src/jupyter/1-notebooks/0_Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "runFile = \"cal2.run\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading ../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr1.json\n",
      "Reading ../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr2.json\n",
      "Reading ../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr3.json\n",
      "Reading ../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr4.json\n",
      "Reading ../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr5.json\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC IS 2020 Evaluation Script\n",
    "# Configured for 2020-A Events\n",
    "# Task 1\n",
    "# Used to evaluate TREC-IS runs\n",
    "# --------------------------------------------------\n",
    "version = 2.4 # Notebook Version Number\n",
    "\n",
    "import os\n",
    "cwd = os.getcwd()\n",
    "\n",
    "\n",
    "# System output file to evaluate:\n",
    "runName = \"calamityrun\"\n",
    "\n",
    "# Do we try and normalize the run priority scores?\n",
    "enablePriorityNorm = False\n",
    "\n",
    "# The location of the ground truth data against which to compare the run\n",
    "classificationLabelFiles = [\n",
    "    \"../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr1.json\",\n",
    "    \"../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr2.json\",\n",
    "    \"../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr3.json\",\n",
    "    \"../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr4.json\",\n",
    "    \"../../../data/input/raw/data/2020/2020-A/ground-truth-set/TRECIS2020A-t12-assr5.json\"\n",
    "]\n",
    "classificationLabelFiles = [x for x in classificationLabelFiles]\n",
    "\n",
    "# The location of the ontology file\n",
    "ontologyFile = \"../../../data/input/raw/data/2020/2020-A/types/TRECIS-2020-ITypes-Task1.json\"\n",
    "#TRECIS-2020-ITypes-Task1.json\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Static data for the 2019 edition\n",
    "# --------------------------------------------------\n",
    "# Identifiers for the test events\n",
    "eventIdentifiers = [\n",
    "    \"athensEarthquake2020\",\n",
    "    \"baltimoreFlashFlood2020\",\n",
    "    \"brooklynBlockPartyShooting2020\",\n",
    "    \"daytonOhioShooting2020\",\n",
    "    \"elPasoWalmartShooting2020\",\n",
    "    \"gilroygarlicShooting2020\",\n",
    "    \"hurricaneBarry2020\",\n",
    "    \"indonesiaEarthquake2020\",\n",
    "    \"keralaFloods2020\",\n",
    "    \"myanmarFloods2020\",\n",
    "    \"papuaNewguineaEarthquake2020\",\n",
    "    \"siberianWildfires2020\",\n",
    "    \"typhoonKrosa2020\",\n",
    "    \"typhoonLekima2020\",\n",
    "    \"whaleyBridgeCollapse2020\"\n",
    "]\n",
    "\n",
    "eventidTopicidMap =\t{\n",
    "  \"athensEarthquake2020\": \"TRECIS-CTIT-H-Test-035\",\n",
    "  \"baltimoreFlashFlood2020\": \"TRECIS-CTIT-H-Test-036\",\n",
    "  \"brooklynBlockPartyShooting2020\": \"TRECIS-CTIT-H-Test-037\",\n",
    "  \"daytonOhioShooting2020\": \"TRECIS-CTIT-H-Test-038\",\n",
    "  \"elPasoWalmartShooting2020\": \"TRECIS-CTIT-H-Test-039\",\n",
    "  \"gilroygarlicShooting2020\": \"TRECIS-CTIT-H-Test-040\",\n",
    "  \"hurricaneBarry2020\": \"TRECIS-CTIT-H-Test-041\",\n",
    "  \"indonesiaEarthquake2020\": \"TRECIS-CTIT-H-Test-042\",\n",
    "  \"keralaFloods2020\": \"TRECIS-CTIT-H-Test-043\",\n",
    "  \"myanmarFloods2020\": \"TRECIS-CTIT-H-Test-044\",\n",
    "  \"papuaNewguineaEarthquake2020\": \"TRECIS-CTIT-H-Test-045\",\n",
    "  \"siberianWildfires2020\": \"TRECIS-CTIT-H-Test-046\",\n",
    "  \"typhoonKrosa2020\": \"TRECIS-CTIT-H-Test-047\",\n",
    "  \"typhoonLekima2020\": \"TRECIS-CTIT-H-Test-048\",\n",
    "  \"whaleyBridgeCollapse2020\": \"TRECIS-CTIT-H-Test-049\"\n",
    "}\n",
    "\n",
    "# What we consider to be highly important categories of information\n",
    "highImportCategories = [\n",
    "    \"Request-GoodsServices\",\n",
    "    \"Request-SearchAndRescue\",\n",
    "    \"CallToAction-MovePeople\",\n",
    "    \"Report-EmergingThreats\",\n",
    "    \"Report-NewSubEvent\",\n",
    "    \"Report-ServiceAvailable\"\n",
    "]\n",
    "\n",
    "highImportCategoriesShort = [\n",
    "    \"GoodsServices\",\n",
    "    \"SearchAndRescue\",\n",
    "    \"MovePeople\",\n",
    "    \"EmergingThreats\",\n",
    "    \"NewSubEvent\",\n",
    "    \"ServiceAvailable\"\n",
    "]\n",
    "\n",
    "# Parameters\n",
    "var_lambda = 0.75 # weight to place on actionable information categories in comparison to non actionable categoriee\n",
    "var_alpha = 0.3 # Flat gain for providing a correct alert, regardless of the categories selected\n",
    "\n",
    "resultsFile = open(runName+\".results.task1.overall.txt\",\"w+\")\n",
    "resultsFile.write(\"TREC-IS 2020-A Task 1 Notebook Evaluator v\"+str(version)+\"\\n\")\n",
    "resultsFile.write(\"Run: \"+runName+\" (\"+runFile+\")\"+\"\\n\")\n",
    "resultsFile.write(\"\"+\"\\n\")\n",
    "\n",
    "perTopicFile = open(runName+\".results.task1.pertopic.txt\",\"w+\")\n",
    "perTopicFile.write(\"TREC-IS 2020-A Task 1 Notebook Evaluator v\"+str(version)+\"\\n\")\n",
    "perTopicFile.write(\"Run: \"+runName+\" (\"+runFile+\")\"+\"\\n\")\n",
    "perTopicFile.write(\"\"+\"\\n\")\n",
    "\n",
    "perEventFile = open(runName+\".results.task1.perevent.txt\",\"w+\")\n",
    "perEventFile.write(\"TREC-IS 2020-A Task 1 Notebook Evaluator v\"+str(version)+\"\\n\")\n",
    "perEventFile.write(\"Run: \"+runName+\" (\"+runFile+\")\"+\"\\n\")\n",
    "perEventFile.write(\"\"+\"\\n\")\n",
    "\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Processing Starts Here\n",
    "# --------------------------------------------------\n",
    "import json\n",
    "from pprint import pprint\n",
    "import gzip\n",
    "import math\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Stage 1: Load the ground truth dataset \n",
    "# --------------------------------------------------\n",
    "\n",
    "groundtruthJSON = []\n",
    "for groundtruthFile in classificationLabelFiles:\n",
    "    print(\"Reading \"+groundtruthFile)\n",
    "    with open(groundtruthFile, encoding='iso-8859-1') as groundtruthJSONFile:    \n",
    "        groundtruthJSON.append(json.load(groundtruthJSONFile))\n",
    "#pprint(groundtruthJSON[\"events\"])\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Stage 2: Load run file \n",
    "# --------------------------------------------------\n",
    "with open(runFile, encoding='utf-8') as openRunFile:\n",
    "    runContents = openRunFile.readlines() # lines not yet decoded\n",
    "#pprint(runContents[0])\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Stage 3: Load the categories \n",
    "# --------------------------------------------------\n",
    "with open(ontologyFile, encoding='utf-8') as ontologyJSONFile:    \n",
    "    ontologyJSON = json.load(ontologyJSONFile)\n",
    "\n",
    "informationTypes2Index = {} # category -> numerical index\n",
    "informationTypesShort2Index = {} # category short form (e.g. Report-EmergingThreats vs. EmergingThreats) -> numerical index\n",
    "informationTypeIndex = 0\n",
    "for informationTypeJSON in ontologyJSON[\"informationTypes\"]:\n",
    "    informationTypeId = informationTypeJSON[\"id\"]\n",
    "    informationTypes2Index[informationTypeId] = informationTypeIndex\n",
    "    informationTypesShort2Index[informationTypeId.split(\"-\")[1]] = informationTypeIndex\n",
    "    informationTypeIndex = informationTypeIndex + 1\n",
    "    \n",
    "# -----------------------------------------------------------\n",
    "# Stage 4: Produce ground truth maps between tweetIds and categories\n",
    "# -----------------------------------------------------------\n",
    "# Notes: Ground truth is used as a base, if a run includes tweets\n",
    "#        not in the ground truth they will be ignored\n",
    "# Assumptions: A tweet will not be returned for multiple events\n",
    "\n",
    "tweetId2TRECInfoCategories = {} # tweet id -> Array of categories selected by assessors\n",
    "tweetId2TRECHighImportInfoCategories = {} # tweet id -> Array of categories selected by assessors\n",
    "tweetId2TRECLowImportInfoCategories = {} # tweet id -> Array of categories selected by assessors\n",
    "tweetId2TRECPriorityCategory = {} # tweet id -> priority label (Critical,High,Medium,Low)\n",
    "index2TweetId = {} # ordered tweets\n",
    "event2tweetIds = {} # event -> tweet ids for tweets within that event\n",
    "countHighCriticalImport = 0\n",
    "countLowMediumImport = 0\n",
    "\n",
    "tweetIndex = 0\n",
    "for groundtruth in groundtruthJSON:\n",
    "    for eventJSON in groundtruth[\"events\"]:\n",
    "        eventid = eventJSON[\"eventid\"]\n",
    "        #print(eventid)\n",
    "        # two events were split and assessed in parts, re-name these so they are correctly read\n",
    "        if eventid.endswith(\"A\") | eventid.endswith(\"B\") | eventid.endswith(\"C\") | eventid.endswith(\"D\") | eventid.endswith(\"E\"):\n",
    "            eventid = eventid[:-1]\n",
    "        \n",
    "        if not event2tweetIds.get(eventid):\n",
    "            event2tweetIds[eventid] = []\n",
    "        \n",
    "        if any(eventid in s for s in eventIdentifiers):\n",
    "            # iterate over tweets in the event\n",
    "            for tweetJSON in eventJSON[\"tweets\"]:\n",
    "                tweetid = tweetJSON[\"postID\"]\n",
    "                categories = tweetJSON[\"categories\"]\n",
    "                priority = tweetJSON[\"priority\"]\n",
    "                \n",
    "                if priority == \"High\" or priority == \"Critical\":\n",
    "                    countHighCriticalImport = countHighCriticalImport + 1\n",
    "                \n",
    "                if priority == \"Low\" or priority == \"Medium\":\n",
    "                    countLowMediumImport = countLowMediumImport + 1\n",
    "                \n",
    "                event2tweetIds[eventid].append(tweetid)\n",
    "                \n",
    "                # check categories for name issues and correct if possible\n",
    "                cleanedCategories = []\n",
    "                highImportCats = []\n",
    "                lowImportCats = []\n",
    "                for categoryId in categories:\n",
    "                    if not any(categoryId in s for s in informationTypesShort2Index.keys()):\n",
    "                        print(\"Found unknown category \"+categoryId)\n",
    "                    else:\n",
    "                        cleanedCategories.append(categoryId)\n",
    "                        if any(categoryId in s for s in highImportCategoriesShort):\n",
    "                            highImportCats.append(categoryId)\n",
    "                        else:\n",
    "                            lowImportCats.append(categoryId)\n",
    "    \n",
    "                tweetId2TRECInfoCategories[tweetid] = cleanedCategories\n",
    "                tweetId2TRECHighImportInfoCategories[tweetid] = highImportCats\n",
    "                tweetId2TRECLowImportInfoCategories[tweetid] = lowImportCats\n",
    "                tweetId2TRECPriorityCategory[tweetid] = priority\n",
    "                index2TweetId[tweetIndex] = tweetid;\n",
    "                tweetIndex = tweetIndex + 1\n",
    "\n",
    "                \n",
    "        else:\n",
    "            print(\"WARN: Found ground truth data for event not in the 2019 topic set \"+eventid+\", ignoring...\")\n",
    "        \n",
    "# -----------------------------------------------------------\n",
    "# Stage 5: Produce run predicted maps between tweetIds and categories\n",
    "# -----------------------------------------------------------\n",
    "tweetId2RunInfoCategories = {} # tweet id -> predicted category by participant system\n",
    "tweetId2RunHighImportInfoCategories = {} # tweet id -> predicted category by participant system\n",
    "tweetId2RunLowImportInfoCategories = {} # tweet id -> predicted category by participant system\n",
    "tweetId2RunPriorityScore = {} # tweet id -> importance score from participant system\n",
    "tweetId2RunPriorityScoreNorm = {} # tweet id -> importance score from participant system\n",
    "event2TweetIdRank = {} # event -> (rank,tweetid)\n",
    "\n",
    "maxPrediction = -999999\n",
    "minPrediction = 999999\n",
    "\n",
    "for runLine in runContents:\n",
    "    \n",
    "    for key in eventidTopicidMap:\n",
    "          runLine = runLine.replace(key, eventidTopicidMap[key])  \n",
    "    \n",
    "    #print(runLine)\n",
    "    \n",
    "    predictionParts = runLine.strip().replace(\", \", \",\").replace(\"\\\",\\\" \", \"\\\",\\\"\").replace(\" \",\"\\t\").replace(\"'\",\"\\\"\").split('\\t')\n",
    "    \n",
    "    #print(runLine)\n",
    "    if (len(predictionParts)<6 ):\n",
    "        print(runLine)\n",
    "        continue\n",
    "    else:\n",
    "        eventId = predictionParts[0]\n",
    "        tweetId = predictionParts[2]\n",
    "        rank = float(predictionParts[3])\n",
    "        #print(predictionParts[5])\n",
    "        categories = json.loads(predictionParts[5])\n",
    "        priority = predictionParts[4]\n",
    "        \n",
    "        tweetId2RunInfoCategories[tweetId] = categories\n",
