Ext. Gibbs vs MH.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SERVER_NAME: localhost:5000\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(7,5)\n",
    "# Add the application folder to the path\n",
    "import sys\n",
    "sys.path.insert(0,'../Peergrade/peergrade/')\n",
    "import time\n",
    "import math\n",
    "from application.model import data_model\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "from bson.objectid import ObjectId\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.stats.stats import pearsonr\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig_store = \"../MasterThesis/Thesis/figures/Analysis/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Grader_(object):\n",
    "    def __init__(self, id):\n",
    "        self.id = id\n",
    "        self.handins = list()\n",
    "        \n",
    "    def add_handin(self, handin):\n",
    "        self.handins.append(handin)\n",
    "                \n",
    "class Question_(object):\n",
    "    def __init__(self,id):\n",
    "        self.id = id\n",
    "                \n",
    "class Answers_(object):\n",
    "    def __init__(self,handin,question):\n",
    "        self.handin = handin\n",
    "        self.question = question\n",
    "        self.answers = dict()\n",
    "    \n",
    "class Handin_(object):\n",
    "    def __init__(self,id,owner):\n",
    "        self.id = id\n",
    "        self.owner = owner\n",
    "        self.gradeings = dict()\n",
    "        self.graders = list()\n",
    "        self.catched_score = dict()\n",
    "            \n",
    "    def add_grader(self,grader):\n",
    "        self.graders.append(grader)\n",
    "        \n",
    "    def add_answer(self,grader,question,value):\n",
    "        if str(question.id) not in self.gradeings:\n",
    "            self.gradeings[str(question.id)] = Answers_(self,question)\n",
    "        self.gradeings[str(question.id)].answers[str(grader.id)] = value\n",
    "        \n",
    "    def get_graders_answers(self):\n",
    "        graders_answers = defaultdict(list)\n",
    "        for q, answer in self.gradeings.iteritems():\n",
    "            for g, answer in answer.answers.iteritems():\n",
    "                graders_answers[str(g)].append((str(q),answer))            \n",
    "        return graders_answers\n",
    "        \n",
    "    def get_grader_answers(self,g):\n",
    "        grader_g = list()\n",
    "        for answers in self.gradeings.itervalues():\n",
    "            if g not in answers.answers:\n",
    "                return None\n",
    "            grader_g.append(answers.answers[g])\n",
    "        return grader_g\n",
    "    \n",
    "    def get_handin_score(self,g):\n",
    "        if g not in self.catched_score:\n",
    "            grader_g = list()\n",
    "            for answers in self.gradeings.itervalues():\n",
    "                if g not in  answers.answers:\n",
    "                    return None\n",
    "                grader_g.append(answers.answers[g])\n",
    "            self.catched_score[g] = np.mean(grader_g)\n",
    "        \n",
    "        return self.catched_score[g]\n",
    "\n",
    "class Assignment_(object):\n",
    "    \n",
    "    def __init__(self, handins_input, graders_input, questions_input, n_gradings):\n",
    "        self.graders = dict()\n",
    "        self.handins = dict()\n",
    "        self.questions = dict()\n",
    "        self.n_gradings = n_gradings\n",
    "        for handin in handins_input:\n",
    "            self.handins[str(handin.id)] = handin\n",
    "        for grader in graders_input:\n",
    "            self.graders[str(grader.id)] = grader\n",
    "        for question in questions_input:\n",
    "            self.questions[str(question.id)] = question\n",
    "\n",
    "    def add_handin(self, handin):\n",
    "        self.handing[str(handin.id)] = handin\n",
    "        \n",
    "    def add_grader(self, grader):\n",
    "        self.graders[str(grader.id)] = grader\n",
    "    \n",
    "    def set_questions(self, questions):\n",
    "        self.questions = list(questions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def user_name(user_id):\n",
    "    user = data_model.User.objects.get(id=user_id)\n",
    "    return user.name\n",
    "\n",
    "def user_id(user_name):\n",
    "    for user in data_model.User.objects(name=user_name):\n",
    "        return user.id\n",
    "\n",
    "def question_text(q_id):\n",
    "    user = data_model.Question.objects.get(id=ObjectId(q_id))\n",
    "    return user.text\n",
    "\n",
    "def question_max_value(question):\n",
    "    if question.question_type == \"boolean\":\n",
    "        return 1\n",
    "    elif question.question_type == \"numerical\":\n",
    "        if question.numericalAnswers:\n",
    "            return max(map(int,question.numericalAnswers.keys()))\n",
    "        else:\n",
    "            return 1\n",
    "\n",
    "def answer_value(answer):\n",
    "    if answer.numerical_answer != None:\n",
    "        return answer.numerical_answer / float(question_max_value(answer.question))\n",
    "    if answer.boolean_answer != None:\n",
    "        return answer.boolean_answer / float(question_max_value(answer.question))\n",
    "\n",
    "def answeres_handin(report_grade):\n",
    "    '''Returns a list of tuples containing the answer and the value of the question'''\n",
    "    answers = data_model.Answer.objects(report_grade=report_grade)\n",
    "    result = list()\n",
    "    for answer in answers:\n",
    "        if answer.numerical_answer != None or answer.boolean_answer != None:\n",
    "            result.append((answer,answer_value(answer)))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fetch_assignment_data(ass_obj):\n",
    "    '''\n",
    "    Takes an course and assignment data model object and transforms it into populated Assignment object\n",
    "    Only student or all?\n",
    "    '''\n",
    "\n",
    "    print ass_obj.id\n",
    "    \n",
    "    ## Make dictionary to hold all the graders who have graded a handin\n",
    "    graders = dict()\n",
    "    \n",
    "    ## Find all relevant question for the assignemnt\n",
    "    questions = dict()\n",
    "    for sec in ass_obj.sections:\n",
    "        for question in sec.questions:\n",
