Algorithms and Data Structures analysis.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": true
   },
   "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",
    "            \n",
    "        return graders_answers\n",
    "        \n",
    "    def get_grader_answers(self,grader):\n",
    "        grader_answers = list()\n",
    "        for key, values in self.graders.iteritems():\n",
    "            grader_answers.append((key,values[grader]))\n",
    "        return grader_answers\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",
    "\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": true
   },
   "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",
    "    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",
    "        # 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 = len(grader.handins)\n",
    "                sum_ = 0.0\n",
    "                for h in grader.handins:\n",
    "                    if g in h.gradeings[q].answers:\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 = np.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 = np.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": 7,
   "metadata": {
    "collapsed": true
   },
   "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": 8,
   "metadata": {
    "collapsed": true
   },
   "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(list)\n",
    "    trace_t_h = defaultdict(list)\n",
    "    trace_u_g = defaultdict(list)\n",
    "    trace_t_g = 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 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].append(u_h[h])\n",
    "                trace_t_h[h].append(t_h[h])\n",
    "            for g in data.graders.iterkeys():\n",
    "                trace_u_g[g].append(u_g[g])\n",
    "                trace_t_g[g].append(t_g[g])\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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def truncate(string,length):\n",
    "    return (string[:length] + '..') if len(string) > length else string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_question_bias(t,result):\n",
    "    for q in t.questions.iterkeys():\n",
    "        scores = list()\n",
    "        for id, g in t.graders.iteritems():\n",
    "            scores.append((user_name(ObjectId(id)),np.mean(result['B'][id][q]),np.std(result['B'][id][q])))\n",
    "\n",
    "        ax = plt.subplot(111)\n",
    "\n",
    "        scores.sort(key=lambda x:x[1])\n",
    "        model_1 = map(lambda x : float(x[1]),scores)\n",
    "        model_e = map(lambda x : float(x[2]),scores)\n",
    "\n",
    "        label = map(lambda x : x[0],scores)\n",
    "        y = xrange(0,len(scores))\n",
    "        plt.errorbar(model_1,y,xerr=model_e,fmt='or',label=\"Gibbs sampling\",alpha=0.65)\n",
    "\n",
    "        box = ax.get_position()\n",
    "        ax.set_position([box.x0, box.y0,\n",
    "                         box.width+0.45, box.height*5])\n",
    "        plt.yticks(xrange(0,len(scores)),label)\n",
    "        plt.legend(loc=4)\n",
    "        plt.title(\"Grader bias: \" + question_text(q))\n",
    "        plt.xlim(-0.5,0.5)\n",
    "        plt.vlines(0, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "        plt.ylabel(\"Grader\")\n",
    "        plt.xlabel(\"Bias\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_question_score(t,result):\n",
    "    for q in t.questions.iterkeys():\n",
    "        scores = list()\n",
    "        for h, handin in t.handins.iteritems():\n",
    "            scores.append((handin_students(h),np.mean(result['T'][h][q]),np.std(result['T'][h][q]),handin.answers[q].values()))\n",
    "\n",
    "        ax = plt.subplot(111)\n",
    "\n",
    "        scores.sort(key=lambda x:x[1])\n",
    "        model_1 = map(lambda x : float(x[1]),scores)\n",
    "        model_e = map(lambda x : float(x[2]),scores)\n",
    "        mean = map(lambda x : x[3],scores)\n",
    "\n",
    "        label = map(lambda x : x[0],scores)\n",
    "        y = xrange(0,len(scores))\n",
    "        plt.errorbar(model_1,y,xerr=model_e,fmt='or',label=\"Gibbs sampling\",alpha=0.7)\n",
    "        plt.boxplot(mean, positions=y, vert=False)\n",
    "\n",
    "        box = ax.get_position()\n",
    "        ax.set_position([box.x0, box.y0,\n",
    "                         box.width+0.45, box.height*4])\n",
    "        plt.yticks(xrange(0,len(scores)),label)\n",
