automated_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "12ea6089",
   "metadata": {
    "id": "12ea6089"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import entropy\n",
    "import numpy as np\n",
    "\n",
    "def calculate_relative_entropy(motif, predictedmotif):\n",
    "  motif_PWM = open(motif, 'r')\n",
    "  p_motif_PWM = open(predictedmotif, 'r')\n",
    "\n",
    "  motif_header = motif_PWM.readline() \n",
    "  p_motif_header = p_motif_PWM.readline() \n",
    "  temp = motif_header.split()\n",
    "  number_of_rows = int(temp[1]) \n",
    "\n",
    "  PWM_array = np.zeros((number_of_rows, 4))\n",
    "  pred_PWM_array = np.zeros((number_of_rows, 4))\n",
    "\n",
    "  index = 0\n",
    "  entropy_array = []\n",
    "\n",
    "  while (True):\n",
    "    line = motif_PWM.readline()\n",
    "    line = line.split()\n",
    "    p_line = p_motif_PWM.readline()\n",
    "    p_line = p_line.split()\n",
    "\n",
    "    if (line[0] == \"<\"):\n",
    "      break\n",
    "\n",
    "    for i in range(4):\n",
    "      if (float(line[i]) == 0.0):\n",
    "        PWM_array[index][i] = 1.0e-16\n",
    "      else:\n",
    "        PWM_array[index][i] = float(line[i])\n",
    "        \n",
    "      if (float(p_line[i]) == 0.0):\n",
    "        pred_PWM_array[index][i] = 1.0e-16\n",
    "      else:\n",
    "        pred_PWM_array[index][i] = float(p_line[i])\n",
    "    \n",
    "    entropy_array.append(entropy(pred_PWM_array[index][:], qk=PWM_array[index][:], base=2, axis=0))\n",
    "    index += 1    \n",
    "\n",
    "  motif_PWM.close()\n",
    "  p_motif_PWM.close()\n",
    "  return entropy_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6af9dbba",
   "metadata": {
    "id": "6af9dbba"
   },
   "outputs": [],
   "source": [
    "def calculate_result_overlap(sites, predictedsites, motif_length):\n",
    "  length_handle = open(motif_length, 'r')\n",
    "  temp = length_handle.readline().split() \n",
    "  length = int(temp[0]) \n",
    "\n",
    "  sites_list = []\n",
    "  predictedsites_list = []\n",
    "\n",
    "  with open(sites) as sites_handle:\n",
    "    for line1 in sites_handle:\n",
    "        sites_list.append(int(line1))\n",
    "\n",
    "  with open(predictedsites) as predictedsites_handle:\n",
    "    for line2 in predictedsites_handle:\n",
    "        predictedsites_list.append(int(line2))\n",
    "\n",
    "  list_length = len(sites_list) \n",
    "  number_perfect_matches = 0\n",
    "  best_case_overlapping_pos = length * list_length \n",
    "  overlap_counter = 0\n",
    "\n",
    "  for i in range(list_length):\n",
    "    site_i = sites_list[i]\n",
    "    p_site_i = predictedsites_list[i]\n",
    "\n",
    "    if (site_i == p_site_i):\n",
    "      number_perfect_matches += 1\n",
    "      overlap_counter += length\n",
    "    else:\n",
    "      position_diff = abs(site_i - p_site_i)\n",
    "      overlap_length = length - position_diff\n",
    "\n",
    "      if (overlap_length > 0):\n",
    "        overlap_counter += overlap_length\n",
    "  \n",
    "  overlap_ratio = overlap_counter / best_case_overlapping_pos\n",
    "\n",
    "  #Uncomment 1 of the 2 print statements below according to your needs\n",
    "\n",
    "  print(\"{0}/{1} perfect matches, ratio of overlap = {2}\".format(number_perfect_matches, list_length, overlap_ratio))\n",
    "  #print(overlap_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "984112aa",
   "metadata": {
    "id": "984112aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1683.0079259\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "def organize(filename):\n",
    "    file_handle = open(filename, 'r')\n",
    "    dict = {}\n",
    "    i = 0\n",
    "    while (i < 100):\n",
    "        i += 1\n",
    "        data_line = file_handle.readline()\n",
    "        temp = data_line.split()\n",
    "        dict[temp[0]] = temp[3]\n",
    "\n",
    "    j = 1\n",
    "    while (j < 101):\n",
    "        current_file = \"data_set_\"+str(j)\n",
    "        print(dict[current_file])\n",
    "        j += 1\n",
    "        \n",
    "organize(\"run2.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c4864e0",
   "metadata": {
    "id": "9c4864e0",
    "outputId": "eaed6529-1964-4927-df4c-f56c8e3a7892"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Average = 3.9089821087517236, Sum = 31.27185687001379, Raw Values = [1.7119878779447968, 1.7119878779447966, 5.310576718340614, 5.310576718340614, 5.310576718340611, 2.2659481168757956, 5.310576718340614, 4.3396261238859495]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
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      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 31.5773917535296, Sum = 189.4643505211776, Raw Values = [0.0, 53.15084951819778, 0.0, 41.363899965111194, 53.150849518197774, 41.798751519670866]\n",
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      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
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      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
      "Average = 0.0, Sum = 0.0, Raw Values = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "# An automated way to calculate relative entropy for all 100 datasets\n",
    "\n",
    "for i in range(1, 101):\n",
    "    current_folder = \"data_set_\"+str(i)\n",
    "    raw_val = calculate_relative_entropy(current_folder+'/motif.txt', current_folder+'/predictedmotif.txt')\n",
    "    sum = 0\n",
    "    length = len(raw_val)\n",
    "    for v in raw_val:\n",
    "        sum += v\n",
    "    average = sum / length\n",
    "    \n",
    "    #Uncomment 1 of the 4 print statements below according to your needs\n",
    "    \n",
    "    print(\"Average = {0}, Sum = {1}, Raw Values = {2}\".format(average, sum, raw_val))\n",
    "    #print(sum)\n",
    "    #print(average)\n",
    "    #print(raw_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f1509b1",
   "metadata": {
    "id": "1f1509b1",
    "outputId": "ef56b95c-ca1a-43e9-ba7d-4b6f8e33e56a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "0/10 perfect matches, ratio of overlap = 0.0875\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "0/10 perfect matches, ratio of overlap = 0.025\n",
      "3/10 perfect matches, ratio of overlap = 0.475\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "6/10 perfect matches, ratio of overlap = 0.6\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "7/10 perfect matches, ratio of overlap = 0.7\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "7/10 perfect matches, ratio of overlap = 0.7\n",
      "0/10 perfect matches, ratio of overlap = 0.0\n",
      "9/10 perfect matches, ratio of overlap = 0.9\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "0/10 perfect matches, ratio of overlap = 0.7714285714285715\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "5/5 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "10/10 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "19/20 perfect matches, ratio of overlap = 0.95\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n",
      "20/20 perfect matches, ratio of overlap = 1.0\n"
     ]
    }
   ],
   "source": [
    "# An automated way to calculate overlap for all 100 datasets\n",
    "\n",
    "for i in range(1, 101):\n",
    "    current_folder = \"data_set_\"+str(i)\n",
    "    calculate_result_overlap(current_folder+'/sites.txt', current_folder+'/predictedsites.txt', current_folder+'/motiflength.txt') # This will print everything out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "927e287f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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