domain-adaptation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domain adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import fasttext\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import random\n",
    "import re\n",
    "import scipy.stats\n",
    "import uuid\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"../data/\"\n",
    "ANNOTATION_DIR = DATADIR + \"annotation/\"\n",
    "DISTANCE = \"distance\"\n",
    "FACEMASK = \"mondkapje\"\n",
    "TESTING = \"testing\"\n",
    "VACCINATION = \"vaccin\"\n",
    "FILEFASTTEXT = \"fasttext.csv\"\n",
    "IDSTR = \"id_str\"\n",
    "IRRELEVANT = \"IRRELEVANT\"\n",
    "NEUTRAL = \"NEUTRAL\"\n",
    "ANDERS = \"ANDERS\"\n",
    "EENS = \"EENS\"\n",
    "ONEENS = \"ONEENS\"\n",
    "SUPPORTS = \"SUPPORTS\"\n",
    "REJECTS = \"REJECTS\"\n",
    "LABEL = \"label\"\n",
    "TEXT = \"text\"\n",
    "USER = \"user\"\n",
    "LABELPREFIX = \"__label__\"\n",
    "LARGEINT = 9999999999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIM = 300\n",
    "EPOCH = 200\n",
    "LR = 0.2\n",
    "N = 10\n",
    "TRAIN = \"TRAIN\"+str(int(random.random()*LARGEINT))\n",
    "TEST = \"TEST\"+str(int(random.random()*LARGEINT))\n",
    "VALIDATION = \"VALIDATION\"+str(int(random.random()*LARGEINT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanup(text):\n",
    "    text = re.sub(r\"\\\\n\",\" \",text)\n",
    "    text = re.sub(r\"https://\\S+\",\"\",text)\n",
    "    text = re.sub(r\"\\s+\",\" \",text)\n",
    "    text = text.strip()\n",
    "    return(text)\n",
    "\n",
    "\n",
    "def tokenize(text):\n",
    "    return(\" \".join(TweetTokenizer().tokenize(text)))\n",
    "\n",
    "\n",
    "def preprocess(text):\n",
    "    return(tokenize(cleanup(text)).lower())\n",
    "\n",
    "\n",
    "def select_annotations(tweets, annotations, target_annotator):\n",
    "    fasttext_data = {}\n",
    "    for i in range(0,len(annotations)):\n",
    "        annotator = annotations.iloc[i][0]\n",
    "        tweet_id = annotations.iloc[i][2]\n",
    "        if annotator == target_annotator and tweet_id in list(tweets.index):\n",
    "            tweet_user = tweets.loc[tweet_id][1]\n",
    "            tweet_label = annotations.iloc[i][4]\n",
    "            if tweet_label == NEUTRAL: tweet_label = IRRELEVANT\n",
    "            if tweet_label == ANDERS: tweet_label = IRRELEVANT\n",
    "            if tweet_label == EENS: tweet_label = SUPPORTS\n",
    "            if tweet_label == ONEENS: tweet_label = REJECTS\n",
    "            fasttext_data[tweet_id] = { LABEL:LABELPREFIX+tweet_label,\n",
    "                                        USER:tweet_user,\n",
    "                                        TEXT:preprocess(tweets.loc[tweet_id][TEXT]) }\n",
    "    return({tweet_id: fasttext_data[tweet_id] for tweet_id in sorted(fasttext_data.keys())})\n",
    "\n",
    "\n",
    "def store_fasttext_data_fixed(fasttext_data):\n",
    "    outfile = open(FILEFASTTEXT, \"w\")\n",
    "    seen_texts = {}\n",
    "    fasttext_data_list = []\n",
    "    tweet_ids = []\n",
    "    for tweet_id in fasttext_data:\n",
    "        text = cleanup(fasttext_data[tweet_id][TEXT])\n",
    "        if not text in seen_texts:\n",
    "            print(fasttext_data[tweet_id][LABEL], text, file=outfile)\n",
    "            fasttext_data_list.append(\" \".join([fasttext_data[tweet_id][LABEL], text]))\n",
    "            tweet_ids.append(tweet_id)\n",
    "            seen_texts[text] = True\n",
    "    outfile.close()\n",
    "    return(fasttext_data_list, tweet_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_fasttext_data(topic):\n",
    "    if topic == TESTING:\n",
    "        tweet_file = ANNOTATION_DIR + topic + \"-202003-202012.csv\"\n",
    "    elif topic == FACEMASK:\n",
    "        tweet_file = ANNOTATION_DIR + topic + \"-202003-202103-1000.csv\"\n",
    "    elif topic == VACCINATION:\n",
    "        tweet_file = ANNOTATION_DIR + topic + \"-202001-202101-1000.csv\"\n",
    "    elif topic == DISTANCE:\n",
    "        tweet_file = DATADIR + topic + \"-tweets.csv\"\n",
    "    else:\n",
    "        print(f\"make_fasttext_data: cannot happen! ({topic})\")\n",
    "    tweets = pd.read_csv(tweet_file, index_col=IDSTR)\n",
    "    annotations = pd.read_csv(tweet_file + \".human-labels.txt\", header=None, sep=\" \")        \n",
    "    main_annotator = annotations.iloc[0][0]\n",
    "    fasttext_data = select_annotations(tweets, annotations, main_annotator)\n",
    "    fasttext_data_list, tweet_ids = store_fasttext_data_fixed(fasttext_data)\n",
    "    return(fasttext_data_list, tweet_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeConfusionMatrix(fasttextData,predictionLabels):\n",
    "    goldLabels = pd.Series([fasttextData[i].split()[0] for i in range(0,len(fasttextData))])\n",
    "    predictedLabels = pd.Series([x[0] for row in predictionLabels for x in row[0]])\n",
    "    return(pd.crosstab(goldLabels,predictedLabels))\n",
    "\n",
    "def evaluate(predictionCounts,predictionLabels,fasttextData,printResults=True,printMatrix=False):\n",
    "    caseTotal = 0\n",
    "    pTotal = 0\n",
    "    rTotal = 0\n",
    "    for i in range(0,len(predictionCounts)):\n",
    "        caseTotal += predictionCounts[i][0]\n",
    "        pTotal += predictionCounts[i][0]*predictionCounts[i][1]\n",
    "        rTotal += predictionCounts[i][0]*predictionCounts[i][2]\n",
    "    precision = round(pTotal/caseTotal,3)\n",
    "    recall = round(rTotal/caseTotal,3)\n",
    "    try:\n",
    "        cf = makeConfusionMatrix(fasttextData,predictionLabels)\n",
    "        if printMatrix:\n",
    "            print(cf)\n",
    "        for label in ['__label__REJECTS','__label__SUPPORTS','__label__IRRELEVANT']:\n",
    "            if not label in cf: cf[label] = [0 for i in range(0,len(cf))]\n",
    "        numberOfPredictedSupportLabels = sum([cf.iloc[i]['__label__SUPPORTS'] for i in range(0,len(cf))])\n",
    "        numberOfGoldSupportLabels = sum(cf.loc['__label__SUPPORTS'])\n",
    "        numberOfPredictedRejectLabels = sum([cf.iloc[i]['__label__REJECTS'] for i in range(0,len(cf))])\n",
    "        numberOfGoldRejectLabels = sum(cf.loc['__label__REJECTS'])\n",
    "        if numberOfPredictedSupportLabels > 0 and numberOfGoldRejectLabels > 0 and numberOfGoldSupportLabels > 0:\n",
    "            fraction = (numberOfPredictedRejectLabels/numberOfPredictedSupportLabels)/(numberOfGoldRejectLabels/numberOfGoldSupportLabels)\n",
    "        else:\n",
    "            fraction = LARGEINT\n",
    "    except:\n",
    "        fraction = 0\n",
    "    if printResults: \n",
    "        print(\"cases: {0}; precision: {1:0.3f}; recall: {2:0.3f}; fraction: {3:0.3f}\".format(caseTotal,precision,recall,fraction))\n",
    "    return((precision,fraction))\n",
    "\n",