    "            if question.question_type != 'text':\n",
    "                questions[str(question.id)] = Question_(str(question.id))\n",
    "    \n",
    "    ## Find all handins\n",
    "    handins = dict()\n",
    "    for handin_ in data_model.Handin.objects(assignment=ass_obj):\n",
    "        handins[str(handin_.id)] = Handin_(str(handin_.id),str(handin_.submitter.id))    \n",
    "        \n",
    "    ## Find all handins graders have graded and vice versa\n",
    "    n_gradings = 0\n",
    "    for handin_ in data_model.Handin.objects(assignment=ass_obj):\n",
    "        for grade in data_model.ReportGrade.objects(handin=handin_,state='ANSWERED'):\n",
    "            \n",
    "            n_gradings = n_gradings + 1\n",
    "            \n",
    "            # Needed if TA or Professor have graded reports as they are not initialy part of it\n",
    "            if str(grade.giver.id) not in graders:\n",
    "                graders[str(grade.giver.id)] = Grader_(str(grade.giver.id))\n",
    "                \n",
    "            handins[str(handin_.id)].add_grader(graders[str(grade.giver.id)])\n",
    "            \n",
    "            ## Find all answers to each question\n",
    "            for answer, answer_value in answeres_handin(grade):\n",
    "                if str(answer.question.id) in questions:\n",
    "                    handins[str(handin_.id)].add_answer(grade.giver,answer.question,answer_value)\n",
    "            \n",
    "    ## update reference in graders\n",
    "    for handin in handins.itervalues():\n",
    "        for grader in handin.graders:\n",
    "            grader.add_handin(handin)\n",
    "\n",
    "    return Assignment_(handins.itervalues(),graders.itervalues(),questions.itervalues(),n_gradings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gibbs_model(data, samples, burn_in=0):\n",
    "    \n",
    "    # Counts\n",
    "    N_H = len(data.handins) # Number of handins\n",
    "    N_G = len(data.graders) # Number of graders\n",
    "    N_Q = len(data.questions) # Number of graders\n",
    "    N_g = data.n_gradings   # Number of gradings\n",
    "    N_eval = N_g*N_G   # Number of evaluations in total\n",
    "    \n",
    "    # Hyperparameters\n",
    "    ga_h = 0.5\n",
    "    la_h = 1.0\n",
    "    al_h = 10.0\n",
    "    be_h = 0.1\n",
    "\n",
    "    ga_g = 0.0\n",
    "    la_g = 1.0\n",
    "    al_g = 50.0\n",
    "    be_g = 0.1\n",
    "    \n",
    "    al_n = 10.0\n",
    "    be_n = 1.0\n",
    "        \n",
    "    # Prior parameters\n",
    "    u_h = dict()\n",
    "    t_h = dict()\n",
    "    u_g = dict()\n",
    "    t_g = dict()\n",
    "    T = dict()\n",
    "    B = dict()\n",
    "\n",
    "    # Draw from priors\n",
    "    n_v = np.random.gamma(al_n,1.0/be_n)\n",
    "    for h in data.handins.iterkeys():\n",
    "        t_h[h] = np.random.gamma(al_h,1.0/be_h)\n",
    "        u_h[h] = np.random.normal(ga_h,np.sqrt(1.0/(la_h * t_h[h])))\n",
    "        T[h] = np.random.normal(u_h[h],np.sqrt(1.0/t_h[h]))\n",
    "    for g in data.graders.iterkeys():\n",
    "        t_g[g] = np.random.gamma(al_g,1.0/be_g)\n",
    "        u_g[g] = np.random.normal(ga_g,np.sqrt(1.0/(la_g * t_g[g])))\n",
    "        B[g] = np.random.normal(u_g[g],np.sqrt(1.0/t_g[g]))\n",
    "            \n",
    "    # Gibbs sampling #\n",
    "    \n",
    "    # Tracers initialising\n",
    "    acc_n_v = list()\n",
    "    acc_u_h = defaultdict(list)\n",
    "    acc_t_h = defaultdict(list)\n",
    "    acc_u_g = defaultdict(list)\n",
    "    acc_t_g = defaultdict(list)\n",
    "    acc_T = defaultdict(list)\n",
    "    acc_B = defaultdict(list)\n",
    "\n",
    "    tw = time.time()\n",
    "    for r in range(burn_in + samples):\n",
    "        print \"\\r%i\" % (r+1) + \" out of %i\" % (burn_in + samples),\n",
    "        \n",
    "        # Sample T\n",
    "        for h, handin in data.handins.iteritems():\n",
    "            n_gradings = 0.0\n",
    "            sum_ = 0.0\n",
    "            for g in handin.graders:\n",
    "                val = handin.get_handin_score(g.id)\n",
    "                if val <> None:\n",
    "                    n_gradings = n_gradings + 1.0\n",
    "                    sum_ = sum_ + val - B[g.id]\n",
    "            v = n_v*n_gradings+t_h[h]\n",
    "            T[h] = np.random.normal((u_h[h]*t_h[h]+n_v*sum_)/v,np.sqrt(1.0/v))\n",
    "\n",
    "        # Sample B\n",
    "        for g, grader in data.graders.iteritems():\n",
    "            n_gradings = 0.0\n",
    "            sum_ = 0.0\n",
    "            for handin in grader.handins:\n",
    "                val = handin.get_handin_score(g)\n",
    "                if val <> None:\n",
    "                    n_gradings = n_gradings + 1.0\n",
    "                    sum_ = sum_ + val - T[handin.id]\n",
    "            v = n_v*n_gradings+t_g[g]\n",
    "            B[g] = np.random.normal((u_g[g]*t_g[g]+n_v*sum_)/v,np.sqrt(1.0/v))\n",
    "\n",
    "        # Sample e\n",
    "        sum_ = 0.0\n",
    "        n_eval = 0\n",
    "        for h, handin in data.handins.iteritems():\n",
    "            for grader in handin.graders:\n",
    "                val = handin.get_handin_score(grader.id)\n",
    "                if val <> None:\n",
    "                    n_eval = n_eval + 1\n",
    "                    sum_ = sum_ + np.square(val - (T[h]+B[grader.id]))\n",
    "        n_v = np.random.gamma(al_n+0.5*n_eval,1.0 / (be_n+0.5*sum_))\n",
    "\n",
    "        # Sample u_h and t_h\n",
    "        for h in data.handins.iterkeys():\n",
    "            la_ = (la_h+1.0)\n",
    "            al_ = al_h+0.5\n",
    "            be_ = be_h+0.5*((la_h*np.square(T[h]-ga_h))/la_)\n",
    "            t_h[h] = np.random.gamma(al_,1.0/be_)\n",
    "            u_h[h] = np.random.normal((la_h*ga_h+T[h])/la_,np.sqrt(1.0 / (la_*t_h[h])))\n",
    "\n",
    "        \n",
    "        # Sample u_g and t_g\n",
    "        for g in data.graders.iterkeys():\n",
    "            la_ = (la_g+1.0)\n",
    "            al_ = al_g+0.5\n",
    "            be_ = be_g+0.5*((la_g*np.square(B[g]-ga_g))/la_)\n",
    "            t_g[g] = np.random.gamma(al_,1.0/be_)\n",
    "            u_g[g] = np.random.normal((la_g*ga_g+B[g])/la_,np.sqrt(1.0 / (la_*t_g[g])))\n",
