this may seem unambitious, simple models can be very robust. And we probably don't have enough data to create more complex models (but we can try later).\n",
    "\n",
    "For more information, see this excellent resource:\n",
    "https://hal.inria.fr/hal-01824205"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit our first model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "    gamma='auto_deprecated', kernel='linear', max_iter=-1, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "l_svr = SVR(kernel='linear') # define the model\n",
    "\n",
    "l_svr.fit(X_train, y_train) # fit the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well... that was easy. Let's see how well the model learned the data!\n",
    "\n",
    "\n",
    "<img src=\"Imgs/modval.png\" alt=\"terms\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# predict the training data based on the model\n",
    "y_pred = l_svr.predict(X_train) \n",
    "\n",
    "# caluclate the model accuracy\n",
    "acc = l_svr.score(X_train, y_train) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view our results and plot them all at once!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (R2) 0.9998455320688827\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Predicted Age')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print results\n",
    "print('accuracy (R2)', acc)\n",
    "\n",
    "sns.regplot(y_pred,y_train)\n",
    "plt.xlabel('Predicted Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HOLY COW! Machine learning is amazing!!! Almost a perfect fit!\n",
    "\n",
    "...which means there's something wrong. What's the problem here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training: 69 testing: 24\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the sample to training/test with a 75/25 ratio, and \n",
    "# stratify by age class, and also shuffle the data.\n",
    "\n",
    "age_class2 = pheno.loc[y_train.index,'AgeGroup']\n",
    "\n",
    "X_train2, X_test, y_train2, y_test = train_test_split(\n",
    "                                                    X_train, # x\n",
    "                                                    y_train, # y\n",
    "                                                    test_size = 0.25, # 75%/25% split  \n",
    "                                                    shuffle = True, # shuffle dataset\n",
    "                                                                    # before splitting\n",
    "                                                    stratify = age_class2,  # keep\n",
    "                                                                           # distribution\n",
    "                                                                           # of ageclass\n",
    "                                                                           # consistent\n",
    "                                                                           # betw. train\n",
    "                                                                           # & test sets.\n",
    "                                                    random_state = 123 # same shuffle each\n",
    "                                                                       # time\n",
    "                                                                       )\n",
    "\n",
    "# print the size of our training and test groups\n",
    "print('training:', len(X_train2),\n",
    "     'testing:', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# fit model just to training data\n",
    "l_svr.fit(X_train2,y_train2)\n",
    "\n",
    "# predict the *test* data based on the model trained on X_train2\n",
    "y_pred = l_svr.predict(X_test) \n",
    "\n",
    "# caluclate the model accuracy\n",
    "acc = l_svr.score(X_test, y_test) \n",
    "mae = mean_absolute_error(y_true=y_test,y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (R2) =  0.44580628947967815\n",
      "MAE =  4.515106303785942\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Predicted Age')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print results\n",
    "print('accuracy (R2) = ', acc)\n",
    "print('MAE = ',mae)\n",
    "\n",
    "sns.regplot(y_pred,y_test)\n",
    "plt.xlabel('Predicted Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not perfect, but as predicting with unseen data goes, not too bad! Especially with a training sample of \"only\" 69 subjects. But we can do better! \n",
    "\n",
    "For example, we can increase the size our training set while simultaneously reducing bias by instead using 10-fold cross-validation\n",
    "\n",
    "<img src=\"Imgs/KCV.png\" alt=\"terms\" width=\"500\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict, cross_val_score\n",
    "\n",
    "# predict\n",
    "y_pred = cross_val_predict(l_svr, X_train, y_train, cv=10)\n",
    "# scores\n",
    "acc = cross_val_score(l_svr, X_train, y_train, cv=10)\n",
    "mae = cross_val_score(l_svr, X_train, y_train, cv=10, scoring='neg_mean_absolute_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the accuracy of the predictions for each fold of the cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 -- Acc = -1.4980116599328797, MAE = 3.0149747651265932\n",
      "Fold 1 -- Acc = 0.4263068462717937, MAE = 4.100179643385793\n",
      "Fold 2 -- Acc = 0.451570790446592, MAE = 5.5013797007581005\n",