    "def runFasttext(fasttextData1, fasttextData2, dim=DIM, epoch=EPOCH, lr=LR, n=N, squealFlag=True, maxTrain=None, pretrainedVectors=None):\n",
    "    print(f\"data size 1={len(fasttextData1)}, data size 2={len(fasttextData2)}, dim={dim}, epoch={epoch}, lr={lr}, pretrainedVectors={pretrainedVectors}\")\n",
    "    predictionCountsTest = []\n",
    "    predictionLabelsTest = []\n",
    "    predictionCountsValidation = []\n",
    "    predictionLabelsValidation = []\n",
    "    for fold in range(0,n):\n",
    "        if squealFlag:\n",
    "            clear_output(wait=True)\n",
    "            print(\"starting fold\",fold)\n",
    "        testStart1 = round(fold*len(fasttextData1)/n)\n",
    "        testEnd1 = round((fold+1)*len(fasttextData1)/n)\n",
    "        testStart2 = round(fold*len(fasttextData2)/n)\n",
    "        testEnd2 = round((fold+1)*len(fasttextData2)/n)\n",
    "        if fold < n-1: nextFold = fold+1\n",
    "        else: nextFold = 0\n",
    "        validationStart1 = round(nextFold*len(fasttextData1)/n)\n",
    "        validationEnd1 = round((nextFold+1)*len(fasttextData1)/n)\n",
    "        validationStart2 = round(nextFold*len(fasttextData2)/n)\n",
    "        validationEnd2 = round((nextFold+1)*len(fasttextData2)/n)\n",
    "        trainFile = open(TRAIN,\"w\")\n",
    "        testFile = open(TEST,\"w\")\n",
    "        validationFile = open(VALIDATION,\"w\")\n",
    "        trainData = []\n",
    "        validationData = []\n",
    "        testData = []\n",
    "        for i in range(0,len(fasttextData1)):\n",
    "            data = fasttextData1[i]\n",
    "            if i >= testStart1 and i < testEnd1: \n",
    "                pass\n",
    "            elif i >= validationStart1 and i < validationEnd1: \n",
    "                pass\n",
    "            else: \n",
    "                print(data,file=trainFile)\n",
    "                trainData.append(data)\n",
    "        for i in range(0,len(fasttextData2)):\n",
    "            data = fasttextData2[i]\n",
    "            if i >= testStart2 and i < testEnd2: \n",
    "                print(data,file=testFile)\n",
    "                testData.append(data)\n",
    "            elif i >= validationStart2 and i < validationEnd2: \n",
    "                print(data,file=validationFile)\n",
    "                validationData.append(data)\n",
    "        testFile.close()\n",
    "        trainFile.close()\n",
    "        validationFile.close()\n",
    "        if not maxTrain == None and maxTrain < len(trainData):\n",
    "            trainData = ranSelect(trainData,maxTrain)\n",
    "            trainFile = open(TRAIN,\"w\")\n",
    "            for i in range(0,len(trainData)):\n",
    "                print(trainData[i],file=trainFile)\n",
    "            trainFile.close()\n",
    "        if pretrainedVectors == None:\n",
    "            model = fasttext.train_supervised(TRAIN, dim=dim, epoch=epoch, lr=lr)\n",
    "        else:\n",
    "            model = fasttext.train_supervised(TRAIN, dim=dim, epoch=epoch, lr=lr, pretrainedVectors=pretrainedVectors)\n",
    "        predictionCountsValidation.append([*model.test(VALIDATION)])\n",
    "        predictionLabelsValidation.append(model.predict(validationData, k=3))\n",
    "        predictionCountsTest.append([*model.test(TEST)])\n",
    "        predictionLabelsTest.append(model.predict(testData, k=3))\n",
    "        print(fold, end=\" \")\n",
    "        evaluate([[*model.test(TEST)]], [model.predict(testData)], fasttextData2[testStart2:testEnd2])\n",
    "        os.unlink(TRAIN)\n",
    "        os.unlink(TEST)\n",
    "        os.unlink(VALIDATION)\n",
    "    print(\"\", end=\" \")\n",
    "    precision, fraction = evaluate(predictionCountsTest, predictionLabelsTest, fasttextData2, printMatrix=True)\n",
    "    if squealFlag:\n",
    "        clear_output(wait=True)\n",
    "        print(\"finished\")\n",
    "    return(predictionCountsValidation, predictionLabelsValidation, predictionCountsTest, predictionLabelsTest, precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasttext_data_list_distance, tweet_ids_distance = make_fasttext_data(DISTANCE)\n",
    "fasttext_data_list_facemask, tweet_ids_facemask = make_fasttext_data(FACEMASK)\n",
    "fasttext_data_list_testing, tweet_ids_testing = make_fasttext_data(TESTING)\n",
    "fasttext_data_list_vaccination, tweet_ids_vaccination = make_fasttext_data(VACCINATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5731, 1011, 1181, 1007)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweet_ids_distance), len(tweet_ids_facemask), len(tweet_ids_testing), len(tweet_ids_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_fasttext_data(data, file_name):\n",
    "    with open(file_name, \"wb\") as outfile:\n",
    "        pickle.dump(data, outfile)\n",
    "        outfile.close()\n",
    "        \n",
    "def read_fasttext_data(file_name):\n",
    "    with open(file_name, \"rb\") as infile:\n",
    "        data = pickle.load(infile)\n",
    "        infile.close()\n",
    "    return(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Evaluation within domains (TGTONLY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prediction_labels_list(prediction_labels_10cv):\n",
    "    prediction_labels = []\n",
    "    for i in range(0, len(prediction_labels_10cv)):\n",
    "        prediction_labels.extend([row[0] for row in prediction_labels_10cv[i][0]])\n",
    "    return prediction_labels\n",
    "\n",
    "\n",
    "def compute_accuracy(prediction_labels, fasttext_data_list):\n",
    "    correct = 0\n",
    "    for i in range(0, len(fasttext_data_list)):\n",
    "        gold_label = fasttext_data_list[i].split(\" \")[0]\n",
    "        if prediction_labels[i] == gold_label:\n",
    "            correct += 1\n",
    "    return(round(correct / len(fasttext_data_list), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=5731, data size 2=5731, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 573; precision: 0.792; recall: 0.792; fraction: 1.143\n",
      "1 cases: 573; precision: 0.756; recall: 0.756; fraction: 1.665\n",
      "2 cases: 573; precision: 0.705; recall: 0.705; fraction: 0.742\n",
      "3 cases: 573; precision: 0.646; recall: 0.646; fraction: 0.937\n",
      "4 cases: 574; precision: 0.624; recall: 0.624; fraction: 0.848\n",
      "5 cases: 573; precision: 0.658; recall: 0.658; fraction: 1.019\n",
      "6 cases: 573; precision: 0.634; recall: 0.634; fraction: 0.657\n",
      "7 cases: 573; precision: 0.578; recall: 0.578; fraction: 0.782\n",
      "8 cases: 573; precision: 0.579; recall: 0.579; fraction: 0.769\n",
      "9 cases: 573; precision: 0.585; recall: 0.585; fraction: 0.835\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  601               205                561\n",
