srm_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import scipy.io\n",
    "from scipy.stats import stats\n",
    "from sklearn.cross_validation import LeaveOneLabelOut\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.svm import NuSVC\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "run download-data.sh before executing the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Training Data (raider movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "movie_file = scipy.io.loadmat('data/movie_data.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_data_left = movie_file['movie_data_lh']\n",
    "movie_data_right = movie_file['movie_data_rh']\n",
    "subjects = movie_data_left.shape[2]\n",
    "movie_data = []\n",
    "for s in range(subjects):\n",
    "    movie_data.append(np.concatenate([movie_data_left[:,:,s], movie_data_right[:,:,s]], axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Testing Data (raider image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_file = scipy.io.loadmat('data/image_data.mat')\n",
    "image_data_left = image_file['image_data_lh']\n",
    "image_data_right = image_file['image_data_rh']\n",
    "image_data = []\n",
    "for s in range(subjects):\n",
    "    image_data.append(np.concatenate([image_data_left[:,:,s], image_data_right[:,:,s]], axis=0))\n",
    "    \n",
    "labels = scipy.io.loadmat('data/label.mat')\n",
    "labels = np.squeeze(labels['label'])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the data if not already processed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for subject in range(subjects):\n",
    "    movie_data[subject] = stats.zscore(movie_data[subject],axis=1,ddof=1)\n",
    "for subject in range(subjects):\n",
    "    image_data[subject] = stats.zscore(image_data[subject],axis=1,ddof=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit model to Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import brainiak.funcalign.srm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SRM(features=50, n_iter=10, rand_seed=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srm = brainiak.funcalign.srm.SRM(n_iter=10, features=50)\n",
    "srm.fit(movie_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def prediction_accuracy_evaluation_loo_cv(data, label):\n",
    "    alldata = data[0]\n",
    "    alllabel = label\n",
    "    session_label = 0*np.ones(len(label))\n",
    "    for s in range(1,len(data)):\n",
    "        alldata = np.append(alldata, data[s], axis = 1)\n",
    "        alllabel = np.append(alllabel, label, axis = 0)\n",
    "        session_label = np.append(session_label, s*np.ones(len(label)), axis = 0)\n",
    "    classifier = NuSVC(nu=0.5, kernel='linear')\n",
    "    cv = LeaveOneLabelOut(session_label)\n",
    "    cv_score = cross_val_score(classifier, alldata.T, alllabel, cv=cv)\n",
    "    print(cv_score)\n",
    "    print(\"The average accuracy among all subjects is {0:f} +/- {1:f}\".format(np.mean(cv_score), np.std(cv_score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.73214286  0.64285714  0.625       0.67857143  0.76785714  0.625\n",
      "  0.66071429  0.60714286  0.55357143  0.67857143]\n",
      "The average accuracy among all subjects is 0.657143 +/- 0.058576\n"
     ]
    }
   ],
   "source": [
    "image_data_shared = srm.transform(image_data)\n",
    "for subject in range(subjects):\n",
    "    image_data_shared[subject] = stats.zscore(image_data_shared[subject], axis=1, ddof=1)\n",
    "prediction_accuracy_evaluation_loo_cv(image_data_shared, labels)"
   ]
  }
 ],
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