    "        tweetId2RunPriorityScore[tweetId] = priority\n",
    "        \n",
    "        if not event2TweetIdRank.get(eventId):\n",
    "            event2TweetIdRank[eventId] = []\n",
    "        rankTuple = (tweetId,rank)\n",
    "        event2TweetIdRank.get(eventId).append(rankTuple)\n",
    "        \n",
    "        highImportCats = []\n",
    "        lowImportCats = []\n",
    "        for categoryId in categories:\n",
    "            cleanedCategories.append(categoryId)\n",
    "            if any(categoryId in s for s in highImportCategories):\n",
    "                highImportCats.append(categoryId)\n",
    "            else:\n",
    "                lowImportCats.append(categoryId)\n",
    "                \n",
    "        tweetId2RunHighImportInfoCategories[tweetId] = highImportCats\n",
    "        tweetId2RunLowImportInfoCategories[tweetId] = lowImportCats\n",
    "\n",
    "for eventId in event2TweetIdRank.keys():\n",
    "    tweetsSorted = sorted(event2TweetIdRank.get(eventId), key=lambda tup: tup[1])\n",
    "    event2TweetIdRank[eventId] = tweetsSorted\n",
    "    \n",
    "for i in range(len(index2TweetId)):\n",
    "    tweetId = index2TweetId[i]\n",
    "    if tweetId2RunPriorityScore.get(tweetId):\n",
    "        \n",
    "        # we used to do score normalization here, but no longer as priorites are now\n",
    "        # expressed by participants as categories\n",
    "        tweetId2RunPriorityScoreNorm[tweetId] = tweetId2RunPriorityScore.get(tweetId)\n",
    "        \n",
    "    else:\n",
    "        tweetId2RunPriorityScoreNorm[tweetId] = \"Low\"\n",
    "    \n",
    "# --------------------------------------------------\n",
    "# Stage 6: Create ground truth vectors per category\n",
    "# --------------------------------------------------\n",
    "\n",
    "category2GroundTruth = {} # category -> tweet vector with binary 1 vs all ground truth category labels\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    categoryIdShort = categoryId.split(\"-\")[1]\n",
    "    categoryVector = []\n",
    "    for i in range(len(index2TweetId)):\n",
    "        tweetId = index2TweetId[i]\n",
    "        categories = tweetId2TRECInfoCategories.get(tweetId)\n",
    "        #pprint(categories)\n",
    "        if any(categoryIdShort in s for s in categories):\n",
    "            categoryVector.append(1)\n",
    "        else:\n",
    "            categoryVector.append(0)\n",
    "    category2GroundTruth[categoryId] = categoryVector\n",
    "            \n",
    "#pprint(category2GroundTruth)\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Stage 7: Create run vectors per category \n",
    "# --------------------------------------------------\n",
    "# Assumptions: If run misses a tweet, we assume it has\n",
    "#              no categories\n",
    "category2Predicted = {} # category -> tweet vector with binary 1 vs all predicted by system labels\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    categoryIdShort = categoryId.split(\"-\")[1]\n",
    "    categoryVector = []\n",
    "    for i in range(len(index2TweetId)):\n",
    "        tweetId = index2TweetId[i]\n",
    "        \n",
    "        if tweetId2RunInfoCategories.get(tweetId):\n",
    "            categories = tweetId2RunInfoCategories.get(tweetId)\n",
    "            if any(categoryIdShort in s for s in categories):\n",
    "                categoryVector.append(1)\n",
    "            else:\n",
    "                categoryVector.append(0)\n",
    "        else:\n",
    "            categoryVector.append(0)\n",
    "\n",
    "    category2Predicted[categoryId] = categoryVector\n",
    "\n",
    "#pprint(category2Predicted)\n",
    "\n",
    "# --------------------------------------------------\n",
    "# Stage 8: Make event category vectors \n",
    "# --------------------------------------------------\n",
    "\n",
    "event2groundtruth = {} # event -> category -> tweet vector with binary 1 vs all ground truth category labels\n",
    "for eventId in eventIdentifiers:\n",
    "    eventCategories = {}\n",
    "    for categoryId in informationTypes2Index.keys():\n",
    "        categoryIdShort = categoryId.split(\"-\")[1]\n",
    "        categoryVector = []\n",
    "        #print(eventId)\n",
    "        for tweetId in event2tweetIds.get(eventId):\n",
    "            #print(tweetId)\n",
    "            categories = tweetId2TRECInfoCategories.get(tweetId)\n",
    "            if any(categoryIdShort in s for s in categories):\n",
    "                categoryVector.append(1)\n",
    "            else:\n",
    "                categoryVector.append(0)\n",
    "            \n",
    "        eventCategories[categoryId] = categoryVector\n",
    "    event2groundtruth[eventId] = eventCategories\n",
    "    \n",
    "\n",
    "event2prediction = {} # event -> category -> tweet vector with binary 1 vs all predicted by system labels\n",
    "for eventId in eventIdentifiers:\n",
    "    eventCategories = {}\n",
    "    for categoryId in informationTypes2Index.keys():\n",
    "        categoryIdShort = categoryId.split(\"-\")[1]\n",
    "        categoryVector = []\n",
    "        #print(tweetId)\n",
    "        for tweetId in event2tweetIds.get(eventId):\n",
    "            #print(tweetId)\n",
    "            categories = tweetId2RunInfoCategories.get(tweetId)\n",
    "            \n",
    "            if categories == None:\n",
    "                categories = json.loads(\"[]\")\n",
    "                tweetId2RunInfoCategories[tweetId] = categories\n",
    "            \n",
    "            if any(categoryId in s for s in categories):\n",
    "                categoryVector.append(1)\n",
    "            else:\n",
    "                categoryVector.append(0)\n",
    "            \n",
    "        eventCategories[categoryId] = categoryVector\n",
    "    event2prediction[eventId] = eventCategories\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------\n",
    "# Stage 9: Make priority classification vectors\n",
    "# -----------------------------------------------------------\n",
    "\n",
    "category2GroundTruthPriority = {} # category -> tweet vector with binary 1 vs all ground truth priority labels\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    categoryIdShort = categoryId.split(\"-\")[1]\n",
    "    priorityVector = []\n",
    "    for i in range(len(index2TweetId)):\n",
    "        tweetId = index2TweetId[i]\n",
    "        categories = tweetId2TRECInfoCategories.get(tweetId)\n",
    "        if any(categoryIdShort in s for s in categories):\n",
    "            priority = tweetId2TRECPriorityCategory.get(tweetId)\n",
    "            priorityVector.append(priority)\n",
    "    category2GroundTruthPriority[categoryId] = priorityVector\n",
    "\n",
    "category2PredictedPriority = {} # category -> tweet vector with binary 1 vs all predicted by system labels\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    categoryIdShort = categoryId.split(\"-\")[1]\n",
    "    categoryVector = []\n",
    "    for i in range(len(index2TweetId)):\n",
    "        tweetId = index2TweetId[i]\n",
    "        categories = tweetId2TRECInfoCategories.get(tweetId)\n",
    "        if any(categoryIdShort in s for s in categories):\n",
    "            if tweetId2RunPriorityScore.get(tweetId):\n",
    "                priority = tweetId2RunPriorityScore.get(tweetId)\n",
    "            \n",
    "                categoryVector.append(priority)\n",
    "            else:\n",
    "                categoryVector.append(\"Low\") # default to low priority\n",
    "\n",
    "    category2PredictedPriority[categoryId] = categoryVector\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --------------------------------------------------\n",
    "# Disable Warnings (comment this out when debugging!)\n",
    "# --------------------------------------------------\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # ignore warnings about 0-score categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High Importance Alert Worth: -0.6953900709219878\n",
      "Low Importance Alert Worth: -0.012907375182118685\n",
      "Accumulated Alert Worth: -0.35414872305205325\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Mertic: Accumulated Alert Worth\n",
    "# Measures system effectiveness from the perspective\n",
    "# of end-user alerting of important information\n",
    "# --------------------------------------------------\n",
    "\n",
    "totalHighImportWorth = 0.0\n",
    "totalLowImportWorth = 0.0\n",
    "AccumulatedAlertWorth = 0.0\n",
    "\n",
    "\n",
    "for eventId in event2TweetIdRank.keys():\n",
    "    numConsecutiveFalseAlerts = 0\n",
    "    for tweetIdRank in event2TweetIdRank[eventId]:\n",
    "        \n",
    "        tweetId = tweetIdRank[0]\n",
    "        try:\n",
    "            trecPriority = tweetId2TRECPriorityCategory[tweetId]\n",
    "        except:\n",
    "            #print(tweetId)\n",
    "            continue # skip tweets not in the ground truth\n",
    "        runPriority = tweetId2RunPriorityScoreNorm[tweetId]\n",
    "        \n",
    "        trecHighImportCats = set(tweetId2TRECHighImportInfoCategories[tweetid])\n",
    "        trecLowImportCats = set(tweetId2TRECHighImportInfoCategories[tweetid])\n",
    "        runHighImportCats = set(tweetId2RunHighImportInfoCategories[tweetId])\n",
    "        runLowImportCats = set(tweetId2RunLowImportInfoCategories[tweetId])\n",
    "        \n",
    "        gamma = 0\n",
    "        if len(trecHighImportCats)>0:\n",
    "            gamma = var_lambda\n",
    "        \n",
    "        ActCScore = 0.0\n",
    "        if len(trecHighImportCats | runHighImportCats) > 0: ActCScore = gamma*(len(trecHighImportCats & runHighImportCats) / len(trecHighImportCats | runHighImportCats))\n",
    "        NActCScore = 0.0\n",
    "        if len(trecLowImportCats | runLowImportCats)>0: NActCScore = (1-gamma)*(len(trecLowImportCats & runLowImportCats) / len(trecLowImportCats | runLowImportCats))\n",
    "        \n",
    "        worth = 0.0\n",
    "        if trecPriority == \"High\" or trecPriority == \"Critical\":\n",
    "            # calculate highImportWorth\n",
    "            if runPriority == \"High\" or runPriority == \"Critical\":\n",
    "                worth = var_alpha + ((1 - var_alpha) * (ActCScore+NActCScore))\n",
    "                numConsecutiveFalseAlerts = 0\n",
    "            else:\n",
    "                worth = -1\n",
    "            \n",
    "            #print(eventId+\" \"+tweetId+\" (High) \"+str(worth))\n",
    "            \n",
    "            \n",
    "            totalHighImportWorth = totalHighImportWorth + worth\n",
    "        else:\n",
    "            if runPriority == \"High\" or runPriority == \"Critical\":\n",
    "                worth = max(-math.log10((numConsecutiveFalseAlerts/2)+1),-1)\n",
    "                numConsecutiveFalseAlerts = numConsecutiveFalseAlerts + 1\n",
    "            else:\n",
    "                worth = ActCScore+NActCScore\n",
    "            \n",
    "            #print(eventId+\" \"+tweetId+\" (Low) \"+str(worth)+\" (\"+str(numConsecutiveFalseAlerts)+\")\")\n",
    "            \n",
    "            totalLowImportWorth = totalLowImportWorth + worth\n",
    "            \n",
    "        AccumulatedAlertWorth = AccumulatedAlertWorth + worth\n",
    "        \n",
    "    \n",
    "totalHighImportWorth = totalHighImportWorth / countHighCriticalImport\n",
    "totalLowImportWorth = totalLowImportWorth / countLowMediumImport\n",
    "AccumulatedAlertWorth = totalHighImportWorth + totalLowImportWorth\n",
    "        \n",
    "AccumulatedAlertWorth = (AccumulatedAlertWorth/2)\n",
    "totalHighImportWorth = totalHighImportWorth\n",
    "totalLowImportWorth = totalLowImportWorth\n",
    "\n",
    "print(\"High Importance Alert Worth: \"+str(totalHighImportWorth))\n",
    "print(\"Low Importance Alert Worth: \"+str(totalLowImportWorth))\n",
    "print(\"Accumulated Alert Worth: \"+str(AccumulatedAlertWorth))\n",
    "       \n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"EVALUATON: Alertng Performance\"+\"\\n\")\n",
    "resultsFile.write(\"Overall performance\"+\"\\n\")\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"> High Importance Alert Worth:\"+\"\\t\"+str(totalHighImportWorth)+\"\\n\")\n",
    "resultsFile.write(\"> Low Importance Alert Worth:\"+\"\\t\"+str(totalLowImportWorth)+\"\\n\")\n",
    "resultsFile.write(\"> Accumulated Alert Worth:\"+\"\\t\"+str(AccumulatedAlertWorth)+\"\\n\")\n",
    "resultsFile.write(\"\"+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gilroygarlicShooting2020\n",
      "\tBest DCG: 24.200530381423853\n",
      "elPasoWalmartShooting2020\n",
      "\tBest DCG: 314.08006311421406\n",
      "whaleyBridgeCollapse2020\n",
      "\tBest DCG: 199.87706687885662\n",
      "myanmarFloods2020\n",
      "\tBest DCG: 71.30544649253791\n",
      "keralaFloods2020\n",
      "\tBest DCG: 109.30948318234077\n",
      "indonesiaEarthquake2020\n",
      "\tBest DCG: 249.75255773490613\n",
      "daytonOhioShooting2020\n",
      "\tBest DCG: 314.08006311421406\n",
      "typhoonLekima2020\n",
      "\tBest DCG: 231.8328063753546\n",
      "siberianWildfires2020\n",
      "\tBest DCG: 63.248011329701775\n",
      "baltimoreFlashFlood2020\n",