      "__label__REJECTS                     216               604                315\n",
      "__label__SUPPORTS                    455               222               2552\n",
      "cases: 5731; precision: 0.656; recall: 0.656; fraction: 0.856\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationDDT,predictionLabelsValidationDDT,predictionCountsTestDDT,predictionLabelsTestDDT = \\\n",
    "    runFasttext(fasttext_data_list_distance, fasttext_data_list_distance, squealFlag=False, pretrainedVectors=\"twiqs-model-2020-exf.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestDDT, f\"predictionLabelsTestDDT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1011, data size 2=1011, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 101; precision: 0.663; recall: 0.663; fraction: 6.176\n",
      "1 cases: 101; precision: 0.703; recall: 0.703; fraction: 0.567\n",
      "2 cases: 101; precision: 0.713; recall: 0.713; fraction: 0.926\n",
      "3 cases: 101; precision: 0.525; recall: 0.525; fraction: 1.351\n",
      "4 cases: 102; precision: 0.569; recall: 0.569; fraction: 2.786\n",
      "5 cases: 101; precision: 0.545; recall: 0.545; fraction: 1.458\n",
      "6 cases: 101; precision: 0.624; recall: 0.624; fraction: 0.876\n",
      "7 cases: 101; precision: 0.485; recall: 0.485; fraction: 0.983\n",
      "8 cases: 101; precision: 0.545; recall: 0.545; fraction: 0.897\n",
      "9 cases: 101; precision: 0.554; recall: 0.554; fraction: 0.265\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  419                65                 74\n",
      "__label__REJECTS                     110               102                 27\n",
      "__label__SUPPORTS                    109                27                 78\n",
      "cases: 1011; precision: 0.592; recall: 0.592; fraction: 0.970\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationFFT,predictionLabelsValidationFFT,predictionCountsTestFFT,predictionLabelsTestFFT = \\\n",
    "    runFasttext(fasttext_data_list_facemask, fasttext_data_list_facemask, squealFlag=False, pretrainedVectors=\"twiqs-model-2020-exf.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestFFT, f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 4.033\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.555; recall: 0.555; fraction: 0.391\n",
      "6 cases: 118; precision: 0.568; recall: 0.568; fraction: 0.767\n",
      "7 cases: 118; precision: 0.602; recall: 0.602; fraction: 0.538\n",
      "8 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.607\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  433                53                145\n",
      "__label__REJECTS                      95                87                 28\n",
      "__label__SUPPORTS                    167                30                143\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.871\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.214\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 3.667\n",
      "3 cases: 118; precision: 0.551; recall: 0.551; fraction: 0.789\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.563; recall: 0.563; fraction: 0.357\n",
      "6 cases: 118; precision: 0.559; recall: 0.559; fraction: 0.793\n",
      "7 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.538\n",
      "8 cases: 118; precision: 0.653; recall: 0.653; fraction: 0.615\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  435                52                144\n",
      "__label__REJECTS                      95                87                 28\n",
      "__label__SUPPORTS                    170                29                141\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.869\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.517; recall: 0.517; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 4.033\n",
      "3 cases: 118; precision: 0.551; recall: 0.551; fraction: 0.750\n",
      "4 cases: 118; precision: 0.627; recall: 0.627; fraction: 0.855\n",
      "5 cases: 119; precision: 0.555; recall: 0.555; fraction: 0.391\n",
      "6 cases: 118; precision: 0.568; recall: 0.568; fraction: 0.767\n",
      "7 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.538\n",
      "8 cases: 118; precision: 0.653; recall: 0.653; fraction: 0.615\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  434                52                145\n",
      "__label__REJECTS                      95                87                 28\n",
      "__label__SUPPORTS                    167                30                143\n",
      "cases: 1181; precision: 0.562; recall: 0.562; fraction: 0.866\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.458; recall: 0.458; fraction: 3.642\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 3.667\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.929\n",
      "5 cases: 119; precision: 0.563; recall: 0.563; fraction: 0.357\n",
      "6 cases: 118; precision: 0.568; recall: 0.568; fraction: 0.767\n",
      "7 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.565\n",
      "8 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.593\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  433                54                144\n",
      "__label__REJECTS                      94                87                 29\n",
      "__label__SUPPORTS                    170                28                142\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.869\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.508; recall: 0.508; fraction: 4.172\n",
      "3 cases: 118; precision: 0.534; recall: 0.534; fraction: 0.750\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.571; recall: 0.571; fraction: 0.379\n",
      "6 cases: 118; precision: 0.559; recall: 0.559; fraction: 0.793\n",
      "7 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.565\n",
      "8 cases: 118; precision: 0.661; recall: 0.661; fraction: 0.593\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  434                52                145\n",
      "__label__REJECTS                      94                88                 28\n",
      "__label__SUPPORTS                    170                29                141\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.871\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.458; recall: 0.458; fraction: 3.642\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 4.033\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.563; recall: 0.563; fraction: 0.357\n",
      "6 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.742\n",
      "7 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.565\n",
      "8 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.571\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.889\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  433                55                143\n",