      "Fold 3 -- Acc = 0.5208888282623957, MAE = 3.133312070769499\n",
      "Fold 4 -- Acc = 0.2002389449183437, MAE = 8.144423863579195\n",
      "Fold 5 -- Acc = 0.49017211078639084, MAE = 4.831217642181098\n",
      "Fold 6 -- Acc = 0.2657960619489612, MAE = 6.038070129620614\n",
      "Fold 7 -- Acc = 0.02179035070211066, MAE = 4.7606772006961675\n",
      "Fold 8 -- Acc = -2.9234217934944082, MAE = 3.054206830952476\n",
      "Fold 9 -- Acc = 0.5951195482214009, MAE = 3.656644444913953\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print('Fold {} -- Acc = {}, MAE = {}'.format(i, acc[i],-mae[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the overall accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.3779439687891867\n",
      "MAE: 4.610024846635719\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Predicted Age')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "overall_acc = r2_score(y_train, y_pred)\n",
    "overall_mae = mean_absolute_error(y_train,y_pred)\n",
    "print('R2:',overall_acc)\n",
    "print('MAE:',overall_mae)\n",
    "\n",
    "sns.regplot(y_pred, y_train)\n",
    "plt.xlabel('Predicted Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad at all! But more importantly, this is a more accurate estimation of our model's predictive efficacy. Our sample size is larger and this is based on several rounds of prediction of unseen data. \n",
    "\n",
    "For example, we can now see that the effect is being driven by the model's successful parsing of adults vs. children, but is not performing so well within the adult or children group. This was not evident during our previous iteration of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweak your model\n",
    "\n",
    "It's very important to learn when and where its appropriate to \"tweak\" your model.\n",
    "\n",
    "Since we have done all of the previous analysis in our training data, it's fine to try out different models. But we **absolutely cannot** \"test\" it on our left out data. If we do, we are in great danger of overfitting.\n",
    "\n",
    "It is not uncommon to try other models, or tweak hyperparameters. In this case, due to our relatively small sample size, we are probably not powered sufficiently to do so, and we would once again risk overfitting. However, for the sake of demonstration, we will do some tweaking. \n",
    "\n",
    "<img src=\"Imgs/KCV2.png\" alt=\"terms\" width=\"500\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try a few different examples:\n",
    "* Normalizing our target data\n",
    "* Tweaking our hyperparameters\n",
    "* Trying a more complicated model\n",
    "* Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalize the target data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Log-Transformed Age')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a log transformer function and log transform Y (age)\n",
    "\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "log_transformer = FunctionTransformer(func = np.log, validate=True)\n",
    "log_transformer.fit(y_train.values.reshape(-1,1))\n",
    "y_train_log = log_transformer.transform(y_train.values.reshape(-1,1))[:,0]\n",
    "\n",
    "sns.distplot(y_train_log)\n",
    "plt.title(\"Log-Transformed Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's go ahead and cross-validate our model once again with this new log-transformed target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.4008224272408131\n",
      "MAE: 0.3917998405840568\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict\n",
    "y_pred = cross_val_predict(l_svr, X_train, y_train_log, cv=10)\n",
    "\n",
    "# scores\n",
    "acc = r2_score(y_train_log, y_pred)\n",
    "mae = mean_absolute_error(y_train_log,y_pred)\n",
    "\n",
    "print('R2:',acc)\n",
    "print('MAE:',mae)\n",
    "\n",
    "sns.regplot(y_pred, y_train_log)\n",
    "plt.xlabel('Predicted Log Age')\n",
    "plt.ylabel('Log Age')\n",
    "plt.savefig('Timeseries_Train_Model_Log_Age.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like a definite improvement, right? I think we can agree on that. \n",
    "\n",
    "But we can't forget about interpretability? The MAE is much less interpretable now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tweak the hyperparameters\n",
    "\n",
    "Many machine learning algorithms have hyperparameters that can be \"tuned\" to optimize model fitting. Careful parameter tuning can really improve a model, but haphazard tuning will often lead to overfitting.\n",
    "\n",
    "Our SVR model has multiple hyperparameters. Let's explore some approaches for tuning them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVR?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way is to plot a \"Validation Curve\" -- this will let us view changes in training and validation accuracy of a model as we shift its hyperparameters. We can do this easily with sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "C_range = 10. ** np.arange(-3, 8) # A range of different values for C\n",
    "\n",
    "train_scores, valid_scores = validation_curve(l_svr, X_train, y_train_log, \n",
    "                                              param_name= \"C\",\n",
    "                                              param_range = C_range,\n",
    "                                              cv=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>Fold</th>\n",