      "\tBest DCG: 50.77168742428136\n",
      "papuaNewguineaEarthquake2020\n",
      "\tBest DCG: 40.76666871020544\n",
      "hurricaneBarry2020\n",
      "\tBest DCG: 143.57068923296504\n",
      "typhoonKrosa2020\n",
      "\tBest DCG: 115.92458461947263\n",
      "athensEarthquake2020\n",
      "\tBest DCG: 63.19983388043606\n",
      "brooklynBlockPartyShooting2020\n",
      "\tBest DCG: 101.28501054423037\n",
      "Mean: 139.54696686767602\n",
      "\n",
      "Event: gilroygarlicShooting2020\n",
      "\tnDCG: 0.8677738860132873\n",
      "Event: elPasoWalmartShooting2020\n",
      "\tnDCG: 0.3327588969668241\n",
      "Event: whaleyBridgeCollapse2020\n",
      "\tnDCG: 0.6454801602933881\n",
      "Event: myanmarFloods2020\n",
      "\tnDCG: 0.7989370884683632\n",
      "Event: keralaFloods2020\n",
      "\tnDCG: 0.4248369368312651\n",
      "Event: indonesiaEarthquake2020\n",
      "\tnDCG: 0.2903873831400208\n",
      "Event: daytonOhioShooting2020\n",
      "\tnDCG: 0.3525181973783601\n",
      "Event: typhoonLekima2020\n",
      "\tnDCG: 0.2685839912086448\n",
      "Event: siberianWildfires2020\n",
      "\tnDCG: 0.4238028882286183\n",
      "Event: baltimoreFlashFlood2020\n",
      "\tnDCG: 0.4253643681713027\n",
      "Event: papuaNewguineaEarthquake2020\n",
      "\tnDCG: 0.8208119967510257\n",
      "Event: hurricaneBarry2020\n",
      "\tnDCG: 0.22768437927044935\n",
      "Event: typhoonKrosa2020\n",
      "\tnDCG: 0.23832342266173187\n",
      "Event: athensEarthquake2020\n",
      "\tnDCG: 0.7614783126156047\n",
      "Event: brooklynBlockPartyShooting2020\n",
      "\tnDCG: 0.7819736922532667\n",
      "\n",
      "System Event-Micro nDCG: 0.5107143733501435\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Priority-Centric Discounted Cumulative Gain\n",
    "# --------------------------------------------------\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def calc_dcg(scores, at_k=100):\n",
    "    position = 1\n",
    "    accumulator = 0.0\n",
    "    for score in scores[:at_k]:\n",
    "\n",
    "        numerator = 2 ** score - 1\n",
    "        denom = np.log2(position + 1)\n",
    "\n",
    "        accumulator += numerator / denom\n",
    "        position += 1\n",
    "\n",
    "    return accumulator\n",
    "\n",
    "priority_map = {\n",
    "    \"Low\": 1,\n",
    "    \"Medium\": 2,\n",
    "    \"High\": 3,\n",
    "    \"Critical\": 4,\n",
    "}\n",
    "\n",
    "at_k = 100\n",
    "\n",
    "tweetId2TRECPriorityCategory_score = {\n",
    "    k:priority_map[v] for k,v in tweetId2TRECPriorityCategory.items()\n",
    "}\n",
    "tweetId2TRECPriorityCategory_scores_sorted = sorted(\n",
    "    tweetId2TRECPriorityCategory_score.values(),\n",
    "    reverse=True\n",
    ")\n",
    "\n",
    "best_dcg_per_event = {}\n",
    "for event, rel_tweets in event2tweetIds.items():\n",
    "    print(event)\n",
    "    \n",
    "    tweetId2TRECPriorityCategory_scores_sorted = sorted(\n",
    "        [tweetId2TRECPriorityCategory_score[x] for x in rel_tweets],\n",
    "        reverse=True\n",
    "    )\n",
    "    ideal_dcg = calc_dcg(tweetId2TRECPriorityCategory_scores_sorted, at_k)\n",
    "    print(\"\\tBest DCG:\", ideal_dcg)\n",
    "    best_dcg_per_event[event] = ideal_dcg\n",
    "    \n",
    "print(\"Mean:\", np.mean(list(best_dcg_per_event.values())))\n",
    "print()\n",
    "\n",
    "# Code below calculates the DCG for a system's \n",
    "#  ranked priority tweets. We have to do some \n",
    "#  sampling here to break ties among tweets with\n",
    "#  the same priority scores.\n",
    "\n",
    "# Build a dataframe from the system's provided\n",
    "#  priority scores, so we can identify what the\n",
    "#  top-most priorities are and get a count of\n",
    "#  the number of tweets in each priority bin.\n",
    "priority_df = pd.DataFrame(\n",
    "    [(k, priority_map[v]) for k, v in tweetId2RunPriorityScoreNorm.items()],\n",
    "    columns=[\"tweet_id\", \"priority\"]\n",
    ")\n",
    "\n",
    "# Build metrics for each event\n",
    "system_dcg_per_event = {}\n",
    "for event, rel_tweets in event2tweetIds.items():\n",
    "    print(\"Event:\", event)\n",
    "    local_priority_df = priority_df[priority_df[\"tweet_id\"].isin(set(rel_tweets))]\n",
    "    \n",
    "    unique_scores = local_priority_df[\"priority\"].value_counts()\n",
    "    \n",
    "    # Find the top priority scores that would be included\n",
    "    #  in the necessary at_k values.\n",
    "    total = 0\n",
    "    top_keys = []\n",
    "    candidates = {}\n",
    "    for top in sorted(unique_scores.index, reverse=True):\n",
    "\n",
    "        # We store this key, so we can go back and shuffle\n",
    "        #. tweets with this score.\n",
    "        top_keys.append(top)\n",
    "        local_restricted_df = local_priority_df[local_priority_df[\"priority\"] == top]\n",
    "        candidates[top] = list(local_restricted_df[\"tweet_id\"])\n",
    "\n",
    "        total += local_restricted_df.shape[0]\n",
    "\n",
    "        # Once we have enough samples, stop.\n",
    "        if ( total > at_k ):\n",
    "            break\n",
    "\n",
    "    # Now we generate distribution over the DCG for this\n",
    "    #  system and do this a number of times to remove\n",
    "    #  dependence on our selection of the top k tweets\n",
    "    random_dcgs = []\n",
    "    for i in range(100):\n",
    "\n",
    "        local_tweet_ids = []\n",
    "        for top in top_keys:\n",
    "            this_top_tweets = candidates[top][:]\n",
    "            np.random.shuffle(this_top_tweets)\n",
    "\n",
    "            needed = at_k - len(local_tweet_ids)\n",
    "            local_tweet_ids.extend(this_top_tweets[:needed])\n",
    "\n",
    "        local_scores = [tweetId2TRECPriorityCategory_score[x] for x in local_tweet_ids]\n",
    "\n",
    "        random_dcgs.append(calc_dcg(local_scores))\n",
    "\n",
    "    system_dcg = np.mean(random_dcgs)\n",
    "\n",
    "    system_ndcg_ = system_dcg / best_dcg_per_event[event]\n",
    "    print(\"\\tnDCG:\", system_ndcg_)\n",
    "    system_dcg_per_event[event] = system_ndcg_\n",
    "    \n",
    "print()\n",
    "system_ndcg_micro = np.mean(list(system_dcg_per_event.values()))\n",
    "print(\"System Event-Micro nDCG:\", system_ndcg_micro)\n",
    "\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"EVALUATON: nDCG and Priority\"+\"\\n\")\n",
    "resultsFile.write(\"Overall performance\"+\"\\n\")\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"> nDCG:\"+\"\\t\"+str(system_ndcg_micro)+\"\\n\")\n",
    "resultsFile.write(\"\"+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Type Precision (positive class, multi-type, macro): 0.3626256600776706\n",
      "Information Type Recall (positive class, multi-type, macro): 0.2752951035601624\n",
      "Information Type F1 (positive class, multi-type, macro): 0.2768995047569121\n",
      "Information Type Accuracy (overall, multi-type, macro): 0.924985687413102\n",
      "High Importance Information Type Precision (positive class, multi-type, macro): 0.41247771767951846\n",
      "High Importance Information Type Recall (positive class, multi-type, macro): 0.1636611658808292\n",
      "High Importance Information Type F1 (positive class, multi-type, macro): 0.20306160005765653\n",
      "High Importance Information Type Accuracy (overall, multi-type, macro): 0.9765409885226685\n",
      "Low Importance Information Type Precision (positive class, multi-type, macro): 0.34688290504550806\n",
      "Low Importance Information Type Recall (positive class, multi-type, macro): 0.31054792598521513\n",
      "Low Importance Information Type F1 (positive class, multi-type, macro): 0.30021673781983493\n",
      "Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9087050660100813\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Type Categorization\n",
    "# Overall performance\n",
    "# --------------------------------------------------\n",
    "# Average performance over information types\n",
    "# Macro averaged (information types have equal weight)\n",
    "# Does not average across events (larger events have more impact)\n",
    "# Positive class is the target class\n",
    "# Precision, recall and F1 only consider the positive class\n",
    "# Accuracy is an overall metric\n",
    "# We report performance for all categories, high importance categories and low importance categories\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "avgPrecision = 0.0\n",
    "avgRecall = 0.0\n",
    "avgF1 = 0.0\n",
    "avgAccuracy = 0.0\n",
    "\n",
    "avgPrecisionHigh = 0.0\n",
    "avgRecallHigh = 0.0\n",
    "avgF1High = 0.0\n",
    "avgAccuracyHigh = 0.0\n",
    "\n",
    "avgPrecisionLow = 0.0\n",
    "avgRecallLow = 0.0\n",
    "avgF1Low = 0.0\n",
    "avgAccuracyLow = 0.0\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    categoryPrecision = precision_score(category2GroundTruth[categoryId], category2Predicted[categoryId], average='binary')\n",
    "    categoryRecall = recall_score(category2GroundTruth[categoryId], category2Predicted[categoryId], average='binary')\n",
    "    categoryF1 = f1_score(category2GroundTruth[categoryId], category2Predicted[categoryId], average='binary')\n",
    "    categoryAccuracy = accuracy_score(category2GroundTruth[categoryId], category2Predicted[categoryId])\n",
    "    \n",
    "    avgPrecision = avgPrecision + categoryPrecision\n",
    "    avgRecall = avgRecall + categoryRecall\n",
    "    avgF1 = avgF1 + categoryF1\n",
    "    avgAccuracy = avgAccuracy + categoryAccuracy\n",
    "    \n",
    "    if any(categoryId in s for s in highImportCategories):\n",
    "        avgPrecisionHigh = avgPrecisionHigh + categoryPrecision\n",
    "        avgRecallHigh = avgRecallHigh + categoryRecall\n",
    "        avgF1High = avgF1High + categoryF1\n",
    "        avgAccuracyHigh = avgAccuracyHigh + categoryAccuracy\n",
    "    else:\n",
    "        avgPrecisionLow = avgPrecisionLow + categoryPrecision\n",
    "        avgRecallLow = avgRecallLow + categoryRecall\n",
    "        avgF1Low = avgF1Low + categoryF1\n",
    "        avgAccuracyLow = avgAccuracyLow + categoryAccuracy\n",
    "\n",
    "numInformationTypes = len(informationTypes2Index)\n",
    "numHighInformationTypes = len(highImportCategories)\n",
    "numLowInformationTypes = numInformationTypes - numHighInformationTypes\n",
    "        \n",
    "print(\"Information Type Precision (positive class, multi-type, macro): \"+str(avgPrecision/numInformationTypes))\n",
    "print(\"Information Type Recall (positive class, multi-type, macro): \"+str(avgRecall/numInformationTypes))\n",
    "print(\"Information Type F1 (positive class, multi-type, macro): \"+str(avgF1/numInformationTypes))\n",
    "print(\"Information Type Accuracy (overall, multi-type, macro): \"+str(avgAccuracy/numInformationTypes))\n",
    "\n",
    "print(\"High Importance Information Type Precision (positive class, multi-type, macro): \"+str(avgPrecisionHigh/numHighInformationTypes))\n",
    "print(\"High Importance Information Type Recall (positive class, multi-type, macro): \"+str(avgRecallHigh/numHighInformationTypes))\n",
    "print(\"High Importance Information Type F1 (positive class, multi-type, macro): \"+str(avgF1High/numHighInformationTypes))\n",
    "print(\"High Importance Information Type Accuracy (overall, multi-type, macro): \"+str(avgAccuracyHigh/numHighInformationTypes))\n",
    "\n",
    "print(\"Low Importance Information Type Precision (positive class, multi-type, macro): \"+str(avgPrecisionLow/numLowInformationTypes))\n",
    "print(\"Low Importance Information Type Recall (positive class, multi-type, macro): \"+str(avgRecallLow/numLowInformationTypes))\n",
    "print(\"Low Importance Information Type F1 (positive class, multi-type, macro): \"+str(avgF1Low/numLowInformationTypes))\n",
    "print(\"Low Importance Information Type Accuracy (overall, multi-type, macro): \"+str(avgAccuracyLow/numLowInformationTypes))\n",
    "\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"EVALUATON: Information Type Categorization\"+\"\\n\")\n",
    "resultsFile.write(\"Overall performance\"+\"\\n\")\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"> Information Type Precision (positive class, multi-type, macro):\"+\"\\t\"+str(avgPrecision/len(informationTypes2Index))+\"\\n\")\n",
    "resultsFile.write(\"> Information Type Recall (positive class, multi-type, macro):\"+\"\\t\"+str(avgRecall/len(informationTypes2Index))+\"\\n\")\n",
    "resultsFile.write(\"> Information Type F1 (positive class, multi-type, macro):\"+\"\\t\"+str(avgF1/len(informationTypes2Index))+\"\\n\")\n",
    "resultsFile.write(\"> Information Type Accuracy (overall, multi-type, macro):\"+\"\\t\"+str(avgAccuracy/len(informationTypes2Index))+\"\\n\")\n",