      "__label__REJECTS                      95                86                 29\n",
      "__label__SUPPORTS                    167                29                144\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.871\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 3.667\n",
      "3 cases: 118; precision: 0.551; recall: 0.551; fraction: 0.750\n",
      "4 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.929\n",
      "5 cases: 119; precision: 0.555; recall: 0.555; fraction: 0.391\n",
      "6 cases: 118; precision: 0.568; recall: 0.568; fraction: 0.767\n",
      "7 cases: 118; precision: 0.602; recall: 0.602; fraction: 0.538\n",
      "8 cases: 118; precision: 0.653; recall: 0.653; fraction: 0.615\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  435                52                144\n",
      "__label__REJECTS                      94                88                 28\n",
      "__label__SUPPORTS                    168                29                143\n",
      "cases: 1181; precision: 0.564; recall: 0.564; fraction: 0.869\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 4.033\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.563; recall: 0.563; fraction: 0.357\n",
      "6 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.742\n",
      "7 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.538\n",
      "8 cases: 118; precision: 0.661; recall: 0.661; fraction: 0.500\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  435                51                145\n",
      "__label__REJECTS                      95                86                 29\n",
      "__label__SUPPORTS                    167                29                144\n",
      "cases: 1181; precision: 0.563; recall: 0.563; fraction: 0.845\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.475; recall: 0.475; fraction: 3.339\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 4.033\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.555; recall: 0.555; fraction: 0.391\n",
      "6 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.733\n",
      "7 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.565\n",
      "8 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.556\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.827\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  435                51                145\n",
      "__label__REJECTS                      95                86                 29\n",
      "__label__SUPPORTS                    169                30                141\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.858\n",
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.466; recall: 0.466; fraction: 3.450\n",
      "1 cases: 118; precision: 0.500; recall: 0.500; fraction: 1.079\n",
      "2 cases: 118; precision: 0.517; recall: 0.517; fraction: 3.667\n",
      "3 cases: 118; precision: 0.542; recall: 0.542; fraction: 0.769\n",
      "4 cases: 118; precision: 0.636; recall: 0.636; fraction: 0.874\n",
      "5 cases: 119; precision: 0.563; recall: 0.563; fraction: 0.357\n",
      "6 cases: 118; precision: 0.568; recall: 0.568; fraction: 0.767\n",
      "7 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.514\n",
      "8 cases: 118; precision: 0.644; recall: 0.644; fraction: 0.593\n",
      "9 cases: 118; precision: 0.585; recall: 0.585; fraction: 0.889\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  433                53                145\n",
      "__label__REJECTS                      95                87                 28\n",
      "__label__SUPPORTS                    169                28                143\n",
      "cases: 1181; precision: 0.561; recall: 0.561; fraction: 0.861\n",
      "[0.561, 0.561, 0.562, 0.561, 0.561, 0.561, 0.564, 0.563, 0.561, 0.561]\n"
     ]
    }
   ],
   "source": [
    "accuracies = []\n",
    "for i in range(0, 10):\n",
    "    predictionCountsValidationTTT, predictionLabelsValidationTTT, predictionCountsTestTTT, predictionLabelsTestTTT, accuracy = \\\n",
    "        runFasttext(fasttext_data_list_testing, fasttext_data_list_testing, squealFlag=False, pretrainedVectors=\"twiqs-model-2020-exf.vec\")\n",
    "    accuracies.append(accuracy)\n",
    "print(accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestTTT, f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1007, data size 2=1007, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 101; precision: 0.663; recall: 0.663; fraction: 4.472\n",
      "1 cases: 100; precision: 0.710; recall: 0.710; fraction: 0.413\n",
      "2 cases: 101; precision: 0.663; recall: 0.663; fraction: 1.057\n",
      "3 cases: 101; precision: 0.693; recall: 0.693; fraction: 0.750\n",
      "4 cases: 101; precision: 0.535; recall: 0.535; fraction: 1.324\n",
      "5 cases: 100; precision: 0.570; recall: 0.570; fraction: 0.444\n",
      "6 cases: 101; precision: 0.624; recall: 0.624; fraction: 1.195\n",
      "7 cases: 101; precision: 0.525; recall: 0.525; fraction: 1.219\n",
      "8 cases: 100; precision: 0.510; recall: 0.510; fraction: 15.484\n",
      "9 cases: 101; precision: 0.584; recall: 0.584; fraction: 6.000\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  500               103                 35\n",
      "__label__REJECTS                     158               100                  7\n",
      "__label__SUPPORTS                     76                16                 12\n",
      "cases: 1007; precision: 0.608; recall: 0.608; fraction: 1.592\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationVVT,predictionLabelsValidationVVT,predictionCountsTestVVT,predictionLabelsTestVVT = \\\n",
    "    runFasttext(fasttext_data_list_vaccination, fasttext_data_list_vaccination, squealFlag=False, pretrainedVectors=\"twiqs-model-2020-exf.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestVVT, f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.561"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracy(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\")), fasttext_data_list_testing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Evaluation of other domains with social distancing data (SRCONLY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_distance = fasttext.train_supervised(\"fasttext-distance.csv\", dim=DIM, epoch=EPOCH, lr=LR, pretrainedVectors=\"twiqs-model-2020-exf.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracy(get_prediction_labels_list([model_distance.predict(fasttext_data_list_distance)]), fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.414"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracy(get_prediction_labels_list([model_distance.predict(fasttext_data_list_facemask)]), fasttext_data_list_facemask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.452"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracy(get_prediction_labels_list([model_distance.predict(fasttext_data_list_testing)]), fasttext_data_list_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.488"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_accuracy(get_prediction_labels_list([model_distance.predict(fasttext_data_list_vaccination)]), fasttext_data_list_vaccination)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting social distancing with facemask data (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_labels(fasttext_data_list):\n",