    "        plt.legend(loc=4)\n",
    "        plt.title(question_text(q))\n",
    "        plt.xlim(-0.5,1.5)\n",
    "        plt.ylabel(\"Handin\")\n",
    "        plt.xlabel(\"Score\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_bias(t,result,nth=1):\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",
    "        q_bias = list()\n",
    "        for q in t.questions.iterkeys():\n",
    "            q_bias.append(result['B'][id][q])\n",
    "        scores.append((text,np.mean(result['u_g'][id]),np.sqrt(1.0 / np.mean(result['t_g'][id])),q_bias))\n",
    "    scores = scores[::nth]\n",
    "    ax = plt.subplot(111)\n",
    "\n",
    "    scores.sort(key=lambda x:x[1])\n",
    "    model_1 = map(lambda x : float(x[1]),scores)\n",
    "    xerr = map(lambda x : float(x[2]),scores)\n",
    "    bias_q = map(lambda x : x[3],scores)\n",
    "    \n",
    "    label = map(lambda x : x[0],scores)\n",
    "    y = xrange(0,len(scores))\n",
    "    plt.errorbar(model_1,y,xerr=xerr,fmt='o',color=\"#467821\",label=\"Latent Bias\")\n",
    "    plt.boxplot(bias_q, positions=y, vert=False)\n",
    "\n",
    "    plt.yticks(xrange(0,len(scores)),label)\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": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_handins(t,result,nth=1):\n",
    "    scores = list()\n",
    "    for id, handin in t.handins.iteritems():\n",
    "        gradings = list()\n",
    "        for x  in handin.get_graders_answers().values():\n",
    "            inter = list()\n",
    "            for (q,val) in x:\n",
    "                inter.append(val)\n",
    "            gradings.append(np.mean(inter))\n",
    "        l = truncate(handin_students(id),45)\n",
    "        scores.append((l,np.mean(result['u_h'][id]),np.sqrt(1.0 / np.mean(result['t_h'][id])),gradings))\n",
    "\n",
    "    ax = plt.subplot(111)\n",
    "\n",
    "    scores.sort(key=lambda x:x[1])\n",
    "    scores = scores[::nth]\n",
    "    model_1 = map(lambda x : float(x[1]),scores)\n",
    "    xerr = map(lambda x : x[2],scores)\n",
    "    mean = map(lambda x : x[3],scores)\n",
    "    \n",
    "    label = map(lambda x : x[0],scores)\n",
    "    y = xrange(0,len(scores))\n",
    "#    plt.plot(model_1,y,'o',color=\"#348ABD\",label=\"Latent Handin Score\",alpha=0.65)\n",
    "    plt.errorbar(model_1,y,xerr=xerr,fmt='o',color=\"#A60628\",label=\"Latent Handin Score\",alpha=0.65)\n",
    "#    plt.boxplot(mean, positions=y, vert=False)\n",
    "    \n",
    "    plt.yticks(xrange(0,len(scores)),label)\n",
    "    leg = plt.legend(numpoints=1,loc=4)\n",
    "    leg.get_frame().set_alpha(0.4)\n",
    "    plt.title(\"True score of handins\")\n",
    "    plt.vlines(0, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "    plt.vlines(1, 0, len(scores), color=\"k\", linestyles=\"--\", lw=1)\n",
    "    plt.ylabel(\"Question\")\n",
    "    plt.xlabel(\"Score\")\n",
    "#    plt.xlim(0,1)\n",
    "    plt.margins(0.02)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_collected_handin_score(t,result,nth=1):\n",
    "    scores = list()\n",
    "\n",
    "    for h, handin in t.handins.iteritems():\n",
    "        g_c = list()\n",
    "        h_list = list()\n",
    "        for q in t.questions.iterkeys():\n",
    "            l = list()\n",
    "            for answers in handin.gradeings[q].answers.itervalues():\n",
    "                l.append(answers)\n",
    "            h_list.append(np.mean(result['T'][h][q]))\n",
    "            g_c.append(np.mean(l))\n",
    "        obs_v = g_c\n",
    "        model_v = h_list\n",
    "        scores.append((h,model_v,obs_v,np.mean(result['u_h'][h])))\n",
    "    \n",
    "    ax = plt.subplot(111)\n",
    "\n",
    "    scores.sort(key=lambda x:x[3])\n",
    "    scores = scores[::nth]\n",
    "    \n",
    "    model = map(lambda x : x[1],scores)\n",
    "    obs = map(lambda x : x[2],scores)\n",
    "    obs_m = map(lambda x : np.mean(x[2]),scores)\n",
    "    found = map(lambda x : x[3],scores)\n",
    "\n",
    "    y = range(0,len(scores)*2,2)\n",
    "    for i in range(len(scores)):\n",
    "        y_ = y[i]\n",
    "        \n",
    "        plt.hlines(y_-1, -0.2, 1.2, color=\"k\", linestyles=\"-\", lw=1,alpha=0.5)\n",
    "        \n",
    "        if i == 0:\n",
    "            plt.plot(obs_m[i],y_+0.6,'o',color='#348ABD',label=\"Observed Handin Score\")\n",