    "resultsFile.write(\"> High Importance Information Type Precision (positive class, multi-type, macro):\"+\"\\t\"+str(avgPrecisionHigh/numHighInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> High Importance Information Type Recall (positive class, multi-type, macro):\"+\"\\t\"+str(avgRecallHigh/numHighInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> High Importance Information Type F1 (positive class, multi-type, macro):\"+\"\\t\"+str(avgF1High/numHighInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> High Importance Information Type Accuracy (overall, multi-type, macro):\"+\"\\t\"+str(avgAccuracyHigh/numHighInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> Low Importance Information Type Precision (positive class, multi-type, macro):\"+\"\\t\"+str(avgPrecisionLow/numLowInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> Low Importance Information Type Recall (positive class, multi-type, macro):\"+\"\\t\"+str(avgRecallLow/numLowInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> Low Importance Information Type F1 (positive class, multi-type, macro):\"+\"\\t\"+str(avgF1Low/numLowInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> Low Importance Information Type Accuracy (overall, multi-type, macro):\"+\"\\t\"+str(avgAccuracyLow/numLowInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"\"+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request-GoodsServices\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "        Other Classes       1.00      1.00      1.00     12216\n",
      "Request-GoodsServices       0.00      0.00      0.00        11\n",
      "\n",
      "             accuracy                           1.00     12227\n",
      "            macro avg       0.50      0.50      0.50     12227\n",
      "         weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "Request-SearchAndRescue\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "          Other Classes       1.00      1.00      1.00     12216\n",
      "Request-SearchAndRescue       1.00      0.18      0.31        11\n",
      "\n",
      "               accuracy                           1.00     12227\n",
      "              macro avg       1.00      0.59      0.65     12227\n",
      "           weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "Request-InformationWanted\n",
      "                           precision    recall  f1-score   support\n",
      "\n",
      "            Other Classes       1.00      1.00      1.00     12196\n",
      "Request-InformationWanted       0.54      0.42      0.47        31\n",
      "\n",
      "                 accuracy                           1.00     12227\n",
      "                macro avg       0.77      0.71      0.74     12227\n",
      "             weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "CallToAction-Volunteer\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "         Other Classes       1.00      1.00      1.00     12214\n",
      "CallToAction-Volunteer       0.75      0.46      0.57        13\n",
      "\n",
      "              accuracy                           1.00     12227\n",
      "             macro avg       0.87      0.73      0.79     12227\n",
      "          weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "CallToAction-Donations\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "         Other Classes       1.00      1.00      1.00     12197\n",
      "CallToAction-Donations       0.81      0.57      0.67        30\n",
      "\n",
      "              accuracy                           1.00     12227\n",
      "             macro avg       0.90      0.78      0.83     12227\n",
      "          weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "CallToAction-MovePeople\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "          Other Classes       0.99      1.00      0.99     12109\n",
      "CallToAction-MovePeople       0.28      0.16      0.20       118\n",
      "\n",
      "               accuracy                           0.99     12227\n",
      "              macro avg       0.64      0.58      0.60     12227\n",
      "           weighted avg       0.98      0.99      0.99     12227\n",
      "\n",
      "Report-FirstPartyObservation\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "               Other Classes       1.00      0.99      1.00     12187\n",
      "Report-FirstPartyObservation       0.12      0.28      0.17        40\n",
      "\n",
      "                    accuracy                           0.99     12227\n",
      "                   macro avg       0.56      0.63      0.58     12227\n",
      "                weighted avg       0.99      0.99      0.99     12227\n",
      "\n",
      "Report-ThirdPartyObservation\n",
      "                              precision    recall  f1-score   support\n",
      "\n",
      "               Other Classes       0.98      0.91      0.95     11890\n",
      "Report-ThirdPartyObservation       0.13      0.46      0.20       337\n",
      "\n",
      "                    accuracy                           0.90     12227\n",
      "                   macro avg       0.56      0.69      0.58     12227\n",
      "                weighted avg       0.96      0.90      0.93     12227\n",
      "\n",
      "Report-Weather\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      " Other Classes       0.99      0.91      0.95     12080\n",
      "Report-Weather       0.06      0.46      0.10       147\n",
      "\n",
      "      accuracy                           0.90     12227\n",
      "     macro avg       0.53      0.68      0.53     12227\n",
      "  weighted avg       0.98      0.90      0.94     12227\n",
      "\n",
      "Report-Location\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  Other Classes       0.79      0.81      0.80      8786\n",
      "Report-Location       0.48      0.44      0.46      3441\n",
      "\n",
      "       accuracy                           0.71     12227\n",
      "      macro avg       0.63      0.63      0.63     12227\n",
      "   weighted avg       0.70      0.71      0.70     12227\n",
      "\n",
      "Report-EmergingThreats\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "         Other Classes       0.96      0.95      0.95     11419\n",
      "Report-EmergingThreats       0.34      0.40      0.37       808\n",
      "\n",
      "              accuracy                           0.91     12227\n",
      "             macro avg       0.65      0.67      0.66     12227\n",
      "          weighted avg       0.92      0.91      0.91     12227\n",
      "\n",
      "Report-NewSubEvent\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "     Other Classes       0.97      1.00      0.98     11828\n",
      "Report-NewSubEvent       0.71      0.12      0.21       399\n",
      "\n",
      "          accuracy                           0.97     12227\n",
      "         macro avg       0.84      0.56      0.60     12227\n",
      "      weighted avg       0.96      0.97      0.96     12227\n",
      "\n",
      "Report-MultimediaShare\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "         Other Classes       0.86      0.89      0.88      9860\n",
      "Report-MultimediaShare       0.47      0.39      0.43      2367\n",
      "\n",
      "              accuracy                           0.80     12227\n",
      "             macro avg       0.66      0.64      0.65     12227\n",
      "          weighted avg       0.78      0.80      0.79     12227\n",
      "\n",
      "Report-ServiceAvailable\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "          Other Classes       1.00      1.00      1.00     12185\n",
      "Report-ServiceAvailable       0.15      0.12      0.13        42\n",
      "\n",
      "               accuracy                           0.99     12227\n",
      "              macro avg       0.57      0.56      0.56     12227\n",
      "           weighted avg       0.99      0.99      0.99     12227\n",
      "\n",
      "Report-Factoid\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      " Other Classes       0.91      0.92      0.91     10563\n",
      "Report-Factoid       0.45      0.42      0.44      1664\n",
      "\n",
      "      accuracy                           0.85     12227\n",
      "     macro avg       0.68      0.67      0.68     12227\n",
      "  weighted avg       0.85      0.85      0.85     12227\n",
      "\n",
      "Report-Official\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  Other Classes       0.97      0.99      0.98     11844\n",
      "Report-Official       0.15      0.07      0.09       383\n",
      "\n",
      "       accuracy                           0.96     12227\n",
      "      macro avg       0.56      0.53      0.54     12227\n",
      "   weighted avg       0.94      0.96      0.95     12227\n",
      "\n",
      "Report-News\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "Other Classes       0.92      0.91      0.91     10583\n",
      "  Report-News       0.44      0.46      0.45      1644\n",
      "\n",
      "     accuracy                           0.85     12227\n",
      "    macro avg       0.68      0.68      0.68     12227\n",
      " weighted avg       0.85      0.85      0.85     12227\n",
      "\n",
      "Report-CleanUp\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      " Other Classes       1.00      1.00      1.00     12220\n",
      "Report-CleanUp       0.00      0.00      0.00         7\n",
      "\n",
      "      accuracy                           1.00     12227\n",
      "     macro avg       0.50      0.50      0.50     12227\n",
      "  weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "Report-Hashtags\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  Other Classes       0.93      0.95      0.94     10806\n",
      "Report-Hashtags       0.56      0.45      0.50      1421\n",
      "\n",
      "       accuracy                           0.90     12227\n",
      "      macro avg       0.75      0.70      0.72     12227\n",
      "   weighted avg       0.89      0.90      0.89     12227\n",
      "\n",
      "Report-OriginalEvent\n",
      "                      precision    recall  f1-score   support\n",
      "\n",
      "       Other Classes       0.98      0.99      0.99     11999\n",
      "Report-OriginalEvent       0.09      0.03      0.05       228\n",
      "\n",
      "            accuracy                           0.98     12227\n",
      "           macro avg       0.53      0.51      0.52     12227\n",
      "        weighted avg       0.97      0.98      0.97     12227\n",
      "\n",
      "Other-ContextualInformation\n",
      "                             precision    recall  f1-score   support\n",
      "\n",
      "              Other Classes       0.97      0.99      0.98     11732\n",
      "Other-ContextualInformation       0.42      0.23      0.30       495\n",
      "\n",
      "                   accuracy                           0.96     12227\n",
      "                  macro avg       0.70      0.61      0.64     12227\n",
      "               weighted avg       0.95      0.96      0.95     12227\n",
      "\n",
      "Other-Advice\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "Other Classes       0.99      0.98      0.98     12084\n",
      " Other-Advice       0.10      0.22      0.14       143\n",
      "\n",
      "     accuracy                           0.97     12227\n",
      "    macro avg       0.55      0.60      0.56     12227\n",
      " weighted avg       0.98      0.97      0.97     12227\n",
      "\n",
      "Other-Sentiment\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  Other Classes       0.99      0.98      0.99     12055\n",
      "Other-Sentiment       0.16      0.20      0.18       172\n",
      "\n",
      "       accuracy                           0.97     12227\n",
      "      macro avg       0.57      0.59      0.58     12227\n",
      "   weighted avg       0.98      0.97      0.98     12227\n",
      "\n",
      "Other-Discussion\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "   Other Classes       1.00      1.00      1.00     12200\n",
      "Other-Discussion       0.00      0.00      0.00        27\n",
      "\n",
      "        accuracy                           1.00     12227\n",
      "       macro avg       0.50      0.50      0.50     12227\n",
      "    weighted avg       1.00      1.00      1.00     12227\n",
      "\n",
      "Other-Irrelevant\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "   Other Classes       0.43      0.91      0.59      4339\n",
      "Other-Irrelevant       0.87      0.35      0.50      7888\n",
      "\n",
      "        accuracy                           0.55     12227\n",
      "       macro avg       0.65      0.63      0.54     12227\n",
      "    weighted avg       0.72      0.55      0.53     12227\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Type Categorization\n",
    "# Per Information Type Performance\n",
    "# --------------------------------------------------\n",
    "# Per Category Classification Performance with confusion matrices\n",
    "# Performance on the target class is what we care about here, \n",
    "# primaraly with respect to recall, as we want the user to \n",
    "# see all of the information for a given category. A small\n",
    "# amount of noise being added to the feed is an acceptable\n",
    "# cost for good recall.\n",
    "#\n",
    "# Does not average across events (larger events have more impact)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "perTopicFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "perTopicFile.write(\"EVALUATON: Information Type Categorization (Multi-type)\"+\"\\n\")\n",
    "perTopicFile.write(\"Per Information Type Performance\"+\"\\n\")\n",
    "perTopicFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    target_names = ['Other Classes', categoryId]\n",