    "                        \n",
    "        # Collect tracings\n",
    "        if r > burn_in:\n",
    "            acc_n_v.append(n_v)\n",
    "            for h in data.handins.iterkeys():\n",
    "                acc_u_h[h].append(u_h[h])\n",
    "                acc_t_h[h].append(t_h[h])\n",
    "                acc_T[h].append(T[h])\n",
    "            for g in data.graders.iterkeys():\n",
    "                acc_u_g[g].append(u_g[g])\n",
    "                acc_t_g[g].append(t_g[g]) \n",
    "                acc_B[g].append(B[g])\n",
    "                    \n",
    "    print\n",
    "    print \"Wall time: %f\" % (time.time() - tw)\n",
    "    \n",
    "    traces = {'n_v' : acc_n_v,\n",
    "              'u_h' : acc_u_h,\n",
    "              't_h' : acc_t_h,\n",
    "              'u_g' : acc_u_g,\n",
    "              't_g' : acc_t_g,\n",
    "              'T' : acc_T,\n",
    "              'B' : acc_B}\n",
    "\n",
    "    return traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gibbs_e_model(data, samples, burn_in=0):\n",
    "    \n",
    "    # Counts\n",
    "    N_H = len(data.handins) # Number of handins\n",
    "    N_G = len(data.graders) # Number of graders\n",
    "    N_Q = len(data.questions) # Number of graders\n",
    "    N_g = data.n_gradings   # Number of gradings\n",
    "    N_eval = N_g*N_G   # Number of evaluations in total\n",
    "    \n",
    "    # Hyperparameters\n",
    "    ga_h = 0.5\n",
    "    la_h = 1.0\n",
    "    al_h = 10.0\n",
    "    be_h = 0.1\n",
    "\n",
    "    ga_g = 0.0\n",
    "    la_g = 1.0\n",
    "    al_g = 50.0\n",
    "    be_g = 0.1\n",
    "    \n",
    "    al_n = 10.0\n",
    "    be_n = 1.0\n",
    "    \n",
    "    # Prior parameters\n",
    "    u_h = dict()\n",
    "    t_h = dict()\n",
    "    T = defaultdict(dict)\n",
    "    B = defaultdict(dict)\n",
    "    u_g = dict()\n",
    "    t_g = dict()\n",
    "\n",
    "    # Draw from priors\n",
    "    n_v = np.random.gamma(al_n,1.0/be_n)\n",
    "    for h in data.handins.iterkeys():\n",
    "        t_h[h] = np.random.gamma(al_h,1.0/be_h)\n",
    "        u_h[h] = np.random.normal(ga_h,np.sqrt(1.0/(la_h * t_h[h])))\n",
    "        for q in data.questions.iterkeys():        \n",
    "            T[h][q] = np.random.normal(u_h[h],np.sqrt(1.0/t_h[h]))\n",
    "    for g in data.graders.iterkeys():\n",
    "        t_g[g] = np.random.gamma(al_g,1.0/be_g)\n",
    "        u_g[g] = np.random.normal(ga_g,np.sqrt(1.0/(la_g * t_g[g])))\n",
    "        for q in data.questions.iterkeys():\n",
    "            B[str(g)][str(q)] = np.random.normal(u_g[g],np.sqrt(1.0/t_g[g]))\n",
    "            \n",
    "    # Gibbs sampling #\n",
    "    \n",
    "    # Tracers initialising\n",
    "    acc_n_v = list()\n",
    "    acc_u_h = defaultdict(list)\n",
    "    acc_t_h = defaultdict(list)\n",
    "    acc_u_g = defaultdict(list)\n",
    "    acc_t_g = defaultdict(list)\n",
    "    acc_T = defaultdict(lambda: defaultdict(list))\n",
    "    acc_B = defaultdict(lambda: defaultdict(list))\n",
    "\n",
    "    tw = time.time()\n",
    "    for r in range(burn_in + samples):\n",
    "        print \"\\r%i\" % (r+1) + \" out of %i\" % (burn_in + samples),\n",
    "        \n",
    "        # Sample T\n",
    "        for h, handin in data.handins.iteritems():\n",
    "            for q, answers in handin.gradeings.iteritems():\n",
    "                n_gradings = len(answers.answers)\n",
    "                sum_ = 0.0\n",
    "                for g, val in answers.answers.iteritems():\n",
    "                    sum_ = sum_ + val - B[str(g)][str(q)]\n",
    "                v = n_v*n_gradings+t_h[h]\n",
    "                T[h][q] = np.random.normal((u_h[h]*t_h[h]+n_v*sum_)/v,np.sqrt(1.0/v))\n",
    "            \n",
    "        # Sample B\n",
    "        for g, grader in data.graders.iteritems():\n",
    "            for q in data.questions.iterkeys():\n",
    "                n_gradings = 0.0\n",
    "                sum_ = 0.0\n",
    "                for h in grader.handins:\n",
    "                    if g in h.gradeings[q].answers:\n",
    "                        n_gradings = n_gradings + 1.0\n",
    "                        sum_ = sum_ + h.gradeings[q].answers[g] - T[h.id][q]\n",
    "                v = n_v * n_gradings + t_g[g]\n",
    "                B[g][q] = np.random.normal((u_g[g]*t_g[g]+n_v*sum_)/v,np.sqrt(1.0/v))\n",
    "        \n",
    "        # Sample e\n",
    "        sum_ = 0.0\n",
    "        n_eval = 0\n",
    "        for h, handin in data.handins.iteritems():\n",
    "            for q, answers in handin.gradeings.iteritems():\n",
    "                for g, answer_val in answers.answers.iteritems():\n",
    "                    n_eval = n_eval + 1\n",
    "                    sum_ = sum_ + np.square(answer_val - (T[h][q]+B[g][q]))\n",
    "        n_v = np.random.gamma(al_n+0.5*n_eval,1.0 / (be_n+0.5*sum_))\n",
    "\n",
    "        # Sample u_q and t_q\n",
    "        for h in data.handins.iterkeys():\n",
    "            la_ = (la_h+N_Q)\n",
    "            sum_q = sum(T[h].values())\n",
    "            mean_q = sum_q / N_Q\n",
    "            sum_minus = 0.0\n",
    "            for q in data.questions.iterkeys():\n",
    "                sum_minus = sum_minus + np.square(T[h][q]-mean_q)\n",
    "            al_ = al_h+0.5*N_Q\n",
    "            be_ = be_h+0.5*(N_Q*sum_minus+(N_Q*la_h*np.square(mean_q-ga_h))/la_)\n",
    "            t_h[h] = np.random.gamma(al_,1.0 / be_)\n",
    "            u_h[h] = np.random.normal((la_h*ga_h+sum_q)/la_,np.sqrt(1.0/(la_*t_h[h])))\n",
    "\n",
    "        # Sample u_g and t_g\n",
    "        for g in data.graders.iterkeys():\n",
    "            la_ = (la_g+N_Q)\n",
    "            sum_q = sum(B[g].values())\n",
    "            mean_q = sum_q / N_Q\n",
    "            sum_minus = 0.0\n",
    "            for q in data.questions.iterkeys():\n",
    "                sum_minus = sum_minus + np.square(B[g][q]-mean_q)\n",
    "            al_ = al_g+0.5*N_Q\n",
    "            be_ = be_g+0.5*(N_Q*sum_minus+(N_Q*la_g*np.square(mean_q-ga_g))/la_)\n",
    "            t_g[g] = np.random.gamma(al_,1.0 / be_)\n",
    "            u_g[g] = np.random.normal((la_g*ga_g+sum_q)/la_,np.sqrt(1.0/(la_*t_g[g])))\n",