    "            plt.plot(obs[i],[y_+0.2]*len(obs[i]),'|',color=\"#348ABD\", label=\"Observed Questions Scores\")\n",
    "            plt.plot(found[i],y_-0.2,'o',color=\"#A60628\",label=\"Found Handin Score\")\n",
    "            plt.plot(model[i],[y_-0.6]*len(model[i]),'|',color=\"#A60628\", label=\"Found Question Scores\")\n",
    "        plt.plot(obs_m[i],y_+0.6,'o',color=\"#348ABD\")\n",
    "        plt.plot(obs[i],[y_+0.2]*len(obs[i]),'|',color=\"#348ABD\")\n",
    "        plt.plot(found[i],y_-0.2,'o',color=\"#A60628\")\n",
    "        plt.plot(model[i],[y_-0.6]*len(model[i]),'|',color=\"#A60628\")\n",
    "        \n",
    "\n",
    "    label = map(lambda x : truncate(handin_students(x[0]),45),scores)\n",
    "    \n",
    "    plt.yticks(y,label)\n",
    "    leg = plt.legend(numpoints=1,loc=2)\n",
    "    leg.get_frame().set_alpha(0.4)\n",
    "    plt.xlim(-0.2,1.2)\n",
    "    plt.autoscale(tight=True)\n",
    "    plt.title(\"Score\")\n",
    "    plt.ylabel(\"Owners of the handins\")\n",
    "    plt.xlabel(\"Model score vs Question score vs Observed Question score\")\n",
    "    plt.margins(0.05)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56ab3b44002566000f6f34e8\n"
     ]
    }
   ],
   "source": [
    "a1 = data_model.Assignment.objects.get(title=\"Aflevering 1: Sjov med tabeller\")\n",
    "\n",
    "a1_data = fetch_assignment_data(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sxe+iXOUD32+v7p3Xv9H0L9IVa5rffrXKcfuv1f+e17/RiR88F7JVf8HA+6rfqn5/bTPb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109569a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 146.783564\n"
     ]
    }
   ],
   "source": [
    "a1_result_MH = MH_model(a1_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a1_result = a1_result_MH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 376.096545\n"
     ]
    }
   ],
   "source": [
    "a1_result = gibbs_model(a1_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5pX1OkEHw2vS10zyo2ltfwOpH9+5Wh/yrtNTSKioS9c3XK1/3gvUrXT0O8vU0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OGJRKqtF++FJRIwahRMn277sWPNZtQoGDbMJrEMGWJhno0X3ru3hQ0aBD17ehNeZjT/ZnQTWtv1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\niIiIiIiIiIiIiIiIiIiIiIiIiEge/D/+ecs3Knz+5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca2f7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"Algo_Ass1_Bias.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(8,8))\n",
    "    plot_bias(a1_data,a1_result,nth=15)\n",
    "    pdf.savefig(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hcZX+aADhizVvAH5Kcu34AJTs1h+pzq0FrOqusRsaAEwDTnbtc8CHbv9dBfrv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lAAAG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d14a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#with PdfPages(fig_store + \"Algo_Ass1_Handins.pdf\") as pdf:\n",
    "#fig = plt.figure(figsize(8,8))\n",
    "plot_handins(a1_data,a1_result_MH,nth=10)\n",
    "#    pdf.savefig(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QyEhvilZtMwxjBaAzyLEmYR552+iJkqm6Iy/yNqKVz65FEqZHoRXHPg6OK+QJv0skJbqS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\njQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5d2090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#with PdfPages(fig_store + \"Algo_Ass1_Handins.pdf\") as pdf:\n",
    "#fig = plt.figure(figsize(8,8))\n",
    "plot_handins(a1_data,a1_result,nth=10)\n",
    "#    pdf.savefig(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NZdu2bVy7do1evXoRHx/PunXryM7OZsqUKTg5ORXZLXa950gIIcStx+aAt9th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\nIiIiIiIiIiLV7v8BSJD7VSu3kbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b148890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"Algo_Ass1_Handins_Questions.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(8,8))\n",
    "    plot_collected_handin_score(a1_data,a1_result,nth=15)\n",
    "    pdf.savefig(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56bc9547ab0a12000f253e5b\n"
     ]
    }
   ],
   "source": [
    "a2 = data_model.Assignment.objects.get(title=\"Aflevering 2: Manglende tal\")\n",
    "\n",
    "a2_data = fetch_assignment_data(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 133.374382\n"
     ]
    }
   ],
   "source": [
    "a2_result = MH_model(a2_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56c5d7abab1c890015689251\n"
     ]
    }
   ],
   "source": [