    "    print(categoryId)\n",
    "    print(classification_report(category2GroundTruth[categoryId], category2Predicted[categoryId], target_names=target_names))\n",
    "\n",
    "\n",
    "    perTopicFile.write(categoryId+\"\\n\")\n",
    "    perTopicFile.write(classification_report(category2GroundTruth[categoryId], category2Predicted[categoryId], target_names=target_names)+\"\\n\")\n",
    "    perTopicFile.write(\"\"+\"\\n\")\n",
    "\n",
    "perTopicFile.write(\"\"+\"\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Type Categorization\n",
    "# Per Information Type F1 Graph\n",
    "# --------------------------------------------------\n",
    "# Per Category Classification Performance\n",
    "# F1 scores for each information type, graphed\n",
    "# Does not average across events (larger events have more impact)\n",
    "\n",
    "\n",
    "\n",
    "N = len(informationTypes2Index)\n",
    "ind = np.arange(N)\n",
    "\n",
    "scoresPerCategoryF1 = []\n",
    "categoryLabels = []\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    scoresPerCategoryF1.append(f1_score(category2GroundTruth[categoryId], category2Predicted[categoryId], average='binary'))\n",
    "    categoryLabels.append(categoryId)\n",
    "    \n",
    "width = 0.90       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, scoresPerCategoryF1, width)\n",
    "\n",
    "plt.ylabel('F1 Scores')\n",
    "plt.title('F1 Scores by Information Type')\n",
    "plt.xticks(ind, categoryLabels, rotation='vertical')\n",
    "plt.yticks(np.arange(0, 1, 0.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "athensEarthquake2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.35965811965811967\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.16822057550292846\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.22751476261706302\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9736997885835094\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.3333333333333333\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.15079365079365079\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.20454545454545456\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9968287526427062\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.3679712100764732\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.1737238148848056\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.23476822832388672\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.966395905196395\n",
      "\n",
      "baltimoreFlashFlood2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.03983673469387755\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.020854700854700856\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.02737727910238429\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9799573560767592\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9992892679459843\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.05241675617615467\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.027440395861448495\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.03602273566103196\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9738525418022669\n",
      "\n",
      "brooklynBlockPartyShooting2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.5654184407448583\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.36710147864839615\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.4338600278518102\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9131524008350729\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.6333333333333333\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.46296296296296285\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.5285714285714286\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9968684759916493\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.5439716325590243\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.3368294309701118\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.40395116446666757\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.8867157455224701\n",
      "\n",
      "daytonOhioShooting2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.07253031634898115\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.050082235588320055\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.04734153997218337\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.943663157894737\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9964912280701754\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.09543462677497519\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.06589767840568428\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.06229149996339917\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9269806094182828\n",
      "\n",
      "elPasoWalmartShooting2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.13488445754102002\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.12461805405308855\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.10978331120182179\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9292650334075724\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.995916852264291\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.17747954939607896\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.1639711237540639\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.144451725265555\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9082170906107138\n",
      "\n",
      "gilroygarlicShooting2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.185492133620017\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.10956819939453075\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.13250091747490889\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9387234042553192\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9985815602836879\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.24406859686844343\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.14416868341385625\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.1743433124669854\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9198208286674131\n",
      "\n",
      "hurricaneBarry2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.06782311415532268\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.0925678656260604\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.05646712996741124\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9229850746268657\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.059722222222222225\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.12222222222222223\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.06835799859055673\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.964818763326226\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.07038129055524915\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.08320333196411457\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.05271211882326004\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9097744360902255\n",
      "\n",
      "indonesiaEarthquake2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.10659458125143063\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.08832266008471565\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.07421855495299155\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.8506382978723406\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.08633093525179857\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.07547169811320754\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.08053691275167786\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.8829787234042553\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.1129936273565776\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.092380858602034\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.07222328406919587\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.8404255319148937\n",
      "\n",
      "keralaFloods2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.34632148313221234\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.2476691813767569\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.2658886187822283\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.8916624040920713\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9846547314578006\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.4556861620160689\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.3258805018115222\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.34985344576608984\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.8622964059765783\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myanmarFloods2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.45891502539328627\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.32465468507922174\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.3759496651848975\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.967404255319149\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.3055555555555556\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.20625692137320048\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.24270894499902132\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9840425531914895\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.5073443316578327\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.3620434525653337\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.41802568208570057\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9621500559910414\n",
      "\n",
      "papuaNewguineaEarthquake2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.47077574572808756\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.27825747268095624\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.33728590160018745\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.93082774049217\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.3194444444444444\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.25\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.2745098039215686\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9731543624161074\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.5185645777123958\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.2871808851065214\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.3571099324460671\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9174614388319795\n",
      "\n",
      "siberianWildfires2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.1678072489027979\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.10795223591994255\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.11989174334737804\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9238818565400841\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.1372549019607843\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.03333333333333333\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.05363984674329502\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9771448663853727\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.17745535846343383\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.13151609989466126\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.14081339490656214\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9070619586942037\n",
      "\n",
      "typhoonKrosa2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.08881048585338457\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.14622934146961608\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.07012944338359574\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.9042060085836909\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9620886981402003\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.11685590243866391\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.19240702824949485\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.09227558339946808\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.8859272645132142\n",
      "\n",
      "typhoonLekima2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.07549069502472092\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.11222125850381565\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.058868366622061144\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.8831556503198292\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.044270833333333336\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.06589147286821705\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.0529595015576324\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9729921819474058\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.08534959871673804\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.12685171712558357\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.060734324010828115\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.8547862192795422\n",
      "\n",
      "whaleyBridgeCollapse2020\n",
      "  Information Type Precision (positive class, multi-type, macro): 0.5582336213837762\n",
      "  Information Type Recall (positive class, multi-type, macro): 0.23642688558690453\n",
      "  Information Type F1 (positive class, multi-type, macro): 0.3282239321861125\n",
      "  Information Type Accuracy (overall, multi-type, macro): 0.912190476190476\n",
      "  High Importance Information Type Precision (positive class, multi-type, macro): 0.403390522875817\n",