    "    counts = {}\n",
    "    for i in range(0, len(fasttext_data_list)):\n",
    "        token_list = fasttext_data_list[i].split()\n",
    "        if token_list[0] in counts:\n",
    "            counts[token_list[0]] += 1\n",
    "        else:\n",
    "            counts[token_list[0]] = 1\n",
    "    return(counts)\n",
    "\n",
    "\n",
    "def swap_labels(fasttext_data_list_in):\n",
    "    fasttext_data_list_out = []\n",
    "    for i in range(0, len(fasttext_data_list_in)):\n",
    "        token_list = fasttext_data_list_in[i].split()\n",
    "        if token_list[0] == \"__label__REJECTS\":\n",
    "            token_list[0] = \"__label__SUPPORTS\"\n",
    "        elif token_list[0] == \"__label__SUPPORTS\":\n",
    "            token_list[0] = \"__label__REJECTS\"\n",
    "        fasttext_data_list_out.append(\" \".join(token_list))\n",
    "    return(fasttext_data_list_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationFD,predictionLabelsValidationFD,predictionCountsTestFD,predictionLabelsTestFD = \\\n",
    "        runFasttext(fasttext_data_list_facemask, fasttext_data_list_distance, squealFlag=False, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 101; precision: 0.386; recall: 0.386; fraction: 4.725\n",
      "1 cases: 101; precision: 0.327; recall: 0.327; fraction: 0.833\n",
      "2 cases: 101; precision: 0.376; recall: 0.376; fraction: 0.561\n",
      "3 cases: 101; precision: 0.396; recall: 0.396; fraction: 1.067\n",
      "4 cases: 102; precision: 0.402; recall: 0.402; fraction: 1.899\n",
      "5 cases: 101; precision: 0.495; recall: 0.495; fraction: 0.580\n",
      "6 cases: 101; precision: 0.416; recall: 0.416; fraction: 0.201\n",
      "7 cases: 101; precision: 0.386; recall: 0.386; fraction: 0.164\n",
      "8 cases: 101; precision: 0.396; recall: 0.396; fraction: 0.270\n",
      "9 cases: 101; precision: 0.416; recall: 0.416; fraction: 0.198\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  169               124                265\n",
      "__label__REJECTS                      46                99                 94\n",
      "__label__SUPPORTS                     34                44                136\n",
      "cases: 1011; precision: 0.400; recall: 0.400; fraction: 0.483\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationDFT, predictionLabelsValidationDFT, predictionCountsTestDFT, predictionLabelsTestDFT = \\\n",
    "    runFasttext(fasttext_data_list_distance, fasttext_data_list_facemask, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestDFT, f\"predictionLabelsTestDFT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 118; precision: 0.390; recall: 0.390; fraction: 0.432\n",
      "1 cases: 118; precision: 0.390; recall: 0.390; fraction: 0.478\n",
      "2 cases: 118; precision: 0.483; recall: 0.483; fraction: 0.731\n",
      "3 cases: 118; precision: 0.441; recall: 0.441; fraction: 0.594\n",
      "4 cases: 118; precision: 0.415; recall: 0.415; fraction: 0.762\n",
      "5 cases: 119; precision: 0.471; recall: 0.471; fraction: 0.428\n",
      "6 cases: 118; precision: 0.458; recall: 0.458; fraction: 0.246\n",
      "7 cases: 118; precision: 0.424; recall: 0.424; fraction: 0.211\n",
      "8 cases: 118; precision: 0.483; recall: 0.483; fraction: 0.991\n",
      "9 cases: 118; precision: 0.305; recall: 0.305; fraction: 1.523\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  223                93                315\n",
      "__label__REJECTS                      66                65                 79\n",
      "__label__SUPPORTS                     84                41                215\n",
      "cases: 1181; precision: 0.426; recall: 0.426; fraction: 0.529\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationDTT, predictionLabelsValidationDTT, predictionCountsTestDTT, predictionLabelsTestDTT = \\\n",
    "    runFasttext(fasttext_data_list_distance, fasttext_data_list_testing, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestDTT, f\"predictionLabelsTestDTT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 101; precision: 0.554; recall: 0.554; fraction: 0.845\n",
      "1 cases: 100; precision: 0.530; recall: 0.530; fraction: 0.213\n",
      "2 cases: 101; precision: 0.495; recall: 0.495; fraction: 0.164\n",
      "3 cases: 101; precision: 0.545; recall: 0.545; fraction: 0.109\n",
      "4 cases: 101; precision: 0.426; recall: 0.426; fraction: 0.343\n",
      "5 cases: 100; precision: 0.480; recall: 0.480; fraction: 0.075\n",
      "6 cases: 101; precision: 0.515; recall: 0.515; fraction: 0.293\n",
      "7 cases: 101; precision: 0.465; recall: 0.465; fraction: 0.300\n",
      "8 cases: 100; precision: 0.460; recall: 0.460; fraction: 0.699\n",
      "9 cases: 101; precision: 0.436; recall: 0.436; fraction: 0.882\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  336               126                176\n",
      "__label__REJECTS                      92               112                 61\n",
      "__label__SUPPORTS                     39                19                 46\n",
      "cases: 1007; precision: 0.491; recall: 0.491; fraction: 0.356\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationDVT, predictionLabelsValidationDVT, predictionCountsTestDVT, predictionLabelsTestDVT = \\\n",
    "    runFasttext(fasttext_data_list_distance, fasttext_data_list_vaccination, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestDVT, f\"predictionLabelsTestDVT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestFD = read_fasttext_data(\"predictionLabelsTestFD.pickle\")\n",
    "predictionLabelsTestDD = read_fasttext_data(\"predictionLabelsTestDD.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(data):\n",
    "    plot_data =[]\n",
    "    for key in data:\n",
    "        if re.search(\"^[0-9]+$\", str(key)):\n",
    "            support_count = 0\n",
    "            reject_count = 0\n",
    "            for label in data[key]:\n",
    "                if label == \"__label__SUPPORTS\":\n",
    "                    support_count += 1\n",
    "                if label == \"__label__REJECTS\":\n",
    "                    reject_count += 1\n",
    "            if support_count+reject_count > 0:\n",
    "                plot_data.append((int(key), support_count/(support_count+reject_count)))\n",
    "    plot_data = [x for x in sorted(plot_data, key=lambda x:x[0])]\n",