    "                        \n",
    "        # Collect tracings\n",
    "        if r > burn_in:\n",
    "            acc_n_v.append(n_v)\n",
    "            for h in data.handins.iterkeys():\n",
    "                acc_u_h[h].append(u_h[h])\n",
    "                acc_t_h[h].append(t_h[h])\n",
    "                for q in data.questions.iterkeys():\n",
    "                    acc_T[h][q].append(T[h][q])\n",
    "            for g in data.graders.iterkeys():\n",
    "                acc_u_g[g].append(u_g[g])\n",
    "                acc_t_g[g].append(t_g[g])\n",
    "                for q in data.questions.iterkeys():\n",
    "                    acc_B[g][q].append(B[g][q])\n",
    "                    \n",
    "    print\n",
    "    print \"Wall time: %f\" % (time.time() - tw)\n",
    "    \n",
    "    traces = {'n_v' : acc_n_v,\n",
    "              'u_h' : acc_u_h,\n",
    "              't_h' : acc_t_h,\n",
    "              'u_g' : acc_u_g,\n",
    "              't_g' : acc_t_g,\n",
    "              'T' : acc_T,\n",
    "              'B' : acc_B}\n",
    "\n",
    "    return traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def norm_log_pdf(x,u,t):\n",
    "    return -0.5*t*(x-u)**2+np.log(t)-np.log(np.sqrt(2.0*math.pi))\n",
    "\n",
    "def gamma_log_pdf(x,a,b):\n",
    "    return a*np.log(b)-np.log(math.gamma(a))+(a-1.0)*np.log(x)-b*x\n",
    "\n",
    "def norm_gamma_log_pdf(u,t,ga,la,a,b):\n",
    "    return a*np.log(b)+np.log(np.sqrt(la))-np.log(math.gamma(a))-np.log(np.sqrt(2.0*math.pi))+(a-1)*np.log(t)-np.log(b*t)-0.5*t*la*(u-ga)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def MH_model(data,samples,burn_in=0):\n",
    "\n",
    "    # Counts\n",
    "    N_H = len(data.handins) # Number of handins\n",
    "    N_G = len(data.graders) # Number of graders\n",
    "    N_g = data.n_gradings   # Number of gradings\n",
    "    N_eval = N_g*N_G   # Number of evaluations in total\n",
    "    \n",
    "    # Hyperparameters\n",
    "    ga_h = 0.5\n",
    "    la_h = 1.0\n",
    "    al_h = 10.0\n",
    "    be_h = 0.1\n",
    "\n",
    "    ga_g = 0.0\n",
    "    la_g = 1.0\n",
    "    al_g = 50.0\n",
    "    be_g = 0.1\n",
    "    \n",
    "    al_e = 10.0\n",
    "    be_e = 1.0\n",
    "    t_h = 500.0\n",
    "    t_g = 100.0\n",
    "    \n",
    "    # Prior parameters\n",
    "    u_h = dict()\n",
    "    t_h = dict()\n",
    "    u_g = dict()\n",
    "    t_g = dict()\n",
    "    \n",
    "    log_h = dict()\n",
    "    log_g = dict()\n",
    "\n",
    "    def prop_u_t_h(handin,u_h,t_h):\n",
    "        sum_ = 0.0\n",
    "        for g in handin.graders:\n",
    "            val = handin.get_handin_score(g.id)\n",
    "            if val <> None:\n",
    "                sum_ = sum_ + norm_log_pdf(val,u_g[g.id]+u_h,t_g[g.id]+t_h)\n",
    "        return sum_ + norm_gamma_log_pdf(u_h,t_h,ga_h,la_h,al_h,be_h)\n",
    "    \n",
    "    def prop_u_t_g(grader,g,u_g,t_g):\n",
    "        sum_ = 0.0\n",
    "        for h in grader.handins:\n",
    "            val = h.get_handin_score(g)\n",
    "            if val <> None:\n",
    "                sum_ = sum_ + norm_log_pdf(val,u_g+u_h[h.id],t_g+t_h[h.id])\n",
    "        return sum_ + norm_gamma_log_pdf(u_g,t_g,ga_g,la_g,al_g,be_g)\n",
    "\n",
    "    \n",
    "    # Draw from priors\n",
    "    for h in data.handins.iterkeys():\n",
    "        t_h[h] = np.random.gamma(al_h,1/be_h)\n",
    "        u_h[h] = np.random.normal(ga_h,np.sqrt(1/(la_g * t_h[h])))\n",
    "    for g in data.graders.iterkeys():\n",
    "        t_g[g] = np.random.gamma(al_g,1/be_g)\n",
    "        u_g[g] = np.random.normal(ga_g,np.sqrt(1/(la_g * t_g[g]))) \n",
    "    # pre calcuate the liklyhood\n",
    "    for h, handin in data.handins.iteritems():\n",
    "        log_h[h] = prop_u_t_h(handin,u_h[h],t_h[h])\n",
    "    for g, grader in data.graders.iteritems():\n",
    "        log_g[g] = prop_u_t_g(grader,g,u_g[g],t_g[g])\n",
    "            \n",
    "    # Tracers initialising\n",
    "    trace_u_h = defaultdict(float)\n",
    "    trace_t_h = defaultdict(float)\n",
    "    trace_u_g = defaultdict(float)\n",
    "    trace_t_g = defaultdict(float)\n",
    "    \n",
    "    tw = time.time()\n",
    "    for r in range(burn_in + samples):\n",
    "        print \"\\r%i\" % (r+1) + \" out of %i\" % (burn_in + samples),\n",
    "        \n",
    "        # Sample u_h and t_h\n",
    "        for h, handin in data.handins.iteritems():\n",
    "            # Propose new candidates\n",
    "            u_h_c = np.random.normal(u_h[h],0.1)\n",
    "            t_h_c = np.random.normal(t_h[h],0.1)\n",
    "            #draw from gamma\n",
    "            p_ = prop_u_t_h(handin,u_h_c,t_h_c)\n",
    "            alpha = min(1,p_-log_h[h])\n",
    "            if np.log(np.random.random()) <= alpha:\n",
    "                u_h[h] = u_h_c\n",
    "                t_h[h] = t_h_c\n",
    "                log_h[h] = p_\n",
    "                    \n",
    "        # Sample u_g and t_g\n",
    "        for g, grader in data.graders.iteritems():\n",
    "            # Propose new candidates\n",
    "            u_g_c = np.random.normal(u_g[g],0.1)\n",
    "            t_g_c = np.random.normal(t_g[g],0.1)\n",
    "            #draw from gamma\n",
    "            p_ = prop_u_t_g(grader,g,u_g_c,t_g_c)\n",
    "            alpha = min(1,p_-log_g[g])\n",
    "            if np.log(np.random.random()) <= alpha:\n",
    "                u_g[g] = u_g_c\n",
    "                t_g[g] = t_g_c    \n",
    "                log_g[g] = p_\n",
    "\n",
    "        # Collect tracings\n",
    "        if r > burn_in:\n",
    "            for h in data.handins.iterkeys():\n",
    "                trace_u_h[h] = trace_u_h[h] + u_h[h]\n",
    "                trace_t_h[h] = trace_t_h[h] + t_h[h]\n",
    "            for g in data.graders.iterkeys():\n",
    "                trace_u_g[g] = trace_u_g[g] + u_g[g]\n",
    "                trace_t_g[g] = trace_t_g[g] + t_g[g]\n",
    "                \n",
    "    n_collected_samples = r - burn_in\n",
    "    for h in data.handins.iterkeys():\n",
    "        trace_u_h[h] = trace_u_h[h] / n_collected_samples\n",
    "        trace_t_h[h] = trace_t_h[h] / n_collected_samples\n",
    "    for g in data.graders.iterkeys():\n",