      "  High Importance Information Type Recall (positive class, multi-type, macro): 0.17472017472017473\n",
      "  High Importance Information Type F1 (positive class, multi-type, macro): 0.24326677435245328\n",
      "  High Importance Information Type Accuracy (overall, multi-type, macro): 0.9238095238095237\n",
      "  Low Importance Information Type Precision (positive class, multi-type, macro): 0.607131441965237\n",
      "  Low Importance Information Type Recall (positive class, multi-type, macro): 0.2559132153342929\n",
      "  Low Importance Information Type F1 (positive class, multi-type, macro): 0.35505250834411023\n",
      "  Low Importance Information Type Accuracy (overall, multi-type, macro): 0.9085213032581454\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Type Categorization\n",
    "# Per Event Performance\n",
    "# --------------------------------------------------\n",
    "# Categorization performance for each event\n",
    "# Precision, recall and F1 only consider the positive class\n",
    "# Accuracy is an overall metric\n",
    "# We report performance for all categories, high importance categories and low importance categories\n",
    "# Macro average (categories have equal weight)\n",
    "\n",
    "perEventFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "perEventFile.write(\"EVALUATON: Information Type Categorization (Multi-type)\"+\"\\n\")\n",
    "perEventFile.write(\"Per Event Performance\"+\"\\n\")\n",
    "perEventFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "\n",
    "for eventId in eventIdentifiers:\n",
    "    tavgPrecision = 0.0\n",
    "    tavgRecall = 0.0\n",
    "    tavgF1 = 0.0\n",
    "    tavgAccuracy = 0.0\n",
    "    \n",
    "    tavgPrecisionHigh = 0.0\n",
    "    tavgRecallHigh = 0.0\n",
    "    tavgF1High = 0.0\n",
    "    tavgAccuracyHigh = 0.0\n",
    "\n",
    "    tavgPrecisionLow = 0.0\n",
    "    tavgRecallLow = 0.0\n",
    "    tavgF1Low = 0.0\n",
    "    tavgAccuracyLow = 0.0\n",
    "\n",
    "    for categoryId in informationTypes2Index.keys():\n",
    "        \n",
    "        categoryPrecision = precision_score(event2groundtruth[eventId].get(categoryId), event2prediction[eventId].get(categoryId), average='binary')\n",
    "        categoryRecall = recall_score(event2groundtruth[eventId].get(categoryId), event2prediction[eventId].get(categoryId), average='binary')\n",
    "        categoryF1 = f1_score(event2groundtruth[eventId].get(categoryId), event2prediction[eventId].get(categoryId), average='binary')\n",
    "        categoryAccuracy = accuracy_score(event2groundtruth[eventId].get(categoryId), event2prediction[eventId].get(categoryId))\n",
    "        \n",
    "        tavgPrecision = tavgPrecision + categoryPrecision\n",
    "        tavgRecall = tavgRecall + categoryRecall\n",
    "        tavgF1 = tavgF1 + categoryF1\n",
    "        tavgAccuracy = tavgAccuracy + categoryAccuracy\n",
    "        \n",
    "        if any(categoryId in s for s in highImportCategories):\n",
    "            tavgPrecisionHigh = tavgPrecisionHigh + categoryPrecision\n",
    "            tavgRecallHigh = tavgRecallHigh + categoryRecall\n",
    "            tavgF1High = tavgF1High + categoryF1\n",
    "            tavgAccuracyHigh = tavgAccuracyHigh + categoryAccuracy\n",
    "        else:\n",
    "            tavgPrecisionLow = tavgPrecisionLow + categoryPrecision\n",
    "            tavgRecallLow = tavgRecallLow + categoryRecall\n",
    "            tavgF1Low = tavgF1Low + categoryF1\n",
    "            tavgAccuracyLow = tavgAccuracyLow + categoryAccuracy\n",
    "        \n",
    "    print(eventId)\n",
    "    print(\"  Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecision/len(informationTypes2Index)))\n",
    "    print(\"  Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecall/len(informationTypes2Index)))\n",
    "    print(\"  Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1/len(informationTypes2Index)))\n",
    "    print(\"  Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracy/len(informationTypes2Index)))\n",
    "    print(\"  High Importance Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecisionHigh/numHighInformationTypes))\n",
    "    print(\"  High Importance Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecallHigh/numHighInformationTypes))\n",
    "    print(\"  High Importance Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1High/numHighInformationTypes))\n",
    "    print(\"  High Importance Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracyHigh/numHighInformationTypes))\n",
    "    print(\"  Low Importance Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecisionLow/numLowInformationTypes))\n",
    "    print(\"  Low Importance Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecallLow/numLowInformationTypes))\n",
    "    print(\"  Low Importance Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1Low/numLowInformationTypes))\n",
    "    print(\"  Low Importance Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracyLow/numLowInformationTypes))\n",
    "    print(\"\")\n",
    "    \n",
    "    perEventFile.write(eventId+\"\\n\")\n",
    "    perEventFile.write(\"  Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecision/len(informationTypes2Index))+\"\\n\")\n",
    "    perEventFile.write(\"  Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecall/len(informationTypes2Index))+\"\\n\")\n",
    "    perEventFile.write(\"  Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1/len(informationTypes2Index))+\"\\n\")\n",
    "    perEventFile.write(\"  Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracy/len(informationTypes2Index))+\"\\n\")\n",
    "    perEventFile.write(\"  High Importance Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecisionHigh/numHighInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  High Importance Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecallHigh/numHighInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  High Importance Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1High/numHighInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  High Importance Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracyHigh/numHighInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  Low Importance Information Type Precision (positive class, multi-type, macro): \"+str(tavgPrecisionLow/numLowInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  Low Importance Information Type Recall (positive class, multi-type, macro): \"+str(tavgRecallLow/numLowInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  Low Importance Information Type F1 (positive class, multi-type, macro): \"+str(tavgF1Low/numLowInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"  Low Importance Information Type Accuracy (overall, multi-type, macro): \"+str(tavgAccuracyLow/numLowInformationTypes)+\"\\n\")\n",
    "    perEventFile.write(\"\\n\")\n",
    "    \n",
    "perEventFile.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Type Categorization\n",
    "# Per Event F1 Graph\n",
    "# --------------------------------------------------\n",
    "# Multi-type (1 vs All): Tweets have multiple information types, aim: predict all of them\n",
    "# Macro average (categories have equal weight)\n",
    "\n",
    "N = len(eventIdentifiers)\n",
    "ind = np.arange(N)\n",
    "\n",
    "scoresPerEventF1 = []\n",
    "for eventId in eventIdentifiers:\n",
    "    avgF1_ = 0.0\n",
    "    \n",
    "    for categoryId in informationTypes2Index.keys():\n",
    "        avgF1_ = avgF1_ + f1_score(event2groundtruth[eventId].get(categoryId), event2prediction[eventId].get(categoryId), average='binary')\n",
    "        \n",
    "    scoresPerEventF1.append(avgF1_/len(informationTypes2Index))\n",
    "    \n",
    "width = 0.90       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, scoresPerEventF1, width)\n",
    "\n",
    "plt.ylabel('F1 Scores')\n",
    "plt.title('F1 Category Scores by Event')\n",
    "plt.xticks(ind, eventIdentifiers, rotation='vertical')\n",
    "plt.yticks(np\n",
    "           .arange(0, 1, 0.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Priority Label Prediction (F1, macro): 0.24454603062502867\n",
      "Priority Label Prediction High Importance (F1, macro): 0.15389797150309306\n",
      "Priority Label Prediction Low Importance (F1, macro): 0.2731717335056399\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nPriority Label Prediction (F1, macro): 0.17789293558612237\\nPriority Label Prediction High Importance (F1, macro): 0.06457810783990874\\nPriority Label Prediction Low Importance (F1, macro): 0.21367656540071608\\n// Subset of selected.json\\nPriority Label Prediction (F1, macro): 0.18414638169354325\\nPriority Label Prediction High Importance (F1, macro): 0.07393693861085165\\nPriority Label Prediction Low Importance (F1, macro): 0.2189493637196564\\n// All selected.json loaded\\nPriority Label Prediction (F1, macro): 0.329702967947908\\nPriority Label Prediction High Importance (F1, macro): 0.16318618536713062\\nPriority Label Prediction Low Importance (F1, macro): 0.3822872150786798\\n\\n// Counting categories properly\\nPriority Label Prediction (F1, macro): 0.21481839425257468\\nPriority Label Prediction High Importance (F1, macro): 0.14300090499390425\\nPriority Label Prediction Low Importance (F1, macro): 0.23749760138689163\\n\\n// Tidying code passing / dataframes\\nPriority Label Prediction (F1, macro): 0.2692907991421786\\nPriority Label Prediction High Importance (F1, macro): 0.15697230759182423\\nPriority Label Prediction Low Importance (F1, macro): 0.30475979647386947\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Priority Level\n",
    "# Overall Performance\n",
    "# --------------------------------------------------\n",
    "# How divergent is the system from the human priority labels?\n",
    "# F1 performance over information types, higher is better\n",
    "# Macro average (categories have equal weight)\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "priorityAvgf1 = 0.0;\n",
    "priorityAvgf1High = 0.0;\n",
    "priorityAvgf1Low = 0.0;\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    groundTruthPriorities = category2GroundTruthPriority[categoryId]\n",
    "    predictedPriorities = category2PredictedPriority[categoryId]\n",
    "\n",
    "    f1 = f1_score(groundTruthPriorities, predictedPriorities, average='macro')\n",
    "    priorityAvgf1 = priorityAvgf1 + f1;\n",
    "    \n",
    "    if any(categoryId in s for s in highImportCategories):\n",
    "        priorityAvgf1High = priorityAvgf1High + f1\n",
    "    else:\n",
    "        priorityAvgf1Low = priorityAvgf1Low + f1\n",
    "    \n",
    "    \n",
    "    \n",
    "print(\"Priority Label Prediction (F1, macro): \"+str(priorityAvgf1/len(informationTypes2Index)))\n",
    "print(\"Priority Label Prediction High Importance (F1, macro): \"+str(priorityAvgf1High/numHighInformationTypes))\n",
    "print(\"Priority Label Prediction Low Importance (F1, macro): \"+str(priorityAvgf1Low/numLowInformationTypes))\n",
    "    \n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"EVALUATON: Information Priority Level\"+\"\\n\")\n",
    "resultsFile.write(\"Overall Performance\"+\"\\n\")\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"> Priority Label Prediction (F1, macro): \"+str(priorityAvgf1/len(informationTypes2Index))+\"\\n\")\n",
    "resultsFile.write(\"> Priority Label Prediction High Importance (F1, macro): \"+str(priorityAvgf1High/numHighInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"> Priority Label Prediction Low Importance (F1, macro): \"+str(priorityAvgf1Low/numLowInformationTypes)+\"\\n\")\n",
    "resultsFile.write(\"\\n\")\n",
    "\n",
    "'''\n",
    "Priority Label Prediction (F1, macro): 0.17789293558612237\n",
    "Priority Label Prediction High Importance (F1, macro): 0.06457810783990874\n",
    "Priority Label Prediction Low Importance (F1, macro): 0.21367656540071608\n",
    "// Subset of selected.json\n",
    "Priority Label Prediction (F1, macro): 0.18414638169354325\n",
    "Priority Label Prediction High Importance (F1, macro): 0.07393693861085165\n",
    "Priority Label Prediction Low Importance (F1, macro): 0.2189493637196564\n",
    "// All selected.json loaded\n",
    "Priority Label Prediction (F1, macro): 0.329702967947908\n",
    "Priority Label Prediction High Importance (F1, macro): 0.16318618536713062\n",
    "Priority Label Prediction Low Importance (F1, macro): 0.3822872150786798\n",
    "\n",
    "// Counting categories properly\n",
    "Priority Label Prediction (F1, macro): 0.21481839425257468\n",
    "Priority Label Prediction High Importance (F1, macro): 0.14300090499390425\n",
    "Priority Label Prediction Low Importance (F1, macro): 0.23749760138689163\n",
    "\n",
    "// Tidying code passing / dataframes\n",
    "Priority Label Prediction (F1, macro): 0.2692907991421786\n",
    "Priority Label Prediction High Importance (F1, macro): 0.15697230759182423\n",
    "Priority Label Prediction Low Importance (F1, macro): 0.30475979647386947\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --------------------------------------------------\n",
    "# TREC-IS 2020-A\n",
    "# Task 1\n",
    "# Information Priority Level\n",
    "# Per Information Type Performance\n",