    "    return(plot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_data_predicted(predictionLabelsTest, tweet_ids, time_factor=4):\n",
    "    predicted = []\n",
    "    for i in range(0, len(predictionLabelsTest)):\n",
    "        for j in range(0, len(predictionLabelsTest[i][0])):\n",
    "            predicted.append(predictionLabelsTest[i][0][j])\n",
    "    data_predicted = {}\n",
    "    for i in range(0, len(tweet_ids)):\n",
    "        summary = str(tweet_ids[i])[0:time_factor]\n",
    "        if summary not in data_predicted:\n",
    "            data_predicted[summary] = []\n",
    "        data_predicted[summary].append(predicted[i][0])\n",
    "    return(data_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_factor = 4\n",
    "data_predicted_fd = make_data_predicted(predictionLabelsTestFD, tweet_ids_distance, time_factor=time_factor)\n",
    "data_predicted_dd = make_data_predicted(predictionLabelsTestDD, tweet_ids_distance, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold = []\n",
    "for tweet in fasttext_data_list_distance:\n",
    "    gold.append(tweet.split()[0])\n",
    "\n",
    "data_gold = {}\n",
    "for i in range(0, len(tweet_ids_distance)):\n",
    "    summary = str(tweet_ids_distance[i])[0:time_factor]\n",
    "    if summary not in data_gold:\n",
    "        data_gold[summary] = []\n",
    "    data_gold[summary].append(gold[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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": [
    "plot_data_predicted_fd = convert(data_predicted_fd)\n",
    "plot_data_predicted_dd = convert(data_predicted_dd)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_fd = [x[0] for x in plot_data_predicted_fd]\n",
    "y_fd = [x[1] for x in plot_data_predicted_fd]\n",
    "x_dd = [x[0] for x in plot_data_predicted_dd]\n",
    "y_dd = [x[1] for x in plot_data_predicted_dd]\n",
    "plt.plot(x_fd, y_fd, label=\"predicted (out-of-domain)\")\n",
    "plt.plot(x_dd, y_dd, label=\"predicted (in-domain)\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting social distancing with facemask data\")\n",
    "plt.savefig(\"facemask-to-distancing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inflate predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_freqs(label_list):\n",
    "    freqs = {}\n",
    "    for label in label_list:\n",
    "        if label in freqs:\n",
    "            freqs[label] += 1\n",
    "        else:\n",
    "            freqs[label] = 1\n",
    "    for label in freqs:\n",
    "        freqs[label] /= len(label_list)\n",
    "    return(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inflate(predictionLabelsTestIn, source_label, target_label, factor):\n",
    "    predictionLabelsTestOut = list(predictionLabelsTestIn)\n",
    "    counter = 0\n",
    "    for fold in predictionLabelsTestOut:\n",
    "        for i in range(0, len(fold[0])):\n",
    "            if fold[0][i][0] == target_label:\n",
    "                for j in range(0, len(fold[0][i])):\n",
    "                    if fold[0][i][j] == source_label:\n",
    "                        break\n",
    "                if fold[0][i][j] != source_label:\n",
    "                    sys.exit(\"cannot happen\")\n",
    "                if fold[1][i][j]*factor > fold[1][i][0]:\n",
    "                    fold[0][i][0], fold[0][i][j] = fold[0][i][j], fold[0][i][0]\n",
    "    return(predictionLabelsTestOut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inflate_plus(predictionLabelsTestIn, source_label, target_label, factor):\n",
    "    predictionLabelsTestOut = list(predictionLabelsTestIn)\n",
    "    counter = 0\n",
    "    for fold in predictionLabelsTestOut:\n",
    "        for i in range(0, len(fold[0])):\n",
    "            if fold[0][i][0] == target_label:\n",
    "                for j in range(0, len(fold[0][i])):\n",
    "                    if fold[0][i][j] == source_label:\n",
    "                        break\n",
    "                if fold[0][i][j] != source_label:\n",
    "                    sys.exit(\"cannot happen\")\n",
    "                if fold[1][i][j]+factor > fold[1][i][0]:\n",
    "                    fold[0][i][0], fold[0][i][j] = fold[0][i][j], fold[0][i][0]\n",
    "    return(predictionLabelsTestOut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__REJECTS': 0.19822020589774908, '__label__SUPPORTS': 0.5632524864770546, '__label__IRRELEVANT': 0.2385273076251963}\n",
      "0.35192069392812886\n"
     ]
    }
   ],
   "source": [
    "freqs = get_freqs(gold)\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__IRRELEVANT': 0.5379514918862328, '__label__SUPPORTS': 0.34252311987436745, '__label__REJECTS': 0.11952538823939976}\n",
      "0.34895568008150796\n"
     ]
    }
   ],
   "source": [
    "FACTOR = 0.9967\n",
    "\n",
    "predictionLabelsTestFDinflated = list(predictionLabelsTestFD)\n",
    "freqs = get_freqs([y[0] for x in inflate_plus(predictionLabelsTestFDinflated, '__label__SUPPORTS', '__label__REJECTS', FACTOR) for y in x[0]])\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": [
    "data_predicted_fd_inflated = make_data_predicted(predictionLabelsTestFDinflated, tweet_ids_distance)\n",
    "plot_data_predicted_fd_inflated = convert(data_predicted_fd_inflated)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_fd = [x[0] for x in plot_data_predicted_fd]\n",
    "y_fd = [x[1] for x in plot_data_predicted_fd]\n",
    "x_dd = [x[0] for x in plot_data_predicted_dd]\n",
    "y_dd = [x[1] for x in plot_data_predicted_dd]\n",
    "x_fd_inflated = [x[0] for x in plot_data_predicted_fd_inflated]\n",
    "y_fd_inflated = [x[1] for x in plot_data_predicted_fd_inflated]\n",
    "plt.plot(x_fd, y_fd, label=\"predicted (out-of-domain)\")\n",
    "plt.plot(x_dd, y_dd, label=\"predicted (in-domain)\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\")\n",
    "plt.plot(x_fd_inflated, y_fd_inflated, label=\"predicted (out-of-domain, inflated\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting social distancing with facemask data\")\n",
    "plt.savefig(\"facemask-to-distancing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical analysis of predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_predicted_fd_dict = { x[0]:x[1] for x in plot_data_predicted_fd }\n",
    "plot_data_predicted_dd_dict = { x[0]:x[1] for x in plot_data_predicted_dd }\n",
    "plot_data_gold_dict = { x[0]:x[1] for x in plot_data_gold }\n",
    "plot_data_predicted_fd_inflated_dict = { x[0]:x[1] for x in plot_data_predicted_fd_inflated }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_common_key_values(dict1, dict2):\n",
    "    values1 = []\n",
    "    values2 = []\n",
    "    for key in sorted(dict1.keys()):\n",
    "        if key in dict2:\n",
    "            values1.append(dict1[key])\n",