       "      <th>Score</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.978755</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.977285</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.976755</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.978485</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.976786</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C  Fold     Score   Type\n",
       "0  0     0  0.978755  Train\n",
       "1  0     1  0.977285  Train\n",
       "2  0     2  0.976755  Train\n",
       "3  0     3  0.978485  Train\n",
       "4  0     4  0.976786  Train"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A bit of pandas magic to prepare the data for a seaborn plot\n",
    "\n",
    "tScores = pandas.DataFrame(train_scores).stack().reset_index()\n",
    "tScores.columns = ['C','Fold','Score']\n",
    "tScores.loc[:,'Type'] = ['Train' for x in range(len(tScores))]\n",
    "\n",
    "vScores = pandas.DataFrame(valid_scores).stack().reset_index()\n",
    "vScores.columns = ['C','Fold','Score']\n",
    "vScores.loc[:,'Type'] = ['Validate' for x in range(len(vScores))]\n",
    "\n",
    "ValCurves = pandas.concat([tScores,vScores]).reset_index(drop=True)\n",
    "ValCurves.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0.001'),\n",
       " Text(0, 0, '0.01'),\n",
       " Text(0, 0, '0.1'),\n",
       " Text(0, 0, '1.0'),\n",
       " Text(0, 0, '10.0'),\n",
       " Text(0, 0, '100.0'),\n",
       " Text(0, 0, '1000.0'),\n",
       " Text(0, 0, '10000.0'),\n",
       " Text(0, 0, '100000.0'),\n",
       " Text(0, 0, '1000000.0')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And plot!\n",
    "g = sns.lineplot(x='C',y='Score',hue='Type',data=ValCurves)\n",
    "g.set_xticks(range(10))\n",
    "g.set_xticklabels(C_range, rotation=90)\n",
    "\n",
    "# g = sns.factorplot(x='C',y='Score',hue='Type',data=ValCurves)\n",
    "# plt.xticks(range(10))\n",
    "# g.set_xticklabels(C_range, rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like accuracy is better for higher values of C, and plateaus somewhere between 0.1 and 1. The default setting is C=1, so it looks like we can't really improve by changing C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But our SVR model actually has two hyperparameters, C and epsilon. Perhaps there is an optimal combination of settings for these two parameters.\n",
    "\n",
    "We can explore that somewhat quickly with a grid search, which is once again easily achieved with sklearn. Because we are fitting the model multiple times witih cross-validation, this will take some time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/isabellesimard2/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                           epsilon=0.1, gamma='auto_deprecated',\n",
       "                           kernel='linear', max_iter=-1, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,\n",
       "       1.e+05, 1.e+06, 1.e+07]),\n",
       "                         'epsilon': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,\n",
       "       1.e+05, 1.e+06, 1.e+07])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "C_range = 10. ** np.arange(-3, 8)\n",
    "epsilon_range = 10. ** np.arange(-3, 8)\n",
    "\n",
    "param_grid = dict(epsilon=epsilon_range, C=C_range)\n",
    "\n",
    "grid = GridSearchCV(l_svr, param_grid=param_grid, cv=10)\n",
    "\n",
    "grid.fit(X_train, y_train_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the grid search has completed, let's find out what was the \"best\" parameter combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 0.001, 'epsilon': 0.001}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if redo our cross-validation with this parameter set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.4008224272408131\n",
      "MAE: 0.3917998405840568\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Age')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = cross_val_predict(SVR(kernel='linear',C=0.10,epsilon=0.10, gamma='auto'), \n",
    "                           X_train, y_train_log, cv=10)\n",
    "\n",
    "# scores\n",
    "acc = r2_score(y_train_log, y_pred)\n",
    "mae = mean_absolute_error(y_train_log,y_pred)\n",
    "\n",
    "print('R2:',acc)\n",
    "print('MAE:',mae)\n",
    "\n",
    "sns.regplot(y_pred, y_train_log)\n",
    "plt.xlabel('Predicted Log Age')\n",
    "plt.ylabel('Log Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps unsurprisingly, the model fit is actually exactly the same as what we had with our defaults. There's a reason they are defaults ;-)\n",
    "\n",
    "Grid search can be a powerful and useful tool. But can you think of a way that, if not properly utilized, it could lead to overfitting?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find a nice set of tutorials with links to very helpful content regarding how to tune hyperparameters and while be aware of over- and under-fitting here:\n",
    "\n",
    "https://scikit-learn.org/stable/modules/learning_curve.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trying a more complicated model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In principle, there is no real reason to do this. Perhaps one could make an argument for quadratic relationship with age, but we probably don't have enough subjects to learn a complicated non-linear model. But for the sake of demonstration, we can give it a shot.\n",