    "a3 = data_model.Assignment.objects.get(title=\"Aflevering 3: Kompleksitet\")\n",
    "\n",
    "a3_data = fetch_assignment_data(a3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 151.174429\n"
     ]
    }
   ],
   "source": [
    "a3_result = MH_model(a3_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56d05d7d6e0834000f91fa10\n"
     ]
    }
   ],
   "source": [
    "a4 = data_model.Assignment.objects.get(title=\"Aflevering 4: Implementation af dobbelt-hægtede lister\")\n",
    "\n",
    "a4_data = fetch_assignment_data(a4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 out of 10000\n",
      "Wall time: 198.619181\n"
     ]
    }
   ],
   "source": [
    "a4_result = MH_model(a4_data,9000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56d987cead9a03001bb3b4eb\n"
     ]
    }
   ],
   "source": [
    "a5 = data_model.Assignment.objects.get(title=\"Aflevering 5: Hobe og tabeller\")\n",
    "\n",
    "a5_data = fetch_assignment_data(a5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 118.697989\n"
     ]
    }
   ],
   "source": [
    "a5_result = MH_model(a5_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56e2cb7b3a25980015a2a922\n"
     ]
    }
   ],
   "source": [
    "a6 = data_model.Assignment.objects.get(title=\"Aflevering 6: Dybdeførst søgning\")\n",
    "\n",
    "a6_data = fetch_assignment_data(a6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 31.856532\n"
     ]
    }
   ],
   "source": [
    "a6_result = MH_model(a6_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56eff76c326bb3000f4f5e2b\n"
     ]
    }
   ],
   "source": [
    "a7 = data_model.Assignment.objects.get(title=\"Aflevering 7: Sociale Netværk\")\n",
    "\n",
    "a7_data = fetch_assignment_data(a7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 53.040544\n"
     ]
    }
   ],
   "source": [
    "a7_result = MH_model(a7_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56eff6a0326bb3000f4f5e2a\n"
     ]
    }
   ],
   "source": [
    "a8 = data_model.Assignment.objects.get(title=\"Aflevering 8: Plantegning\")\n",
    "\n",
    "a8_data = fetch_assignment_data(a8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 48.557972\n"
     ]
    }
   ],
   "source": [
    "a8_result = MH_model(a8_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56eff7f4326bb3000f4f5e2c\n"
     ]
    }
   ],
   "source": [
    "a9 = data_model.Assignment.objects.get(title=\"Aflevering 9: Kruskals algoritme\")\n",
    "\n",
    "a9_data = fetch_assignment_data(a9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 out of 6000\n",
      "Wall time: 18.247347\n"
     ]
    }
   ],
   "source": [
    "a9_result = MH_model(a9_data,5000,burn_in=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_data = [(a1_data,a1_result),(a2_data,a2_result),(a3_data,a3_result),(a4_data,a4_result),\n",
    "            (a5_data,a5_result),(a6_data,a6_result),(a7_data,a7_result),(a8_data,a8_result),(a9_data,a9_result)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "221 253 5 2.60869565217 2.98642533937\n",
      "196 212 8 2.75 2.97448979592\n",
      "222 235 7 2.8085106383 2.97297297297\n",
      "166 182 3 2.72527472527 2.98795180723\n",
      "179 202 4 2.62871287129 2.96648044693\n",
      "48 56 2 2.57142857143 3.0\n",
      "80 95 5 2.50526315789 2.975\n",
      "73 88 5 2.45454545455 2.95890410959\n",
      "28 32 2 2.59375 2.96428571429\n"
     ]
    }
   ],
   "source": [
    "for (ass_data, ass_result) in all_data:\n",
    "    print len(ass_data.graders),  len(ass_data.handins), len(ass_data.questions), \n",
    "    print np.mean([len(x.graders) for x in ass_data.handins.values()]),\n",
    "    print np.mean([len(x.handins) for x in ass_data.graders.values()])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "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((np.mean(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[2::nth]\n",
    "    \n",
    "    ax = plt.subplot(111)\n",
    "    \n",
    "    plotted = dict()\n",
    "    p_style = {1:'v',2:'.',3:'<',4:'d',5:'8',6:'s',7:'x',8:'*',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",
    "    plt.legend(loc=4)\n",