    "            values2.append(dict2[key])\n",
    "    return(values1, values2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson r correlation gold vs in-domain: 0.88\n",
      "Pearson r correlation gold vs out-of-domain: 0.47\n",
      "Pearson r correlation gold vs out-of-domain (inflated): -0.06\n"
     ]
    }
   ],
   "source": [
    "values_dd, values_gold_dd = get_common_key_values(plot_data_predicted_dd_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs in-domain:\", round(scipy.stats.linregress(np.array(values_gold_dd), np.array(values_dd)).rvalue, 2))\n",
    "values_fd, values_gold_fd = get_common_key_values(plot_data_predicted_fd_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain:\", round(scipy.stats.linregress(np.array(values_gold_fd), np.array(values_fd)).rvalue, 2))\n",
    "values_fd_inflated, values_gold_fd_inflated = get_common_key_values(plot_data_predicted_fd_inflated_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (inflated):\", round(scipy.stats.linregress(np.array(values_gold_fd), np.array(values_fd_inflated)).rvalue, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def absolute_difference(list1, list2):\n",
    "    absolute_difference = 0\n",
    "    for i in range(0, len(list1)):\n",
    "        absolute_difference += abs(list1[i]-list2[i])\n",
    "    return(absolute_difference/len(list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absolute difference gold vs in-domain: 0.06\n",
      "absolute difference gold vs out-of-domain: 0.42\n",
      "absolute difference gold vs out-of-domain (inflated): 0.18\n"
     ]
    }
   ],
   "source": [
    "print(\"absolute difference gold vs in-domain:\", round(absolute_difference(values_gold_dd, values_dd), 2))\n",
    "print(\"absolute difference gold vs out-of-domain:\", round(absolute_difference(values_gold_fd, values_fd),2))\n",
    "print(\"absolute difference gold vs out-of-domain (inflated):\", round(absolute_difference(values_gold_fd_inflated, values_fd_inflated),2)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting facemasks with social distancing data (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDF,predictionLabelsValidationDF,predictionCountsTestDF,predictionLabelsTestDF = \\\n",
    "        runFasttext(fasttext_data_list_distance, fasttext_data_list_facemask, squealFlag=False, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDFT,predictionLabelsValidationDFT,predictionCountsTestDFT,predictionLabelsTestDFT = \\\n",
    "        runFasttext(fasttext_data_list_distance, fasttext_data_list_facemask, squealFlag=False, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    store_fasttext_data(predictionLabelsTestDFT, \"predictionLabelsTestDFT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDF = read_fasttext_data(\"predictionLabelsTestDF.pickle\")\n",
    "predictionLabelsTestFF = read_fasttext_data(\"predictionLabelsTestFF.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_factor = 5\n",
    "data_predicted_df = make_data_predicted(predictionLabelsTestDF, tweet_ids_facemask, time_factor=time_factor)\n",
    "data_predicted_ff = make_data_predicted(predictionLabelsTestFF, tweet_ids_facemask, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold = []\n",
    "for tweet in fasttext_data_list_facemask:\n",
    "    gold.append(tweet.split()[0])\n",
    "\n",
    "data_gold = {}\n",
    "for i in range(0, len(tweet_ids_facemask)):\n",
    "    summary = str(tweet_ids_facemask[i])[0:time_factor]\n",
    "    if summary not in data_gold:\n",
    "        data_gold[summary] = []\n",
    "    data_gold[summary].append(gold[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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": [
    "plot_data_predicted_df = convert(data_predicted_df)\n",
    "plot_data_predicted_ff = convert(data_predicted_ff)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_df = [x[0] for x in plot_data_predicted_df]\n",
    "y_df = [x[1] for x in plot_data_predicted_df]\n",
    "x_ff = [x[0] for x in plot_data_predicted_ff]\n",
    "y_ff = [x[1] for x in plot_data_predicted_ff]\n",
    "plt.plot(x_df, y_df, label=\"predicted (out-of-domain)\")\n",
    "plt.plot(x_ff, y_ff, label=\"predicted (in-domain)\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting facemask with social distancing data\")\n",
    "plt.savefig(\"distancing-to-facemask.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inflate predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__IRRELEVANT': 0.4809688581314879, '__label__REJECTS': 0.46366782006920415, '__label__SUPPORTS': 0.05536332179930796}\n",
      "8.375\n"
     ]
    }
   ],
   "source": [
    "freqs = get_freqs(gold)\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__IRRELEVANT': 0.27760968229954613, '__label__REJECTS': 0.518910741301059, '__label__SUPPORTS': 0.20347957639939485}\n",
      "2.550185873605948\n"
     ]
    }
   ],
   "source": [
    "FACTOR = 0.9893\n",
    "\n",
    "predictionLabelsTestDFinflated = list(predictionLabelsTestDF)\n",
    "freqs = get_freqs([y[0] for x in inflate_plus(predictionLabelsTestDFinflated, '__label__REJECTS', '__label__SUPPORTS', FACTOR) for y in x[0]])\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_predicted_df_inflated = make_data_predicted(inflate(predictionLabelsTestDFinflated, '__label__REJECTS', '__label__SUPPORTS', FACTOR), tweet_ids_facemask, time_factor=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RWYwziLz3XFaBhu7DixvgPEqrKLCi8njo9W3kfnnsx1RxlrY/PZ9J/dqVWmb79u0899xzdO7cmZEjRzJr1izuvfdewLnPd+PGjRw6dIibbrqJt99+m7i4OB5++GEef/xxxo8fz69+9SvWrFnDz372MwYPHhx0G/fccw9du3Zl6dKlnDlzhuPHjzNt2jQ+/vhjcnJyAFi9ejWffvopH3zwAapK//79Wb9+PXFxcSxevJgPP/yQoqIi0tPT6dChw1nb2LRpU7H5w4YN4y9/+Qtdu3Zl4sSJPPTQQ8yYMSNofLGxsUyZMoXs7GyeeuqpoGVKqm/06NE0aNDAv8989u/fz6RJk9i6dSvx8fF0796dtLQ0AMaOHcuwYcMYPnw48+bN45577mHZsmUAfP3116xZs4bly5fTr18/Nm3axNy5c+nYsSM5OTmkpqYydepULrzwQs6cOUOPHj346KOPSElJKbb9b775hszMTKZOncr48eN59tlnefDBBwHIyMhgw4YNXHVVeR7Pakz5hXu08lC6XJpQ/JmP+e48r6eANjjPU/xf4L/ch8zWOM2aNaNz584ADB06lI0bN/qX+RL0e++9R25uLp07dyY1NZWFCxfy+eefk5eXR4sWLWjZsiUiwtChQ4NuY82aNYwZMwaAmJgY4uPjzyqzevVqVq9eTVpaGunp6eTl5fHpp5+yYcMGBg0aRP369Tn//PPp3z/4c5L3799PYqIzINvRo0c5cuQIXbt2BWD48OGsX1/aozVLV5H63n//fbp160ZiYiKxsbHF/tht3rzZ3zV05513Ftvn/fr1Q0RITk7m4osvJjk5mfPOO4927dqxZ88eAF5++WXS09NJS0tj27ZtQfvNY2Nj/dczOnTo4F8X4KKLLuLLL7+s0L4wpjyeWbcTgO+KwpMeQzlCD3YvVODfmd44D7f9OXA58HcR2aCqxQ6xRWQUMArg0ksvLXWjZR1Jh0vgrV/e6bi4OMD5Ecd1113HSy+9VKxsTk5Opd06pqrcf//93H333cXmz5gxI6Rt1KtXL6R7kmvVqsX33zsfrtLK9+7dm4KCAjIyMnjsscfKrPfMmTP+M4T+/fuTnp4e8r7xlqtTpw4A5513nv+1b7qoqIjdu3czffp0tmzZQqNGjRgxYkTQdtSuXdtfb0xMDEVFP5xAnjx5knr16oUUmzE/xuIt+8ou9COEcoSeDzTzTDfFORL3+iXwmjp24jxdvHVgRao6R1UzVDXDd/RY3ezdu5fNmzcD8NJLL5GVlXVWmczMTDZt2sTOnc5f2xMnTrBjxw5at27N7t27/Xd3BCZ8nx49evDMM88ATuI7duwYDRs2pLCw0F+md+/ezJs3z983/8UXX3DgwAG6dOnC0qVL+fbbbyksLOT1118Puo02bdr444uPj6dRo0Zs2LABgBdeeMF/