    "# --------------------------------------------------\n",
    "# F1 per information type (macro averaged), higher is better\n",
    "# Macro average (categories have equal weight)\n",
    "\n",
    "N = len(informationTypes2Index)\n",
    "ind = np.arange(N)\n",
    "\n",
    "priorityCatF1Values = []\n",
    "categoryLabels = []\n",
    "for categoryId in informationTypes2Index.keys():\n",
    "    groundTruthPriorities = category2GroundTruthPriority[categoryId]\n",
    "    predictedPriorities = category2PredictedPriority[categoryId]\n",
    "    priorityCatF1 = f1_score(groundTruthPriorities, predictedPriorities, average='macro')\n",
    "    categoryLabels.append(categoryId)\n",
    "    priorityCatF1Values.append(priorityCatF1);\n",
    "    \n",
    "width = 0.90       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, priorityCatF1Values, width)\n",
    "\n",
    "plt.ylabel('Priorty Label Prediction F1 (higher is better)')\n",
    "plt.title('Priorty Label Prediction F1 Per Information Type')\n",
    "plt.xticks(ind, categoryLabels, rotation='vertical')\n",
    "plt.yticks(np.arange(0, 1, 0.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run & NDCG & AW-H & AW-A & CF1-H & CF1-A & CAcc & PErr-H & PErr-A \\\\\n",
      "calamityrun & 0.5107 & -0.6954 & -0.3541 & 0.2031 & 0.2769 & 0.9250 & 0.1539 & 0.2445 \\\\\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the evaluation table row in latex\n",
    "print(\"Run & NDCG & AW-H & AW-A & CF1-H & CF1-A & CAcc & PErr-H & PErr-A \\\\\\\\\")\n",
    "\n",
    "resultLine = (str.format('{0:.4f}', system_ndcg_micro)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}', totalHighImportWorth)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}', AccumulatedAlertWorth)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}',avgF1High/numHighInformationTypes)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}',avgF1/numInformationTypes)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}',avgAccuracy/numInformationTypes)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}',priorityAvgf1High/numHighInformationTypes)+\n",
    "     \" & \"+\n",
    "     str.format('{0:.4f}',priorityAvgf1/len(informationTypes2Index))+\n",
    "     \" \\\\\\\\\")\n",
    "\n",
    "print(runName+\" & \"+resultLine)\n",
    "\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(\"LATEX\"+\"\\n\")\n",
    "resultsFile.write(\"--------------------------------------------------\"+\"\\n\")\n",
    "resultsFile.write(runName+\" & \"+resultLine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Done\n",
    "resultsFile.close() \n",
    "perTopicFile.close()\n",
    "perEventFile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nRun &       NDCG &   AW-H &   AW-A &   CF1-H &   CF1-A &  CAcc &  PErr-H & PErr-A \\\\\\nmarksrun & 0.3708 & 0.0000 & 0.0000 & 0.0000 & 0.0000 ›& 0.9287 & 0.0646 & 0.1779 \\\\\\nmarksrun & 0.3695 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.9287 & 0.0646 & 0.1779 \\\\\\nmarksrun & 0.3705 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.9285 & 0.0640 & 0.1505 \\\\\\n\\n\\nmarksrun2 & 0.5440 & -0.1474 & -0.0739 & 0.1090 & 0.1872 & 0.9201 & 0.1569 & 0.2731 \\\\ priority_y -> x\\nmarksrun2 & 0.5453 & -0.1459 & -0.0731 & 0.1037 & 0.1884 & 0.9200 & 0.1557 & 0.2740 \\\\ work on label map\\nmarksrun2 & 0.5418 & -0.1505 & -0.0754 & 0.1151 & 0.1935 & 0.9203 & 0.1570 & 0.2693 \\\\ label/eventID tidy\\nmarksrun2 & 0.5538 & -0.1505 & -0.0754 & 0.1063 & 0.1954 & 0.9213 & 0.1565 & 0.2715 \\\\ eventID passing properly\\nmarksrun2 & 0.5825 & -0.1459 & -0.0731 & 0.1284 & 0.2172 & 0.9259 & 0.1549 & 0.2770 \\\\ labels passing properly\\nmarksrun2 & 0.5527 & -0.2765 & -0.1382 & 0.0279 & 0.0645 & 0.9122 & 0.1125 & 0.2366 \\\\ tidying input labels df\\nmarksrun2 & 0.5892 & -0.1612 & -0.0808 & 0.1452 & 0.2225 & 0.9246 & 0.1508 & 0.3074 \\\\\\nmarksrun2 & 0.4947 & -0.2381 & -0.1207 & 0.1892 & 0.2318 & 0.9244 & 0.1679 & 0.2900 \\\\ BERT\\nmarksrun2 & 0.4882 & -0.1647 & -0.0841 & 0.1890 & 0.2446 & 0.9258 & 0.1470 & 0.2351 \\\\\\nmarksrun2 & 0.4826 & -0.2381 & -0.1209 & 0.1899 & 0.2345 & 0.9242 & 0.1443 & 0.2376 \\\\ 200 embed\\n\\nmarksrun2 & 0.4009 & -0.0654 & -0.0327 & 0.0024 & 0.1151 & 0.9311 & 0.0716 & 0.2231 \\\\ run0.json\\nmarksrun2 & 0.4032 & -0.2473 & -0.1241 & 0.0531 & 0.1310 & 0.9254 & 0.0696 & 0.2169 \\\\ run + run0.json\\nmarksrun2 & 0.4049 & -0.2357 & -0.1183 & 0.0988 & 0.1472 & 0.9256 & 0.0843 & 0.2184 \\\\ combined run+run0\\nmarksrun2 & 0.4356 & -0.2606 & -0.1323 & 0.1242 & 0.2357 & 0.9256 & 0.1116 & 0.2175 \\\\ missing 4\\n\\nmarksrun2   & 0.4276 & -0.2652 & -0.1345 & 0.1159 & 0.2389 & 0.9256 & 0.1068 & 0.2121 \\\\\\ncalamityrun & 0.4323 & -0.2719 & -0.1381 & 0.1003 & 0.2174 & 0.9254 & 0.0975 & 0.2354 \\\\\\ncalamityrun & 0.4246 & -0.2688 & -0.1366 & 0.0971 & 0.2329 & 0.9247 & 0.0938 & 0.2046 \\\\ - tfidf\\ncalamityrun & 0.4249 & -0.2688 & -0.1366 & 0.0971 & 0.2329 & 0.9247 & 0.0938 & 0.2046 \\\\ features-tfidf\\ncalamityrun & 0.4385 & -0.2596 & -0.1316 & 0.1205 & 0.2331 & 0.9253 & 0.1236 & 0.2248 \\\\ features-offset-tfidf\\ncalamityrun & 0.4431 & -0.2626 & -0.1332 & 0.1266 & 0.2528 & 0.9261 & 0.1127 & 0.2251 \\\\ +tfidf\\ncalamityrun & 0.4369 & -0.2626 & -0.1332 & 0.1256 & 0.2431 & 0.9260 & 0.1086 & 0.2152 \\\\ +offset\\ncalamityrun & 0.4366 & -0.2626 & -0.1332 & 0.1256 & 0.2431 & 0.9260 & 0.1086 & 0.2152 \\\\\\ncalamityrun & 0.4601 & -0.2629 & -0.1333 & 0.1312 & 0.2497 & 0.9270 & 0.1092 & 0.2172 \\\\ +athens\\ncalamityrun & 0.4900 & -0.2567 & -0.1303 & 0.1985 & 0.2810 & 0.9291 & 0.1506 & 0.2499 \\\\ all events from other runs\\ncalamityrun & 0.4923 & -0.4201 & -0.2137 & 0.1907 & 0.2771 & 0.9290 & 0.1477 & 0.2330 \\\\ both run files\\ncalamityrun & 0.5308 & -0.2661 & -0.1350 & 0.2202 & 0.3014 & 0.9265 & 0.1637 & 0.2774 \\\\ new run on selected\\ncalamityrun & 0.5131 & -0.2465 & -0.1250 & 0.2085 & 0.2915 & 0.9265 & 0.1652 & 0.2727 \\\\ tidy label duplicates\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Run &       NDCG &   AW-H &   AW-A &   CF1-H &   CF1-A &  CAcc &  PErr-H & PErr-A \\\\\n",
    "marksrun & 0.3708 & 0.0000 & 0.0000 & 0.0000 & 0.0000 ›& 0.9287 & 0.0646 & 0.1779 \\\\\n",
    "marksrun & 0.3695 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.9287 & 0.0646 & 0.1779 \\\\\n",
    "marksrun & 0.3705 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.9285 & 0.0640 & 0.1505 \\\\\n",
    "\n",
    "\n",
    "marksrun2 & 0.5440 & -0.1474 & -0.0739 & 0.1090 & 0.1872 & 0.9201 & 0.1569 & 0.2731 \\\\ priority_y -> x\n",
    "marksrun2 & 0.5453 & -0.1459 & -0.0731 & 0.1037 & 0.1884 & 0.9200 & 0.1557 & 0.2740 \\\\ work on label map\n",
    "marksrun2 & 0.5418 & -0.1505 & -0.0754 & 0.1151 & 0.1935 & 0.9203 & 0.1570 & 0.2693 \\\\ label/eventID tidy\n",
    "marksrun2 & 0.5538 & -0.1505 & -0.0754 & 0.1063 & 0.1954 & 0.9213 & 0.1565 & 0.2715 \\\\ eventID passing properly\n",
    "marksrun2 & 0.5825 & -0.1459 & -0.0731 & 0.1284 & 0.2172 & 0.9259 & 0.1549 & 0.2770 \\\\ labels passing properly\n",
    "marksrun2 & 0.5527 & -0.2765 & -0.1382 & 0.0279 & 0.0645 & 0.9122 & 0.1125 & 0.2366 \\\\ tidying input labels df\n",
    "marksrun2 & 0.5892 & -0.1612 & -0.0808 & 0.1452 & 0.2225 & 0.9246 & 0.1508 & 0.3074 \\\\\n",
    "marksrun2 & 0.4947 & -0.2381 & -0.1207 & 0.1892 & 0.2318 & 0.9244 & 0.1679 & 0.2900 \\\\ BERT\n",
    "marksrun2 & 0.4882 & -0.1647 & -0.0841 & 0.1890 & 0.2446 & 0.9258 & 0.1470 & 0.2351 \\\\\n",
    "marksrun2 & 0.4826 & -0.2381 & -0.1209 & 0.1899 & 0.2345 & 0.9242 & 0.1443 & 0.2376 \\\\ 200 embed\n",
    "\n",
    "marksrun2 & 0.4009 & -0.0654 & -0.0327 & 0.0024 & 0.1151 & 0.9311 & 0.0716 & 0.2231 \\\\ run0.json\n",
    "marksrun2 & 0.4032 & -0.2473 & -0.1241 & 0.0531 & 0.1310 & 0.9254 & 0.0696 & 0.2169 \\\\ run + run0.json\n",
    "marksrun2 & 0.4049 & -0.2357 & -0.1183 & 0.0988 & 0.1472 & 0.9256 & 0.0843 & 0.2184 \\\\ combined run+run0\n",
    "marksrun2 & 0.4356 & -0.2606 & -0.1323 & 0.1242 & 0.2357 & 0.9256 & 0.1116 & 0.2175 \\\\ missing 4\n",
    "\n",
    "marksrun2   & 0.4276 & -0.2652 & -0.1345 & 0.1159 & 0.2389 & 0.9256 & 0.1068 & 0.2121 \\\\\n",
    "calamityrun & 0.4323 & -0.2719 & -0.1381 & 0.1003 & 0.2174 & 0.9254 & 0.0975 & 0.2354 \\\\\n",
    "calamityrun & 0.4246 & -0.2688 & -0.1366 & 0.0971 & 0.2329 & 0.9247 & 0.0938 & 0.2046 \\\\ - tfidf\n",
    "calamityrun & 0.4249 & -0.2688 & -0.1366 & 0.0971 & 0.2329 & 0.9247 & 0.0938 & 0.2046 \\\\ features-tfidf\n",
    "calamityrun & 0.4385 & -0.2596 & -0.1316 & 0.1205 & 0.2331 & 0.9253 & 0.1236 & 0.2248 \\\\ features-offset-tfidf\n",
    "calamityrun & 0.4431 & -0.2626 & -0.1332 & 0.1266 & 0.2528 & 0.9261 & 0.1127 & 0.2251 \\\\ +tfidf\n",
    "calamityrun & 0.4369 & -0.2626 & -0.1332 & 0.1256 & 0.2431 & 0.9260 & 0.1086 & 0.2152 \\\\ +offset\n",
    "calamityrun & 0.4366 & -0.2626 & -0.1332 & 0.1256 & 0.2431 & 0.9260 & 0.1086 & 0.2152 \\\\\n",
    "calamityrun & 0.4601 & -0.2629 & -0.1333 & 0.1312 & 0.2497 & 0.9270 & 0.1092 & 0.2172 \\\\ +athens\n",
    "calamityrun & 0.4900 & -0.2567 & -0.1303 & 0.1985 & 0.2810 & 0.9291 & 0.1506 & 0.2499 \\\\ all events from other runs\n",
    "calamityrun & 0.4923 & -0.4201 & -0.2137 & 0.1907 & 0.2771 & 0.9290 & 0.1477 & 0.2330 \\\\ both run files\n",
    "calamityrun & 0.5308 & -0.2661 & -0.1350 & 0.2202 & 0.3014 & 0.9265 & 0.1637 & 0.2774 \\\\ new run on selected\n",
    "calamityrun & 0.5131 & -0.2465 & -0.1250 & 0.2085 & 0.2915 & 0.9265 & 0.1652 & 0.2727 \\\\ tidy label duplicates\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>run</th>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>...</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "      <td>marksrun2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NDCG</th>\n",
       "      <td>0.3695</td>\n",
       "      <td>0.3705</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.3746</td>\n",
       "      <td>0.3741</td>\n",
       "      <td>0.3973</td>\n",
       "      <td>0.3993</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5124</td>\n",
       "      <td>0.4983</td>\n",
       "      <td>0.4976</td>\n",
       "      <td>0.4981</td>\n",
       "      <td>0.4942</td>\n",
       "      <td>0.4983</td>\n",
       "      <td>0.4844</td>\n",
       "      <td>0.4873</td>\n",
       "      <td>0.4892</td>\n",
       "      <td>0.4826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlertWorth-High</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2304</td>\n",
       "      <td>-0.2411</td>\n",
       "      <td>-0.2319</td>\n",
       "      <td>-0.2319</td>\n",
       "      <td>-0.2396</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.235</td>\n",
       "      <td>-0.2365</td>\n",
       "      <td>-0.2319</td>\n",
       "      <td>-0.2381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AlertWorth-All</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>-0.1225</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.1216</td>\n",
       "      <td>-0.1193</td>\n",
       "      <td>-0.1193</td>\n",
       "      <td>-0.1202</td>\n",
       "      <td>-0.1176</td>\n",
       "      <td>-0.1209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CF1-H (High-Type Precision)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>0.1947</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>0.1952</td>\n",
       "      <td>0.1748</td>\n",
       "      <td>0.1828</td>\n",
       "      <td>0.1978</td>\n",
       "      <td>0.1831</td>\n",
       "      <td>0.1691</td>\n",
       "      <td>0.1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CF1-A (Type Overall Precision)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0253</td>\n",
       "      <td>0.098</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2647</td>\n",
       "      <td>0.2437</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>0.2491</td>\n",
       "      <td>0.2463</td>\n",
       "      <td>0.2368</td>\n",
       "      <td>0.2349</td>\n",
       "      <td>0.2359</td>\n",
       "      <td>0.2453</td>\n",
       "      <td>0.2345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAcc (Type Accuracy)</th>\n",
       "      <td>0.9287</td>\n",
       "      <td>0.9285</td>\n",
       "      <td>0.9285</td>\n",
       "      <td>0.9285</td>\n",
       "      <td>0.9285</td>\n",
       "      <td>0.9287</td>\n",
       "      <td>0.9287</td>\n",
       "      <td>0.9292</td>\n",
       "      <td>0.9307</td>\n",
       "      <td>0.9319</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9265</td>\n",
       "      <td>0.9254</td>\n",
       "      <td>0.9249</td>\n",
       "      <td>0.9249</td>\n",
       "      <td>0.9255</td>\n",
       "      <td>0.9245</td>\n",
       "      <td>0.9248</td>\n",
       "      <td>0.9244</td>\n",
       "      <td>0.9256</td>\n",
       "      <td>0.9242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PErr-H (High-level divergence)</th>\n",
       "      <td>0.0646</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.0546</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>0.0739</td>\n",
       "      <td>0.0733</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.0855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1729</td>\n",
       "      <td>0.1678</td>\n",
       "      <td>0.1668</td>\n",
       "      <td>0.1668</td>\n",
       "      <td>0.1519</td>\n",
       "      <td>0.1503</td>\n",
       "      <td>0.1517</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.1262</td>\n",