    "    plt.title(\"Bias over Assignments\")\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": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W7u8ByKvxZAXXMAzDMAKEQiG6n9iTe297iAt7X0Xx6vpsWrc1M8qX7EP4qt6+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\n7/VAJWh/jsrhnorCniYBd7ltxiLDNB+t7G+iJiHkwfgJMlQ8lgMD3T6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FlDtb9ipzmzq3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\ny2MWcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df08a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with PdfPages(fig_store + \"Algo_Ass_Bias.pdf\") as pdf:\n",
    "    fig = plt.figure(figsize(10,8))\n",
    "    plot_all_grader_bias(all_data,nth=7)\n",
    "    pdf.savefig(fig,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.26899737986131961"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a4_result['u_g']['56979e4d700960000f17075f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_question_hist((ass_data,ass_result),name_,plot = True,question = None, question_contains = None):\n",
    "    '''\n",
    "    Plots the questions value and the graders values for each handin the person have graded\n",
    "    '''\n",
    "    u_id = user_id(name_)\n",
    "\n",
    "    bar_width = 0.2\n",
    "    \n",
    "    tot_neg = 0\n",
    "    tot_pos = 0\n",
    "    tot_net = 0\n",
    "    tot_n = 0\n",
    "    \n",
    "    if question:\n",
    "        print \"Question: %s\" % question_text(question)\n",
    "    \n",
    "    # Find the handins that the person have graded    \n",
    "    for handin in ass_data.graders[str(u_id)].handins:\n",
    "        # find the values of each question in each hadnin\n",
    "        print \"Handin: %s\" % handin.id\n",
    "        \n",
    "        neutral = 0\n",
    "        positive = 0\n",
    "        negative = 0\n",
    "        \n",
    "        n_q = 0\n",
    "        \n",
    "        for q in ass_data.questions.iterkeys():\n",
    "            if question and q <> question:\n",
    "                continue\n",
    "            if question_contains:\n",
    "                if question_contains not in question_text(q):\n",
    "                    continue\n",
    "                else:\n",
    "                    print question_text(q)\n",
    "                    \n",
    "            tot_n = tot_n + 1 \n",
    "            n_q = n_q + 1 \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",
    "            g_q_val = int(handin.gradeings[q].answers[str(u_id)]*q_max)\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",
    "                negative = negative + 1\n",
    "                if plot:\n",
    "                    print \"[Negative]\",\n",
    "            elif np.median(q_values) + 0.5 < g_q_val:\n",
    "                tot_pos = tot_pos + 1\n",
    "                positive = positive + 1\n",
    "                if plot:\n",
    "                    print \"[Positive]\",\n",
    "            else:\n",
    "                tot_net = tot_net + 1\n",
    "                neutral = neutral + 1\n",
    "                if plot:\n",
    "                    print \"[Neutral]\",\n",
    "\n",
    "                    \n",
    "            if plot:\n",
    "                print \"Median: %f\" % np.median(q_values),\n",
    "                print \"Given score: %i\" % g_q_val\n",
    "\n",
    "                ax = plt.subplot(111) \n",
    "\n",
    "                bar = plt.bar(range(0,q_max+1),val,width=bar_width,alpha = 0.65, linewidth=0,align='center',color='gray')\n",
    "                bar[g_q_val].set_color('r')\n",
    "                plt.xticks(range(0,q_max+1),range(0,q_max+1))\n",
    "                box = ax.get_position()\n",
    "                ax.set_position([box.x0, box.y0,\n",
    "                                 box.width*2, box.height*1])\n",
    "                plt.legend(loc=4)\n",
    "                plt.ylim(0,max(val)+1)\n",
    "                plt.title(question_text(q))\n",
    "                plt.xlabel(\"value\")\n",
    "                plt.show()\n",
    "\n",
    "        result = [(\"Positive\",positive),(\"Neutral\",neutral),(\"Negative\",negative)]\n",
    "        for (l,v) in result:\n",
    "            print str(v) + \" \" + l + \" (%.1f%%)\" % ((float(v)/float(n_q))*100.0),\n",
    "        if not question or not question_contains:\n",
    "            print \"out of %i\" % n_q\n",
    "        print\n",
    "    \n",
    "    if not question or not question_contains:\n",
    "        print \"Total result:\"\n",
    "        result = [(\"Positive\",tot_pos),(\"Neutral\",tot_net),(\"Negative\",tot_neg)]\n",
    "        for (l,v) in result:\n",