    "        trace_u_g[g] = trace_u_g[g] / n_collected_samples\n",
    "        trace_t_g[g] = trace_t_g[g] / n_collected_samples\n",
    "                \n",
    "    print\n",
    "    print \"Wall time: %f\" % (time.time() - tw)\n",
    "    \n",
    "    \n",
    "    traces = {'u_h' : trace_u_h,\n",
    "              't_h' : trace_t_h,\n",
    "              'u_g' : trace_u_g,\n",
    "              't_g' : trace_t_g}\n",
    "\n",
    "    return traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55f277cff3bd61000a2112d4\n"
     ]
    }
   ],
   "source": [
    "a1 = data_model.Assignment.objects.get(title=\"UNIX, Python and Fast Data\")\n",
    "\n",
    "a1_data = fetch_assignment_data(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55f69a6cb3cbf8000b5ae1c8\n"
     ]
    }
   ],
   "source": [
    "a2 = data_model.Assignment.objects.get(title=\"Databases and Streaming\")\n",
    "\n",
    "a2_data = fetch_assignment_data(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(7,5)\n",
    "q_scores = list()\n",
    "for handin in a2_data.handins.itervalues():\n",
    "    for answer in handin.gradeings.itervalues():\n",
    "        q_scores.extend(answer.answers.values())\n",
    "plt.hist(q_scores,bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55f7cd326e34ab000b4bc376\n"
     ]
    }
   ],
   "source": [
    "a3 = data_model.Assignment.objects.get(title=\"Big Data and Deep Learning\")\n",
    "\n",
    "a3_data = fetch_assignment_data(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(7,5)\n",
    "q_scores = list()\n",
    "for handin in a3_data.handins.itervalues():\n",
    "    for answer in handin.gradeings.itervalues():\n",
    "        q_scores.extend(answer.answers.values())\n",
    "plt.hist(q_scores,bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55f7cd456e34ab000b4bc37c\n"
     ]
    }
   ],
   "source": [
    "a4 = data_model.Assignment.objects.get(title=\"Your Choice of Subject\")\n",
    "\n",
    "a4_data = fetch_assignment_data(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(7,5)\n",
    "q_scores = list()\n",
    "for handin in a4_data.handins.itervalues():\n",
    "    for answer in handin.gradeings.itervalues():\n",
    "        q_scores.extend(answer.answers.values())\n",
    "plt.hist(q_scores,bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(7,5)\n",
    "q_scores = list()\n",
    "for handin in a1_data.handins.itervalues():\n",
    "    for answer in handin.gradeings.itervalues():\n",
    "        q_scores.extend(answer.answers.values())\n",
    "plt.hist(q_scores,bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a1_gibbs_e_result = gibbs_e_model(a1_data,2000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 out of 5000\n",
      "Wall time: 75.688723\n"
     ]
    }
   ],
   "source": [
    "a1_MH_result = MH_model(a1_data,4000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a2_gibbs_e_result = gibbs_e_model(a2_data,2000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 84.266960\n"
     ]
    }
   ],
   "source": [
    "a2_MH_result = MH_model(a2_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a3_gibbs_e_result = gibbs_e_model(a3_data,2000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 89.455500\n"
     ]
    }
   ],
   "source": [
    "a3_MH_result = MH_model(a3_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a4_gibbs_e_result = gibbs_e_model(a4_data,2000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 91.684823\n"
     ]
    }
   ],
   "source": [
    "a4_MH_result = MH_model(a4_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print len(a1_data.questions), len(a1_data.graders), len(a1_data.handins)\n",
    "print len(a2_data.questions), len(a2_data.graders), len(a2_data.handins)\n",
    "print len(a3_data.questions), len(a3_data.graders), len(a3_data.handins)\n",
    "print len(a4_data.questions), len(a4_data.graders), len(a4_data.handins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print np.mean([len(x.graders) for x in a1_data.handins.values()])\n",
    "print np.mean([len(x.graders) for x in a2_data.handins.values()])\n",
    "print np.mean([len(x.graders) for x in a3_data.handins.values()])\n",
    "print np.mean([len(x.graders) for x in a4_data.handins.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print np.mean([len(x.handins) for x in a1_data.graders.values()])\n",
    "print np.mean([len(x.handins) for x in a2_data.graders.values()])\n",
    "print np.mean([len(x.handins) for x in a3_data.graders.values()])\n",
    "print np.mean([len(x.handins) for x in a4_data.graders.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MAE(true,estimated):\n",
    "    return np.mean(map(lambda x : abs((x[1] - x[0])/x[1]),zip(true,estimated)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def truncate(string,length):\n",
    "    return (string[:length] + '..') if len(string) > length else string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def handin_students(h_id):\n",
    "    model_handin = data_model.Handin.objects.get(id=ObjectId(h_id))\n",
    "    students_ = list()\n",
    "    for student in model_handin.students:\n",
    "        students_.append(student.name)\n",
    "    return \", \".join(students_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_handins(t,results,nth=1):\n",
    "\n",
    "    colors = [\"#348ABD\",\"#A60628\",\"#7A68A6\",\"#467821\",\"m\",\"c\"]\n",
    "    labels = list()\n",
    "    for (name,data) in results:\n",
    "        labels.append(name)\n",
    "        \n",
    "    scores = list()\n",
    "    for id, g in t.handins.iteritems():\n",
    "        text = truncate(handin_students(id),45)\n",
    "        val = list()\n",
    "        for (name,data) in results:\n",
    "            val.append(np.mean(data['u_h'][id]))\n",
    "        obs_scores = list()\n",
    "        for grader in t.handins[id].graders:\n",
    "            val_ = t.handins[id].get_grader_answers(grader.id)\n",
    "            if val_ != None:\n",
    "                obs_scores.extend(val_)\n",
    "        scores.append((text,val,np.mean(data['u_h'][id]),np.mean(obs_scores)))\n",
    "    scores.sort(key=lambda x:x[2]) \n",
    "    scores_ = scores[::nth]\n",
    "\n",
    "    y = xrange(0,len(scores_))\n",
    "    for i in xrange(len(results)):\n",