    "\n",
    "We'll use a validation curve to see the result of our model if, instead of fitting a linear model, we instead try to the fit a 2nd, 3rd, ... 8th order polynomial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_curve?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "degree_range = list(range(1,8)) # A range of different values for C\n",
    "\n",
    "#Every range is a different type of polynomial\n",
    "\n",
    "train_scores, valid_scores = validation_curve(SVR(kernel='poly',\n",
    "                                                  gamma='scale'\n",
    "                                                 ), \n",
    "                                              X=X_train, y=y_train_log, \n",
    "                                              param_name= \"degree\",\n",
    "                                              param_range = degree_range,\n",
    "                                              cv=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Degree</th>\n",
       "      <th>Fold</th>\n",
       "      <th>Score</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.972564</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.970426</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.967232</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.970897</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.975922</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Degree  Fold     Score   Type\n",
       "0       0     0  0.972564  Train\n",
       "1       0     1  0.970426  Train\n",
       "2       0     2  0.967232  Train\n",
       "3       0     3  0.970897  Train\n",
       "4       0     4  0.975922  Train"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A bit of pandas magic to prepare the data for a seaborn plot\n",
    "\n",
    "tScores = pandas.DataFrame(train_scores).stack().reset_index()\n",
    "tScores.columns = ['Degree','Fold','Score']\n",
    "tScores.loc[:,'Type'] = ['Train' for x in range(len(tScores))]\n",
    "\n",
    "vScores = pandas.DataFrame(valid_scores).stack().reset_index()\n",
    "vScores.columns = ['Degree','Fold','Score']\n",
    "vScores.loc[:,'Type'] = ['Validate' for x in range(len(vScores))]\n",
    "\n",
    "ValCurves = pandas.concat([tScores,vScores]).reset_index(drop=True)\n",
    "ValCurves.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1'),\n",
       " Text(0, 0, '2'),\n",
       " Text(0, 0, '3'),\n",
       " Text(0, 0, '4'),\n",
       " Text(0, 0, '5'),\n",
       " Text(0, 0, '6'),\n",
       " Text(0, 0, '7')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And plot!\n",
    "\n",
    "g = sns.lineplot(x='Degree',y='Score',hue='Type',data=ValCurves)\n",
    "g.set_xticks(range(10))\n",
    "g.set_xticklabels(degree_range, rotation=90)\n",
    "\n",
    "# g = sns.factorplot(x='Degree',y='Score',hue='Type',data=ValCurves)\n",
    "# plt.xticks(range(10))\n",
    "# g.set_xticklabels(degree_range, rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that we cannot improve our model by increasing the complexity of the fit. If one looked only at the training data, one might surmise that a 2nd order fit could be a slightly better model. But that improvement does not generalize to the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = cross_val_predict(SVR(kernel='rbf', gamma='scale'), X_train, y_train_log, cv=10)\n",
    "\n",
    "# # scores\n",
    "# acc = r2_score(y_train_log, y_pred)\n",
    "# mae = mean_absolute_error(y_train_log,y_pred)\n",
    "\n",
    "# print('R2:',acc)\n",
    "# print('MAE:',mae)\n",
    "\n",
    "# sns.regplot(y_pred, y_train_log)\n",
    "# plt.xlabel('Predicted Log Age')\n",
    "# plt.ylabel('Log Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature selection\n",
    "\n",
    "Right now, we have 2016 features. Are all of those really going to contribute to the model stably? \n",
    "\n",
    "Intuitively, models tend to perform better when there are fewer, more important features than when there are many, less imortant features. The tough part is figuring out which features are useful or important.\n",
    "\n",
    "Here will quickly try a basic feature seclection strategy\n",
    "\n",
    "<img src=\"Imgs/FeatSel.png\" alt=\"terms\" width=\"400\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SelectPercentile() function will select the top X% of features based on univariate tests. This is a way of identifying theoretically more useful features. But remember, significance != prediction! \n",
    "\n",
    "We are also in danger of overfitting here. For starters, if we want to test this with 10-fold cross-validation, we will need to do a separate feature selection within each fold! That means we'll need to do the cross-validation manually instead of using cross_val_predict(). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Reduction: PCA\n",
    "Establishing the number of components to use in the model by plotting the results of the PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "#Fitting the PCA algorithm with our Data\n",
    "pca = PCA().fit(X_features)\n",
    "#Plotting the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "ax = plt.figure().add_subplot(1, 1, 1)\n",
    "#plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.plot(pca.explained_variance_ratio_)\n",
    "# Major ticks every 20, minor ticks every 5\n",