    "            print str(v) + \" \" + l + \" (%.1f%%)\" % ((float(v)/float(tot_n))*100.0),\n",
    "        print \"out of %i\" % tot_n\n",
    "    print "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "collected_ass_data = [(a1_data,a1_result),(a2_data,a2_result),(a3_data,a3_result),(a4_data,a4_result),\n",
    "                     (a5_data,a5_result),(a6_data,a6_result),(a7_data,a7_result),(a8_data,a8_result),\n",
    "                     (a9_data,a9_result)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Handin: 56dc09894a4bed000f327ec9\n",
      "0 Positive (0.0%) 0 Neutral (0.0%) 3 Negative (100.0%) out of 3\n",
      "\n",
      "Handin: 56d813204399280015d24fc4\n",
      "0 Positive (0.0%) 2 Neutral (66.7%) 1 Negative (33.3%) out of 3\n",
      "\n",
      "Handin: 56dc30ab4a4bed000f328145\n",
      "0 Positive (0.0%) 1 Neutral (33.3%) 2 Negative (66.7%) out of 3\n",
      "\n",
      "Total result:\n",
      "0 Positive (0.0%) 3 Neutral (33.3%) 6 Negative (66.7%) out of 9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "plot_question_hist((a4_data,a4_result),u'Valdemar Carøe',plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId('56979e4d700960000f17075f')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_id('Valdemar Carøe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_grader_answers(handin,g):\n",
    "        grader_g = list()\n",
    "        for answers in handin.gradeings.itervalues():\n",
    "            if g not in answers.answers:\n",
    "                return None\n",
    "            grader_g.append(answers.answers[g])\n",
    "        return grader_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simon Heimdal\n",
      "Valdemar Carøe\n",
      "0.277777777778 -0.269410069148\n",
      "Emre Kayhan\n",
      "0.888888888889 0.199855165327\n",
      "Michael Sylvest Christensen\n",
      "1.0 0.138085665687\n",
      "Observed mean\n",
      "0.722222222222\n",
      "Model hand_in score\n",
      "0.682441515357\n",
      "\n",
      "Martin Dalby\n",
      "Valdemar Carøe\n",
      "0.555555555556 -0.269410069148\n",
      "Simon Koimaru Skaarup\n",
      "0.888888888889 -0.0269101758808\n",
      "Mustafa Sidiqi\n",
      "0.888888888889 0.0694722959939\n",
      "Observed mean\n",
      "0.777777777778\n",
      "Model hand_in score\n",
      "0.835457639576\n",
      "\n",
      "Alexander Wichmann\n",
      "Valdemar Carøe\n",
      "0.277777777778 -0.269410069148\n",
      "Shehip Ramadani\n",
      "1.0 0.283021590132\n",
      "Mikael Staugaard Mejling\n",
      "0.777777777778 0.0748659013419\n",
      "Observed mean\n",
      "0.685185185185\n",
      "Model hand_in score\n",
      "0.642551002588\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for handin in a4_data.graders['56979e4d700960000f17075f'].handins:\n",
    "    print handin_students(handin.id)\n",
    "    values = list()\n",
    "    for grader in handin.graders:\n",
    "        print user_name(grader.id)\n",
    "        print np.mean(get_grader_answers(handin,grader.id)), np.mean(a4_result['u_g'][grader.id])\n",
    "        values.append(np.mean(get_grader_answers(handin,grader.id)))\n",
    "    print \"Observed mean\"\n",
    "    print np.mean(values)\n",
    "    print \"Model hand_in score\"\n",
    "    print np.mean(a4_result['u_h'][handin.id])\n",
    "    print "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def find_nature((ass_data,ass_result)):\n",
    "    \n",
    "    tot_neg = 0\n",
    "    tot_pos = 0\n",
    "    tot_net = 0\n",
    "    tot_n = 0\n",
    "    \n",
    "    for u_id, grader in ass_data.graders.iteritems():\n",
    "    # Find the handins that the person have graded    \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",
    "    \n",
    "    print \"Total result:\"\n",
    "    result = sorted([(\"Positive\",tot_pos),(\"Neutral\",tot_net),(\"Negative\",tot_neg)],key=lambda x : x[1], reverse=True)\n",
    "    for (l,v) in result:\n",
    "        print str(v) + \" \" + l + \" (%.1f%%)\" % ((float(v)/float(tot_n))*100.0),\n",
    "    print \"out of %i\" % tot_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total result:\n",
      "3866 Neutral (82.9%) 461 Negative (9.9%) 337 Positive (7.2%) out of 4664\n"
     ]
    }
   ],
   "source": [
    "find_nature((a2_data,a2_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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