       "      <td>0.1443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PErr-A (Priority Divergence)</th>\n",
       "      <td>0.1779</td>\n",
       "      <td>0.1505</td>\n",
       "      <td>0.1505</td>\n",
       "      <td>0.1505</td>\n",
       "      <td>0.1686</td>\n",
       "      <td>0.1779</td>\n",
       "      <td>0.1841</td>\n",
       "      <td>0.1636</td>\n",
       "      <td>0.1713</td>\n",
       "      <td>0.2017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2994</td>\n",
       "      <td>0.2761</td>\n",
       "      <td>0.2832</td>\n",
       "      <td>0.2832</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>0.2485</td>\n",
       "      <td>0.242</td>\n",
       "      <td>0.2541</td>\n",
       "      <td>0.2408</td>\n",
       "      <td>0.2376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Notes</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Multiple labels now mapping</td>\n",
       "      <td></td>\n",
       "      <td>Fixed label capitalisation</td>\n",
       "      <td>Adding events in</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>offset</td>\n",
       "      <td>all-jsonl + new features</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>crit.8</td>\n",
       "      <td>All features -1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200-embed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        0           1           2   \\\n",
       "run                             marksrun2   marksrun2   marksrun2    \n",
       "NDCG                                0.3695      0.3705      0.3723   \n",
       "AlertWorth-High                          0           0           0   \n",
       "AlertWorth-All                           0           0           0   \n",
       "CF1-H (High-Type Precision)              0           0           0   \n",
       "CF1-A (Type Overall Precision)           0           0           0   \n",
       "CAcc (Type Accuracy)                0.9287      0.9285      0.9285   \n",
       "PErr-H (High-level divergence)      0.0646       0.064       0.064   \n",
       "PErr-A (Priority Divergence)        0.1779      0.1505      0.1505   \n",
       "Notes                                                                \n",
       "\n",
       "                                        3                             4   \\\n",
       "run                             marksrun2                     marksrun2    \n",
       "NDCG                                 0.371                         0.369   \n",
       "AlertWorth-High                          0                             0   \n",
       "AlertWorth-All                           0                             0   \n",
       "CF1-H (High-Type Precision)              0                             0   \n",
       "CF1-A (Type Overall Precision)           0                             0   \n",
       "CAcc (Type Accuracy)                0.9285                        0.9285   \n",
       "PErr-H (High-level divergence)       0.064                        0.0546   \n",
       "PErr-A (Priority Divergence)        0.1505                        0.1686   \n",
       "Notes                                        Multiple labels now mapping   \n",
       "\n",
       "                                        5                            6   \\\n",
       "run                             marksrun2                    marksrun2    \n",
       "NDCG                                 0.369                       0.3746   \n",
       "AlertWorth-High                          0                            0   \n",
       "AlertWorth-All                           0                            0   \n",
       "CF1-H (High-Type Precision)              0                            0   \n",
       "CF1-A (Type Overall Precision)           0                            0   \n",
       "CAcc (Type Accuracy)                0.9287                       0.9287   \n",
       "PErr-H (High-level divergence)      0.0646                       0.0739   \n",
       "PErr-A (Priority Divergence)        0.1779                       0.1841   \n",
       "Notes                                        Fixed label capitalisation   \n",
       "\n",
       "                                               7           8           9   \\\n",
       "run                                    marksrun2   marksrun2   marksrun2    \n",
       "NDCG                                       0.3741      0.3973      0.3993   \n",
       "AlertWorth-High                                 0      -0.002     -0.0013   \n",
       "AlertWorth-All                                  0      -0.001     -0.0007   \n",
       "CF1-H (High-Type Precision)                 0.022      0.0017       0.095   \n",
       "CF1-A (Type Overall Precision)             0.0323      0.0253       0.098   \n",
       "CAcc (Type Accuracy)                       0.9292      0.9307      0.9319   \n",
       "PErr-H (High-level divergence)             0.0733      0.0776      0.0855   \n",
       "PErr-A (Priority Divergence)               0.1636      0.1713      0.2017   \n",
       "Notes                            Adding events in                           \n",
       "\n",
       "                                ...          44                         45  \\\n",
       "run                             ...  marksrun2                  marksrun2    \n",
       "NDCG                            ...      0.5124                     0.4983   \n",
       "AlertWorth-High                 ...     -0.2304                    -0.2411   \n",
       "AlertWorth-All                  ...      -0.117                    -0.1225   \n",
       "CF1-H (High-Type Precision)     ...      0.2072                     0.1947   \n",
       "CF1-A (Type Overall Precision)  ...      0.2647                     0.2437   \n",
       "CAcc (Type Accuracy)            ...      0.9265                     0.9254   \n",
       "PErr-H (High-level divergence)  ...      0.1729                     0.1678   \n",
       "PErr-A (Priority Divergence)    ...      0.2994                     0.2761   \n",
       "Notes                           ...      offset   all-jsonl + new features   \n",
       "\n",
       "                                        46          47          48  \\\n",
       "run                             marksrun2   marksrun2   marksrun2    \n",
       "NDCG                                0.4976      0.4981      0.4942   \n",
       "AlertWorth-High                    -0.2319     -0.2319     -0.2396   \n",
       "AlertWorth-All                      -0.118      -0.118     -0.1216   \n",
       "CF1-H (High-Type Precision)         0.1952      0.1952      0.1748   \n",
       "CF1-A (Type Overall Precision)      0.2491      0.2491      0.2463   \n",
       "CAcc (Type Accuracy)                0.9249      0.9249      0.9255   \n",
       "PErr-H (High-level divergence)      0.1668      0.1668      0.1519   \n",
       "PErr-A (Priority Divergence)        0.2832      0.2832      0.2836   \n",
       "Notes                                  NaN         NaN         NaN   \n",
       "\n",
       "                                        49                 50          51  \\\n",
       "run                             marksrun2          marksrun2   marksrun2    \n",
       "NDCG                                0.4983             0.4844      0.4873   \n",
       "AlertWorth-High                     -0.235             -0.235     -0.2365   \n",
       "AlertWorth-All                     -0.1193            -0.1193     -0.1202   \n",
       "CF1-H (High-Type Precision)         0.1828             0.1978      0.1831   \n",
       "CF1-A (Type Overall Precision)      0.2368             0.2349      0.2359   \n",
       "CAcc (Type Accuracy)                0.9245             0.9248      0.9244   \n",
       "PErr-H (High-level divergence)      0.1503             0.1517       0.149   \n",
       "PErr-A (Priority Divergence)        0.2485              0.242      0.2541   \n",
       "Notes                               crit.8   All features -1          NaN   \n",
       "\n",
       "                                        52          53  \n",
       "run                             marksrun2   marksrun2   \n",
       "NDCG                                0.4892      0.4826  \n",
       "AlertWorth-High                    -0.2319     -0.2381  \n",
       "AlertWorth-All                     -0.1176     -0.1209  \n",
       "CF1-H (High-Type Precision)         0.1691      0.1899  \n",
       "CF1-A (Type Overall Precision)      0.2453      0.2345  \n",
       "CAcc (Type Accuracy)                0.9256      0.9242  \n",
       "PErr-H (High-level divergence)      0.1262      0.1443  \n",
       "PErr-A (Priority Divergence)        0.2408      0.2376  \n",
       "Notes                                  NaN   200-embed  \n",
       "\n",
       "[10 rows x 54 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare Algorithms\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "# Set the figure size\n",
    "rcParams['figure.figsize'] = 20,10\n",
    "data = pd.read_csv('../results2.txt', sep=\"&\")\n",
    "\n",
    "\n",
    "data.columns = [\"run\", \"NDCG\", \"AlertWorth-High\" , \"AlertWorth-All\" , \"CF1-H (High-Type Precision)\" , \"CF1-A (Type Overall Precision)\" , \"CAcc (Type Accuracy)\" , \"PErr-H (High-level divergence)\", \"PErr-A (Priority Divergence)\", \"Notes\"]\n",
    "\n",
    "data.transpose()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax = sns.lineplot(data=data, palette=\"Dark2\", linewidth=2, linestyle='--')\n",
    "ax.set(xlabel=\"Run #\", ylabel = \"Score\")\n",
    "\n",
    "# Increase the font-size for the legend\n",
    "#fig.suptitle('Marks runs')\n",
    "\n",
    "\n",
    "# x coordinates for the lines\n",
    "xcoords = [0.1, 0.3, 0.5]\n",
    "# colors for the lines\n",
    "colors = ['r','k','b','c','m','r','k','b','c','m','y','r','m','r','k','b','c','m','y','r','k','b','c','m','y','r','k','b','c','m','y','r','k','b','c','m','y','r','k','b','c','m','y']\n",
    "\n",
    "\n",
    "i = -1    \n",
    "for row, c, in zip(data.iterrows(),colors):\n",
    "    #print(row.index)\n",
    "    i = i+1\n",
    "    if(len(row[1].Notes) != 1):\n",
    "        ax.axvline(x=i, label=str(i) + \":\" + row[1].Notes , c=colors[i], linestyle='-', linewidth=2)\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.0, 1), fontsize='xx-large', title_fontsize='30', loc='upper left')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#plt.savefig('../../../0-data/screenshots/results.png',dpi=300)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../../../0-data/screenshots/benchmark_models_performance.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-cea14c9c0213>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../../../0-data/screenshots/benchmark_models_performance.png'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    857\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   2309\u001b[0m                 \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2311\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2209\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2210\u001b[0;31m                 result = print_method(\n\u001b[0m\u001b[1;32m   2211\u001b[0m                     \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2212\u001b[0m                     \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[1;32m    509\u001b[0m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m         mpl.image.imsave(\n\u001b[0m\u001b[1;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m             dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dpi\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1605\u001b[0;31m         \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpil_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2146\u001b[0m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2147\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2148\u001b[0;31m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2150\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../../0-data/screenshots/benchmark_models_performance.png'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results\n",
    "\n",
    "# Set the figure size\n",
    "rcParams['figure.figsize'] = 20,15\n",
    "\n",
    "# Seaborn theme \n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Read the results.txt file in\n",
    "data = pd.read_csv('../results2.txt', sep=\"&\", header=None)\n",
    "\n",
    "# Set the column names\n",
    "data.columns = [\"run\", \"NDCG\", \"AlertWorth-High\" , \"AlertWorth-All\" , \"CF1-H (High-Type Precision)\" , \"CF1-A (Type Overall Precision)\" , \"CAcc (Type Accuracy)\" , \"PErr-H (High-level divergence)\", \"PErr-A (Priority Divergence)\", \"Notes\"]\n",
    "\n",
    "\n",
    "# Take a rolling mean\n",
    "data = data.rolling(5).mean()\n",
    "\n",
    "sns.lineplot(data=data, palette=\"Set3\", linewidth=3)\n",
    "\n",
    "# Increase the font-size for the legend\n",
    "#fig.suptitle('Marks runs')\n",
    "plt.legend(fontsize='large', title_fontsize='10')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('../../../0-data/screenshots/benchmark_models_performance.png',dpi=300)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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