    "        x = map(lambda x : x[1][i],scores)\n",
    "        x_ = map(lambda x : x[1][i],scores_)\n",
    "        plt.plot(x_,y,'o',color=colors[i],label=labels[i],alpha=0.65)\n",
    "\n",
    "    obs_score_ = map(lambda x : x[3],scores_)\n",
    "    for i, ob in enumerate(obs_score_):\n",
    "        if i == 0:\n",
    "#            plt.plot(ob,[y[i]]*len(ob),'.',color='gray',alpha=0.1,label=\"Obs scores\")\n",
    "            plt.plot(np.mean(ob),y[i],'.',color='black',label=\"Average obs score\")\n",
    "        else:\n",
    "#            plt.plot(ob,[y[i]]*len(ob),'.',color='gray',alpha=0.1)\n",
    "            plt.plot(np.mean(ob),y[i],'.',color='black')\n",
    "                      \n",
    "    ylabel = map(lambda x : x[0],scores_)    \n",
    "    plt.yticks(xrange(0,len(scores)),ylabel)\n",
    "    leg = plt.legend(numpoints=1,loc=2)\n",
    "    leg.get_frame().set_alpha(0.4)\n",
    "    plt.title(\"Score of the handins\")\n",
    "#    plt.vlines(0, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "    plt.xlim(-0.05,1.05)\n",
    "    plt.ylabel(\"Handin\")\n",
    "    plt.xlabel(\"Score\")\n",
    "    plt.margins(0.02)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_bias(t,results,nth=1):\n",
    "\n",
    "    colors = [\"#467821\",\"#A60628\",\"#7A68A6\",\"#348ABD\",\"m\",\"c\"]\n",
    "    labels = list()\n",
    "    for (name,data) in results:\n",
    "        labels.append(name)\n",
    "        \n",
    "    scores = list()\n",
    "    for id, g in t.graders.iteritems():\n",
    "        text = truncate(user_name(ObjectId(id)),45)\n",
    "        text = text + \": %i\" % len(t.graders[id].handins)\n",
    "        val = list()\n",
    "        for (name,data) in results:\n",
    "            val.append(np.mean(data['u_g'][id]))\n",
    "        scores.append((text,val,np.mean(data['u_g'][id])))\n",
    "        \n",
    "    scores = scores[::nth]\n",
    "    ax = plt.subplot(111)\n",
    "    scores.sort(key=lambda x:x[2])\n",
    "    y = xrange(0,len(scores))\n",
    "    \n",
    "    for i in xrange(len(results)):\n",
    "        x = map(lambda x : x[1][i],scores)\n",
    "        plt.plot(x,y,'o',color=colors[i],label=labels[i],alpha=0.65)\n",
    "    \n",
    "    ylabel = map(lambda x : x[0],scores)    \n",
    "    plt.yticks(xrange(0,len(scores)),ylabel)\n",
    "#    leg = plt.legend(numpoints=1,loc=4)\n",
    "#    leg.get_frame().set_alpha(0.4)\n",
    "    plt.title(\"Bias of the graders\")\n",
    "    plt.vlines(0, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "    plt.ylabel(\"Grader : [Graded handins]\")\n",
    "    plt.xlabel(\"Bias\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_all_grader_bias(data_result,nth=1):\n",
    "    \n",
    "    grader_bias = defaultdict(list)\n",
    "    grader_n_h = defaultdict(list)\n",
    "    grader_ass = defaultdict(list)\n",
    "    \n",
    "    for i, (data,result) in enumerate(data_result):\n",
    "\n",
    "        for id, g in data.graders.iteritems():\n",
    "            n_h = len(data.graders[id].handins)\n",
    "            grader_n_h[id].append(n_h)\n",
    "            grader_bias[id].append((result['u_g'][id]))\n",
    "            grader_ass[id].append(i+1)\n",
    "\n",
    "    scores = list()\n",
    "    for name, values in grader_bias.iteritems():\n",
    "        l = user_name(ObjectId(name)) + \": %i, %i\" % (len(grader_n_h[name]),sum(grader_n_h[name]))\n",
    "        scores.append((l,values,grader_ass[name]))\n",
    "    scores.sort(key=lambda x: np.mean(x[1]))\n",
    "    scores = scores[6::nth]\n",
    "    \n",
    "    ax = plt.subplot(111)\n",
    "    \n",
    "    plotted = dict()\n",
    "    p_style = {1:'x',2:'.',3:'+',4:'*',5:'8',6:'s',7:'v',8:'d',9:'<'}\n",
    "    \n",
    "    model = map(lambda x : x[1],scores)\n",
    "    model_m = map(lambda x : np.mean(x[1]),scores)\n",
    "    ass = map(lambda x : x[2],scores)\n",
    "    label = map(lambda x : x[0],scores)\n",
    "    y = xrange(0,len(scores))\n",
    "    for i, m in enumerate(model):\n",
    "        for j, n in enumerate(m):\n",
    "            a = ass[i][j] \n",
    "            if a not in plotted:\n",
    "                plt.plot(m[j],y[i],p_style[a],color=\"#467821\",label=\"Assignment \"+str(a),alpha=0.65)\n",
    "                plotted[a] = True\n",
    "            else:\n",
    "                plt.plot(m[j],y[i],p_style[a],color=\"#467821\",alpha=0.65)\n",
    "#    plt.boxplot(model_1,positions=y, vert=False)\n",
    "    plt.plot(model_m,y,'o',color=\"#A60628\",label=\"Average Bias\",alpha=0.65)\n",
    "\n",
    "    plt.yticks(xrange(0,len(scores)),label)\n",
    "    leg = plt.legend(loc=4,numpoints=1)\n",
    "    leg.get_frame().set_alpha(0.4)\n",
    "    plt.title(\"Assignments Bias\")\n",
    "    plt.vlines(0, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "    plt.ylabel(\"Grader: [Graded assignments], [Graded handins]\")\n",
    "    plt.xlabel(\"Bias\")\n",
    "    plt.margins(0.01)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YZUwBLFhVnCwVyK4eBFkJwSobDaYzYqPBmALYaDBZWOXSGGOMMTXDRoMxxnQxrD1hqoWX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11558a050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"CT_Ass1_bias.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(6,6))\n",
    "    plot_bias(a1_data,[(\"PG\",a1_MH_result)],nth=5)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RpnHjxqZjx45mypQpxcfWrFljTjvtNNOsWTPTqlUr88ADD5Rq14IFC0y7du1K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\n3WEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165b3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"CT_Ass1_Handin_Scores.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(6,6))\n",
    "    plot_handins(a1_data,[(\"PG\",a1_MH_result)],nth=4)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive 57.522124\n",
      "negative 42.477876\n"
     ]
    }
   ],
   "source": [
    "pos_g = 0.0\n",
    "neg_g = 0.0\n",
    "for id, g in a1_data.graders.iteritems():\n",
    "    val = np.mean(a1_MH_result['u_g'][id])\n",
    "    if val < 0:\n",
    "        neg_g = neg_g + 1.0\n",
    "    elif val > 0:\n",
    "        pos_g = pos_g + 1.0\n",
    "    else:\n",
    "        print \"zero\"\n",
    "print \"positive %f\" % (pos_g/(pos_g+neg_g)*100)\n",