    "major_ticks = np.arange(0, 161, 20)\n",
    "minor_ticks = np.arange(0, 161, 5)\n",
    "\n",
    "\n",
    "ax.set_xticks(major_ticks)\n",
    "ax.set_xticks(minor_ticks, minor=True)\n",
    "\n",
    "ax.grid(which='both')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Variance (%)') #for each component\n",
    "plt.title('Explained Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it looks like there are 154 components that explain all the variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets run the PCA analysis on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pca = PCA(n_components=154)\n",
    "#dataset = pca.fit_transform(X_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.feature_selection import SelectPercentile, f_regression\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "#pca = PCA(n_components=5)\n",
    "#dataset = pca.fit_transform(data_rescaled)\n",
    "# Build a tiny pipeline that does feature selection (top 20% of features), \n",
    "# and then prediction with our linear svr model.\n",
    "model = Pipeline([\n",
    "    ('feature_selection', PCA(random_state=123)),\n",
    "    ('prediction', l_svr)\n",
    "                 ])\n",
    "\n",
    "    \n",
    "y_pred = [] # a container to catch the predictions from each fold\n",
    "y_index = [] # just in case, the index for each prediciton\n",
    "\n",
    "# First we create 10 splits of the data\n",
    "skf = KFold(n_splits=10, shuffle=True, random_state=123)\n",
    "\n",
    "# For each split, assemble the train and test samples \n",
    "for tr_ind, te_ind in skf.split(X_train):\n",
    "    X_tr = X_train[tr_ind]\n",
    "    y_tr = y_train_log[tr_ind]\n",
    "    X_te = X_train[te_ind]\n",
    "    y_index += list(te_ind) # store the index of samples to predict\n",
    "    \n",
    "    # and run our pipeline \n",
    "    model.fit(X_tr, y_tr) # fit the data to the model using our mini pipeline\n",
    "    predictions = model.predict(X_te).tolist() # get the predictions for this fold\n",
    "    y_pred += predictions # add them to the list of predictions\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alrighty, let's see if only using the PCA dimension reduces model improves the model at all..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.4001441433269275\n",
      "MAE: 0.390438768368376\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = r2_score(y_train_log[y_index], y_pred)\n",
    "mae = mean_absolute_error(y_train_log[y_index],y_pred)\n",
    "\n",
    "print('R2:',acc)\n",
    "print('MAE:',mae)\n",
    "\n",
    "sns.regplot(np.array(y_pred), y_train_log[y_index])\n",
    "plt.xlabel('Predicted Log Age')\n",
    "plt.ylabel('Log Age')\n",
    "\n",
    "plt.savefig('Timeseries_Train_Model_Log_Age_PCA.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nope, in fact it got a bit worse. It seems we're getting \"value at the margins\" so to speak. This is a very good example of how significance != prediction, as demonstrated in this figure from Bzdok et al., 2018 *bioRxiv*\n",
    "\n",
    "![Bzdok2018](https://www.biorxiv.org/content/biorxiv/early/2018/05/21/327437/F1.large.jpg?width=800&height=600&carousel=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See here for an explanation of different feature selection options and how to implement them in sklearn: https://scikit-learn.org/stable/modules/feature_selection.html\n",
    "\n",
    "And here is a thoughtful tutorial covering feature selection for novel machine learners: https://www.datacamp.com/community/tutorials/feature-selection-python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there you have it. We've tried many different strategies, but most of our \"tweaks\" haven't really lead to improvements in the model. This is not always the case, but it is not uncommon. Can you think of some reasons why?\n",
    "\n",
    "Moving on to our validation data, we probably should just stick to a basic model, though predicting log age might be a good idea!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can our model predict age in completely un-seen data?\n",
    "Now that we've fit a model we think has possibly learned how to decode age based on rs-fmri signal, let's put it to the test. We will train our model on all of the training data, and try to predict the age of the subjects we left out at the beginning of this section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we performed a log transformation on our training data, we will need to transform our testing data using the *same information!*  But that's easy because we stored our transformation in an object!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notice how we use the Scaler that was fit to X_train and apply to X_test,\n",
    "# rather than creating a new Scaler for X_test\n",
    "y_val_log = log_transformer.transform(y_val.values.reshape(-1,1))[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now for the moment of truth! \n",
    "\n",
    "No cross-validation needed here. We simply fit the model with the training data and use it to predict the testing data\n",
    "\n",
    "I'm so nervous. Let's just do it all in one cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_svr.fit(X_train, y_train_log) # fit to training data\n",
    "y_pred = l_svr.predict(X_val) # classify age class using testing data\n",