    "print \"negative %f\" % (neg_g/(pos_g+neg_g)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total result:\n",
      "34 Positive (31.5%) 74 Neutral (68.5%) 0 Negative (0.0%) out of 108\n",
      "-0.193379219928\n"
     ]
    },
    {
     "data": {
      "image/png": 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hATQaDd566w2azd8B4I03fsSyZY8Umsfhw8/y+usLkU6SBOOPq/xloEPaJgWwFXgbuK7v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114946710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_nature((a1_data,a1_MH_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total result:\n",
      "31 Positive (28.7%) 77 Neutral (71.3%) 0 Negative (0.0%) out of 108\n",
      "-1.19769474489\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11491ad90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_nature((a1_data,a1_MH_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(6,6)\n",
    "plot_bias(a2_data,[(\"MH\",a2_MH_result)],nth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(8,6)\n",
    "plot_handins(a2_data,[(\"MH\",a2_MH_result)],nth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(8,6)\n",
    "plot_bias(a3_data,[(\"MH\",a3_MH_result)],nth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(8,6)\n",
    "plot_handins(a3_data,[(\"MH\",a3_MH_result)],nth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(8,6)\n",
    "plot_bias(a4_data,[(\"MH\",a4_MH_result)],nth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B2VXU+YXrM0h24PNDVF4TkGD7Q0R/JYHPZaR/D21CovAE4Eokdn2xOQu5D61B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9733XnD3ZgagAfRWuDG+hBaswUoVngc0ac4R7v6lgLVwPYx1+yAoqjUu58yV96H3HNlGIaxSVLf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\n95jfOObr9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w4qsp2OID53re23PfeCMcYYY7oBPSWzZndnGMrwOBm4j1Q2wzKUNfJQFJt6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\nGGOMMcYYY4wxxpSO/w9Agl8aEybaVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ecdd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"CT_Ass4_Handin_scores.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(8,6))\n",
    "    plot_handins(a4_data,[(\"PG\",a4_MH_result)],nth=2)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive 74.000000\n",
      "negative 26.000000\n"
     ]
    }
   ],
   "source": [
    "pos_g = 0.0\n",
    "neg_g = 0.0\n",
    "for id, g in a4_data.graders.iteritems():\n",
    "    val = np.mean(a4_MH_result['u_g'][id])\n",
    "    if val < 0:\n",
    "        neg_g = neg_g + 1.0\n",
    "    elif val > 0:\n",
    "        pos_g = pos_g + 1.0\n",
    "    else:\n",
    "        print \"zero\"\n",
    "print \"positive %f\" % (pos_g/(pos_g+neg_g)*100)\n",
    "print \"negative %f\" % (neg_g/(pos_g+neg_g)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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trvZEflI4v7r91Lbd6izMPcg9mJSUlkx/OxuXK5nyynLGXJ1J7/S+dTJfQyE3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\nRSkL/w9tnaGaKxdgywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115edcbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"CT_Ass_Bias.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(8,6))\n",
    "    plot_all_grader_bias([(a1_data,a1_MH_result),(a2_data,a2_MH_result),(a3_data,a3_MH_result),(a4_data,a4_MH_result)],nth=5)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c_l = list()\n",
    "print np.mean([np.mean(map(lambda y: y[1],x)) for x in a1_data.handins['560948691bd43a000fb1228d'].get_graders_answers().values()])\n",
    "print np.mean(a1_MH_result['u_h']['560948691bd43a000fb1228d'])\n",
    "for grader in a1_data.handins['560948691bd43a000fb1228d'].graders:\n",
    "    c_l.append(np.mean(a1_MH_result['u_g'][grader.id]))\n",
    "print np.mean(c_l)\n",
    "print np.mean(a1_gibbs_e_result['u_h']['560948691bd43a000fb1228d'])\n",
    "for grader in a1_data.handins['560948691bd43a000fb1228d'].graders:\n",
    "    c_l.append(np.mean(a1_gibbs_e_result['u_g'][grader.id]))\n",
    "print np.mean(c_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56ab3b44002566000f6f34e8\n"
     ]
    }
   ],
   "source": [
    "a_a2 = data_model.Assignment.objects.get(title=\"Aflevering 1: Sjov med tabeller\")\n",
    "\n",
    "a_a2_data = fetch_assignment_data(a_a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(7,5)\n",
    "q_scores = list()\n",
    "for handin in a_a2_data.handins.itervalues():\n",
    "    for answer in handin.gradeings.itervalues():\n",
    "        q_scores.extend(answer.answers.values())\n",
    "plt.hist(q_scores,bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a_a2_gibbs_e_result = gibbs_e_model(a_a2_data,1000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11000 out of 11000\n",
      "Wall time: 312.945545\n"
     ]
    }
   ],
   "source": [
    "a_a2_MH_result = MH_model(a_a2_data,10000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(10,15)\n",
    "plot_handins(a_a2_data,[(\"MH\",a_a2_MH_result)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lzHhqUJLwViiR9kzgeuQhAeXmPIm8DJNR9dWnKOfln0gR+SbU8qATcASx6iFH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\nMIzK4f8BO9gp9fB+31AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159958d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"Algo_Ass1_Bias.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(6,6))\n",
    "    plot_bias(a_a2_data,[(\"PG\",a_a2_MH_result)],nth=6)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HjyzhAPxx2Ag3d/7CjNxv1apVrm/fvlv9/yfMCdiuaBj5/H+U3InPSE3sP/Bt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SiWPAJtS/wqkSztOLSLoOc+ojK+T4FLiEr0/IS2BVFM+pJm+jwSZbIfRLRNcQ3KlId04R7InYD9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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156808d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"Algo_Ass1_Handin_Scores.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(6,6))\n",
    "    plot_handins(a_a2_data,[(\"PG\",a_a2_MH_result)],nth=7)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos_g = 0.0\n",
    "neg_g = 0.0\n",
    "his = list()\n",
    "for id, g in a_a2_data.graders.iteritems():\n",
    "    val = np.mean(a_a2_MH_result['u_g'][id])\n",
    "    his.append(his)\n",
    "    if val < 0:\n",
    "        neg_g = neg_g + 1.0\n",
    "    elif val > 0:\n",
    "        pos_g = pos_g + 1.0\n",
    "    else:\n",
    "        print \"zero\"\n",
    "print \"positive %f\" % (pos_g/(pos_g+neg_g)*100)\n",
    "print \"negative %f\" % (neg_g/(pos_g+neg_g)*100)\n",
    "plt.hist(his,bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_nature((ass_data,ass_result)):\n",
    "        \n",
    "    p = list()\n",
    "    n = list()\n",
    "    net = list()\n",
    "        \n",
    "    for u_id, grader in ass_data.graders.iteritems():\n",
    "    # Find the handins that the person have graded    \n",
    "        tot_neg = 0\n",
    "        tot_pos = 0\n",
    "        tot_net = 0\n",
    "        tot_n = 0\n",
    "\n",
    "        for handin in grader.handins:\n",
    "            # find the values of each question in each hadnin\n",
    "\n",
    "            for q in ass_data.questions.iterkeys():\n",
    "\n",
    "\n",
    "                q_data_model = data_model.Question.objects.get(id=ObjectId(q))\n",
    "                q_max = question_max_value(q_data_model)            \n",
    "\n",
    "                q_values = map(lambda x : int(x*q_max),handin.gradeings[q].answers.values())\n",
    "                if q not in handin.gradeings or str(u_id) not in handin.gradeings[q].answers:\n",
    "                    continue\n",
    "                g_q_val = int(handin.gradeings[q].answers[str(u_id)]*q_max)\n",
    "\n",
    "                tot_n = tot_n + 1 \n",
    "                \n",
    "                val = [0]*(q_max+1)\n",
    "                for q_val in q_values:\n",
    "                    val[q_val] = val[q_val] + 1\n",
    "\n",
    "                if np.median(q_values) - 0.5 > g_q_val:\n",
    "                    tot_neg = tot_neg + 1\n",
    "                elif np.median(q_values) + 0.5 < g_q_val:\n",
    "                    tot_pos = tot_pos + 1\n",
    "                else:\n",
    "                    tot_net = tot_net + 1\n",
    "        p.append(tot_pos)\n",
    "        p.append(-tot_neg)\n",
    "    \n",
    "    print \"Total result:\"\n",
    "    result = [(\"Positive\",tot_pos),(\"Neutral\",tot_net),(\"Negative\",tot_neg)]\n",
    "    for (l,v) in result:\n",
    "        print l + \" (%.1f%%)\" % ((float(v)/float(tot_n))*100.0),\n",
    "    print \"out of %i\" % tot_n\n",
    "    \n",
    "    plt.hist(p,bins=20)\n",
    "    print np.mean(p)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total result:\n",
      "Positive (6.7%) Neutral (93.3%) Negative (0.0%) out of 15\n",
      "-0.287330316742\n"
     ]
    },
    {
     "data": {
      "image/png": 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b9dUrm35wOWr+XfVDnfvh7aL24peLz7q3adf3fD/aNG/7tupXq++u3ts0RXLYXOo1OFa9\noOn32l5V3VP9zf0b2r661Otwsrplw7q5/vLAXrvYa/COzs3h/2j1/6r379egZnag34hgn11Z/VL1\n9qY9rqPkH1VfbjqetTbvUC7uqxuWF9UTcw1kRr9WvWbD9YervzrTWObyt6ozTceyHm2aInqs+msz\njmku31f9dvUXZx7Hfvq2pmOZZ53saJ6McUX169W/mHsgM/nxpj3uR5sOHf3f6n2zjugC/lfnvmH/\nvaYzCI+aN1d3rpZvbJoqPeqO6okYtzadPfbCuQeyz7wRwfRD+/uapseYunAgzx781qZ52/ur/9E0\nj3vUXFH9QvWZ6hMd4N3iffS/O5rReqj6P03TI5+sfnre4eyro/5GBN/edBzn/s79/996yUccbq9p\nk7MHAQAAAAAAAAAAAAAAAACAI+L/A+KPZ/a8q1vbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159c6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "find_nature((a_a2_data,a_a2_MH_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_graders_bias_handin(h_id):\n",
    "    print \"Observed\"\n",
    "    obs_scores = list()\n",
    "    for grader in a_a2_data.handins[h_id].graders:\n",
    "        val_ = a_a2_data.handins[h_id].get_grader_answers(grader.id)\n",
    "        if val_ != None:\n",
    "            obs_scores.extend(val_)\n",
    "    print np.mean(obs_scores)\n",
    "    print \"Gibbs\"\n",
    "    print np.mean(a_a2_gibbs_e_result['u_h'][h_id])\n",
    "    print \"bias\"\n",
    "    for grader in a_a2_data.handins[h_id].graders:\n",
    "        print np.mean(a_a2_gibbs_e_result['u_g'][grader.id])\n",
    "    print \"MH\"\n",
    "    print np.mean(a_a2_MH_result['u_h'][h_id])\n",
    "    print 'bias'\n",
    "    for grader in a_a2_data.handins[h_id].graders:\n",
    "        print np.mean(a_a2_MH_result['u_g'][grader.id])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kasper Emil Okkels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56be489fdde080000fe09ebe')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Emil Nilsson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56bf698813dc75000f71bd82')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Asger Malte Starup Stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56c0cd941505fb000f1e6893')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daniel Sollide Mouritzen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56bf78a613dc75000f71bd89')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mickey Exaudi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56c072e413dc75000f71be1a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Burim Abdulahi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print_graders_bias_handin('56c0be691505fb000f1e686c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c_l = list()\n",
    "print np.mean([np.mean(map(lambda y: y[1],x)) for x in a2_data.handins['56c06ccc13dc75000f71be15'].get_graders_answers().values()])\n",
    "print np.mean(a2_MH_result['u_h']['56c06ccc13dc75000f71be15'])\n",
    "for grader in a2_data.handins['56c06ccc13dc75000f71be15'].graders:\n",
    "    c_l.append(np.mean(a2_MH_result['u_g'][grader.id]))\n",
    "print np.mean(c_l)\n",
    "print np.mean(a2_gibbs_e_result['u_h']['56c06ccc13dc75000f71be15'])\n",
    "for grader in a2_data.handins['56c06ccc13dc75000f71be15'].graders:\n",
    "    c_l.append(np.mean(a2_gibbs_e_result['u_g'][grader.id]))\n",
    "print np.mean(c_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for grader in a2_data.handins['56c06ccc13dc75000f71be15'].graders:\n",
    "    print np.mean(a2_gibbs_e_result['u_g'][grader.id])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "sequence item 0: expected string, float found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-75-3e7e3cd2f9d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m','\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: sequence item 0: expected string, float found"
     ]
    }
   ],
   "source": [
    "','.join([0.1,0.2,0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}