    "acc = l_svr.score(X_val, y_val_log) # get accuracy (r2)\n",
    "mae = mean_absolute_error(y_val_log, y_pred) # get mae\n",
    "\n",
    "# print results\n",
    "print('accuracy (r2) =', acc)\n",
    "print('mae = ',mae)\n",
    "\n",
    "# plot results\n",
    "sns.regplot(y_pred, y_val_log)\n",
    "plt.xlabel('Predicted Log Age')\n",
    "plt.ylabel('Log Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Wow!!*** Congratulations. You just trained a machine learning model that used real rs-fmri data to predict the age of real humans.\n",
    "\n",
    "The proper thing to do at this point would be to repeat the train-validation split multiple times. This will ensure the results are not specific to this validation set, and will give you some confidence intervals around your results.\n",
    "\n",
    "As an assignment, you can give that a try below. Create 10 different splits of the entire dataset, fit the model and get your predictions. Then, plot the range of predictions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPACE FOR YOUR ASSIGNMENT\n",
    "from sklearn.model_selection import cross_val_predict, cross_val_score\n",
    "\n",
    "l_svr.fit(X_train, y_train_log) # fit to training data\n",
    "y_pred = cross_val_predict(l_svr, X_val, y_val_log, cv=10) # classify age class using testing data\n",
    "acc = l_svr.score(X_val, y_val_log) # get accuracy (r2)\n",
    "mae = mean_absolute_error(y_val_log, y_pred) # get mae\n",
    "\n",
    "\n",
    "# print results\n",
    "print('accuracy (r2) =', acc)\n",
    "print('mae = ',mae)\n",
    "\n",
    "# plot results\n",
    "sns.regplot(y_pred, y_val_log)\n",
    "plt.xlabel('Predicted Log Age')\n",
    "plt.ylabel('Log Age')\n",
    "\n",
    "#for i in range(10):\n",
    "  #  print('fold {} -- Acc = {}, MAE = {}'.format(i, acc[i],-mae[i]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it seems like something in this data does seem to be systematically related to age ... but what?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpreting model feature importances\n",
    "Interpreting the feature importances of a machine learning model is a real can of worms. This is an area of active research. Unfortunately, it's hard to trust the feature importance of some models. \n",
    "\n",
    "You can find a whole tutorial on this subject here:\n",
    "http://gael-varoquaux.info/interpreting_ml_tuto/index.html\n",
    "\n",
    "For now, we'll just eschew better judgement and take a look at our feature importances. While we can't ascribe any biological relevance to the features, it can still be helpful to know what the model is using to make its predictions. This is a good way to, for example, establish whether your model is actually learning based on a confound! Could you think of some examples?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the feature importances (weights) used my the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_svr.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets plot these weights to see their distribution better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar(range(l_svr.coef_.shape[-1]),l_svr.coef_[0])\n",
    "plt.title('feature importances')\n",
    "plt.xlabel('feature')\n",
    "plt.ylabel('weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or perhaps it will be easier to visualize this information as a matrix similar to the one we started with\n",
    "\n",
    "We can use the correlation measure from before to perform an inverse transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_svr.coef.reshape(X_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_features.inverse_transform(l_svr.coef_).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn import plotting\n",
    "\n",
    "feat_exp_matrix = correlation_measure.inverse_transform(l_svr.coef_)[0]\n",
    "\n",
    "plotting.plot_matrix(feat_exp_matrix, figure=(10, 8),  \n",
    "                     labels=range(feat_exp_matrix.shape[0]),\n",
    "                     reorder=False,\n",
    "                    tri='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can throw those features onto an actual brain.\n",
    "\n",
    "First, we'll need to gather the coordinates of each ROI of our atlas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = plotting.find_parcellation_cut_coords(atlas_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can use our feature matrix and the wonders of nilearn to create a connectome map where each node is an ROI, and each connection is weighted by the importance of the feature to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting.plot_connectome(feat_exp_matrix, coords, colorbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoa!! That's...a lot to process. Maybe let's threshold the edges so that only the most important connections are visualized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting.plot_connectome(feat_exp_matrix, coords, colorbar=True, edge_threshold=0.035)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's definitely an improvement, but it's still a bit hard to see what's going on.\n",
    "Nilearn has a new feature that let's use view this data interactively!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotting.view_connectome(feat_exp_matrix, coords, threshold='98%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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