dJ2UlMTWrVsB+Nvf/uZfPzCeVatWkZOTw9y5c0utzycmJoacnBxycnKYMmUKV199NevWrePw4cOcPn2aV155xV/2mmuuYfHixQAsWrQo6D4vybFjx4iLiyM+Pp6CggLefPPNkNf12bFjB1deeWW51zOmugkloW8BWopIC/dC523A8oAye4EeACJyMc5zLz+rzECrSps2bVi4cCEpKSl89dVX/q4Rr8TERBYsWMDtt99OSkoKmZmZ5OXlUbduXebMmcMNN9xAVlYWzZs3D7qNJ598krVr15KcnEyHDh3Ytm0bjRs3pnPnzlx55ZWMGzeOXr16MWTIEDp16kRycjK33HILhYWFpKenM3jwYFJTU7n55pu59tprg26jb9++xbpBFi5cyLhx40hJSSEnJ4eJEycCcO+99/LMM89wzTXXcOjQIX/57t27k5ubW+JF0ZLqK8kll1zC5MmT6dSpEz179iQ9Pd2/bObMmcyfP5+UlBReeOEFnnzyyVLr8mrfvj1paWm0a9eOkSNH+rvLymPTpk307Nmz3OsZU+2oapn/gOuBHTgPJX7AnTcaGO2+/imwGqf//GNgaFl1dujQQQPl5uaeNa8q7d69W9u1axfRGCrTwIEDdceOHZEOo1r75z//qUOHDo10GJUm0t8hU7qOf/67Nr9vhTa/b0WF6wCytYS8GtLgXKq6ElgZMG+25/WXQK8f+bfFVLJp06axf/9+WrZsGelQqq1Dhw7xpz/9KdJhGFMpqtVoi5GWlJTExx9/HOkwKk2rVq1o1apVpMOo1q677rpIh2BMpbGf/htjTJSwhG6MMVHCEroxxlSRMP9Q1BK6McZEC0voYVSdhs+dOHEib7/9drnr9/G2JVxCiXHFihXFBuAyxvzAEnoF1MThc6dMmVLtfzwTSow33HADy5cv94+CaUxNcrDwu7DWbwndI5qHzx0xYoT/p/1JSUlMmjSJ9PR0kpOTycvLC1pHSW356quvGDhwoP9Xsh999BHgnC0MHz6cXr16kZSUxGuvvcb48eNJTk6mT58+nD59GnASd8eOHbnyyisZNWqU78drIcUoInTr1o0VK1aE9qYacw6pvvehvzkB/v2/lVvnT5Khb8ndHhC9w+cGSkhI4J///CezZs1i+vTpzJ07t9jykydPltiWSZMmkZaWxrJly1izZg3Dhg3zx71r1y7Wrl1Lbm4unTp14tVXX+WRRx5h0KBBvPHGGwwcOJCxY8f6hwq48847WbFiBf369Qs5Rt9wt7/4xS9KbJ8x5yI7Qg8QjcPnBnPTTTcBZw8l61NaWzZu3Midd94JwM9//nMOHz7M0aNHAWcMmdq1a5OcnMyZM2fo06cPAMnJyf7trF27lquvvprk5GTWrFnDtm3byhWjDXdrTHDV9wi9jCPpcDlXhs/1DUXrHUrWO0Tu2LFjS9yOr4vEy1fWO9ytd8ha33C3J0+e5Ne//jXZ2dk0a9aMyZMnlxhnsBjBhrs1piR2hB4gGofPDZV3iNzS2tKlSxcWLVoEOHe/JCQkcP7554e0DV/yTkhI4Pjx48WG7A2VDXdrTHCW0ANE6/C55VVaWyZPnkx2djYpKSlMmDCBhQsXhlzvBRdcwK9+9SuSk5MZOHAgHTt2LHdsa9eu5YYbbij3esZEOwl2+lwVMjIy1PcAX59PPvmENm3aRCQecO5yufHGG6NmgK5BgwbxyCOPRNVoiwUFBQwZMoR//OMfkQ6lWor0d8iULmnCG/7Xe6ZV7KBERLaqakawZXaEHsV8w+dGk71794b0CDxjzkXV96JoBNjwudVfRbpojDlXhHSELiJ9RGS7iOwUkbN+7igi40Qkx/33sYicEZELKz9cY4wxJSkzoYtIDPA00BdoC9wuIm29ZVT1UVVNVdVU4H7gHVX9KgzxGmOMKUEoR+hXATtV9TNVPQUsBgaUUv52IPj9esYYY8ImlITeBNjnmc53551FROoDfYBXS1g+SkSyRST74MGD5Y3VGGNMKUJJ6MF+LljSvY79gE0ldbeo6hxVzVDVjNJ+ll6TeAeU8qqK4WaNMcYrlISeDzTzTDcFShpI4zasu8UYYyIilNsWtwAtRaQF8AVO0h4SWEhE4oGuQPARqWqQP/3pTyxatIhmzZqRkJBAhw4d6NmzJ6NHj+bEiRNcfvnlzJs3j0aNGhVb76233uK3v/0tCQkJpKenRyh6Y8y5qsyErqpFIjIWWAXEAPNUdZuIjHaXz3aLDgJWq+o3lRHYwx88TN5XwcfprqjWF7bmvqvuK7VMdnY2r7766lnD0w4bNoy//OUvdO3alYkTJ/LQQw8xY8YM/3qlDTdrjDFVIaT70FV1papeoaqXq+pUd95sTzJHVReo6m3hCrSqbNy4kQEDBlCvXj0aNmxIv379+Oabbzhy5Ahdu3YFYPjw4WeNkxLq0LnGGBMu1faXomUdSYfLjxnbprKGzjXGmIqwsVwCZGVl8frrr3Py5EmOHz/OG2+8QVxcHI0aNWLDhg0AvPDCC/6jdZ9Qh841xphwqbZH6JHSsWNH+vfvT/v27WnevDkZGRnEx8ezcOFC/0XRyy67jPnz5xdbzzvcbEJCAllZWVE1Lowxpvqz4XODOH78OA0aNODEiRN06dKFOXPm2F0rpkaoLt8hE1y4h8+1I/QgRo0aRW5uLidPnmT48OGWzI0xNYIl9CD++te/RjoEY4wpN7soaowxUcISujHGRAlL6MYYEyUsoRtjTJSwhB5G3iF0ly9fzrRp00ose+TIEWbNmlXubUyePJnp06cHXTZjxgyef/75ctcJkJOTw8qVK8u9Xl5eHqmpqaSlpfl/ZBXMggULGDt2bIViC9Vdd91Fbm5uhdbNzs7mnnvuKbPczJkzadOmDXfccUdIbVq3bh3vvvtuueNJSkri0KFDnDp1ii5dulBUVFTuOkz0s4ReAWfOnCn3Ov3792fChLMex+pX0YRekqKiIubNm8eQIWcNjBmSiib0ZcuWMWDAAD788EMuv/zyCm27ssydO5e2bduWXTCIjIwMZs6cWWa5WbNmsXLlShYtWhRSvRVN6D6xsbH06NGDJUuWVLgOE70soXvs2bOH1q1bM3z4cFJSUrjllls4ceIE4BwhTZkyhaysLF555RVWr15Np06dSE9P59Zbb+X48eOAM4Ru69atycrK4rXXXvPX7T16KygoYNCgQbRv35727dvz7rvvMmHCBHbt2kVqairjxo0D4NFHH6Vjx46kpKQwadIkf11Tp06lVatW9OzZk+3btwdty5o1a0hPT6dWLefO1JycHDIzM0lJSWHQoEF8/fXXAHTr1g3fD7wOHTpEUlISp06dYuLEiSxZsoTU1NSgySNYfStXrmTGjBnMnTuX7t27n7XO/PnzueKKK+jatSubNm3yz//888/p0aMHKSkp9OjRg7179wLOw0PGjBlD9+7dueyyy3jnnXcYOXIkbdq0YcSIEf71x4wZQ0ZGBu3atSu2n7xta9CgAQ888ADt27cnMzOTgoKCoPvNx3t2NXnyZEaOHEm3bt247LLL/Il+9OjRfPbZZ/Tv358nnnii2Pqvv/46V199NWlpafTs2ZOCggL27NnD7NmzeeKJJ0hNTWXDhg0cPHiQm2++mY4dO9KxY0f/fjl8+DC9evUiLS2Nu+++u9gYQwMHDgz5D4g5t1Tb+9D//d//zXefVO7wuXXatOYnf/xjqWW2b9/Oc889R+fOnRk5ciSzZs3i3nvvBZyf92/cuJFDhw5x00038fbbbxMXF8fDDz/M448/zvjx40MaQveee+6ha9euLF26lDNnznD8+HGmTZvGxx9/TE5ODgCrV6/m008/5YMPPkBV6d+/P+vXrycuLo7FixefNbxvoE2bNhWbX9bwv16xsbFMmTKF7OxsnnrqqaBlSqpv9OjRNGjQwL/PfPbv38+kSZPYunUr8fHxdO/enbS0NADGjh3LsGHDGD58OPPmzeOee+5h2bJlAHz99desWbOG5cuX069fPzZt2sTcuXPp2LEjOTk5pKamMnXqVC688ELOnDlDjx49+Oijj0hJSSm2/W+++YbMzEymTp3K+PHjefbZZ3nwwQeDti2YvLw81q5dS2FhIa1atWLMmDHMnj2bt956i7Vr15KQkMCCBQv85bOysnjvvfcQEebOncsjjzzCY489dtb+GTJkCL/73e/Iyspi79699O7dm08++YSHHnqIrKwsJk6cyBtvvMGcOXP8dV955ZVs2bIl5NjNuaPaJvRIadasGZ07dwZg6NChzJw50//l8yXo9957j9zcXH+5U6dO0alTp2JD6PrW934RfdasWePv246JiSE+Pt5/xOyzevVqVq9e7U96x48f59NPP6WwsJBBgwZRv359wOnKCWb//v3+n4AfPXr0rOF/b7311gruoYrV9/7779OtWzd8jx4cPHgwO3bsAGDz5s3+s5k777yT8ePH+9fr168fIkJycjIXX3wxycnJALRr1449e/aQmprKyy+/zJw5cygqKmL//v3k5uaeldBjY2P9R9wdOnTg73//e7nafMMNN1CnTh3q1KnDRRddREFBAU2bNi2xfH5+PoMHD2b//v2cOnWKFi1aBC339ttvF+vnP3bsGIWFhaxfv96/T2644YZiD1OJiYkhNjaWwsJCGjZsWK52mOgWUkIXkT7AkzgPuJirqmdd3RORbsAMoDZwSFW7BpYpj7KOpMMlcAhc73RcXBzgDLF73XXXnTWiYk5OTqUNoauq3H///dx9993F5s+YMSOkbdSrV4+TJ0+WWa5WrVp8//33AKWW7927NwUFBWRkZPDYY4+VWe+ZM2f8Zwj9+/cnPT095H3jLVenTh0AzjvvPP9r33RRURG7d+9m+vTpbNmyhUaNGjFixIig7ahdu7a/3piYmHJfVPRuO5T1f/Ob3/D73/+e/v37s27dOiZPnhy03Pfff8/mzZupV6/eWctK21/fffcddevWDS14c84osw9dRGKAp4G+QFvgdhFpG1DmAmAW0F9V2wEVP/yLsL1797J582bAGQI3KyvrrDKZmZls2rSJnTt3AnDixAl27NgR8hC6PXr04JlnngGcxHfs2DEaNmxIYWGhv0zv3r2ZN2+ev2/+iy++4MCBA3Tp0oWlS5fy7bffUlhYyOuvvx50G23atPHHFx8fX+Lwv0lJSWzduhWg2MOuA+NZtWoVOTk5zJ07t9T6fGJiYsjJySEnJ4cpU6Zw9dVXs27dOg4fPszp06d55ZVX/GWvueYaFi9eDMCiRYuC7vOSHDt2jLi4OOLj4ykoKODNN98MeV2ApUuXcv/995drnVAcPXqUJk2aALBw4UL//MD92qtXr2LdWr4uty5duvj7yd98881iZ3CHDx8mMTGR2rVrV3rcpmYL5aLoVcBOVf1MVU8Bi4EBAWWGAK+p6l4AVT1QuWFWnTZt2rBw4UJSUlL46quvGDNmzFllEhMTWbBgAbfffjspKSlkZmaSl5dXbAjdrKwsmjdvHnQbTz75JGvXriU5OZkOHTqwbds2GjduTOfOnbnyyisZN24cvXr1YsiQIXTq1Ink5GRuueUWCgsLSU9PZ/DgwaSmpnLzzTdz7bXXBt1G3759iz1VaeHChYwbN46UlBRycnKYOHEiAPfeey/PPPMM11xzDYcOHfKX7969O7m5uSVeFC2pvpJccsklTJ48mU6dOtGzZ89iA57NnDmT+fPnk5KSwgsvvMCTTz5Zal1e7du3Jy0tjXbt2jFy5Eh/N1iodu3axfnnn1+udUIxefJkbr31Vq699loSEhL88/v168fSpUv9F0VnzpxJdnY2KSkptG3bltmznYeATZo0ifXr15Oens7q1au59NJL/XWsXbuW66+/vtJjNlFAVUv9B9yC083im74TeCqgzAyco/h1wFZgWAl1jQKygexLL71UA+Xm5p41ryrt3r1b27VrF9EYKtPAgQN1x44dkQ6jWrvjjjv0wIEDkQ6jXAYNGqR5eXlBl0X6O2RK1/y+Ff5/FQVkawn5OpQj9GAdeYGDqNcCOgA3AL2B/yMiVwT54zFHVTNUNcN3ccyEz7Rp09i/f3+kw6jWXnzxRWrSZ/HUqVMMHDiQVq1aRToUUw2FclE0H2jmmW4KfBmkzCFV/Qb4RkTWA+2BHZUSZRVJSkqKqqcMtWrVyr74USY2NpZhw4ZFOgxTTYVyhL4FaCkiLUQkFrgNWB5Q5v8B14pILRGpD1wNfFKRgDRCT1Aypqaz744p8whdVYtEZCywCue2xXmquk1ERrvLZ6vqJyLyFvAR8D1On3u5D3Xr1q3L4cOHady4caXd/mfMuUBVOXz4sN3KeI4L6T50VV0JrAyYNztg+lHg0R8TTNOmTcnPz+fgwYM/phpjzkl169Yt9cdOJvpVq1+K1q5du8Rf1BljjCmdDc5ljDFRwhK6McZECUvoxhgTJSyhG2NMlLCEbowxUcISujHGRAlL6MYYEyUsoRtjTJSwhG6MMVHCEroxxkQJS+jGGBMlLKEbY0yUsIRujDFRwhK6McZEiZASuoj0EZHtIrJTRCYEWd5NRI6KSI77r/RHwBtjjKl0ZY6HLiIxwNPAdTjPDt0iIstVNTeg6AZVvTEMMRpjjAlBKEfoVwE7VfUzVT0FLAYGhDcsY4yJPrGc5ne1XqEOp8JSfygJvQmwzzOd784L1ElE/iUib4pIu2AVicgoEckWkWx7zJwx5lxzZ8zf+a9aSxkd83pY6g8loQd7WnPg48X/CTRX1fbAX4BlwSpS1TmqmqGqGYmJieUK1Bhjaro6nHb+l9NhqT+UhJ4PNPNMNwW+9BZQ1WOqetx9vRKoLSIJlRalMcaYMoWS0LcALUWkhYjEArcBy70FROQnIiLu66vceg9XdrDGGGNKVuZdLqpaJCJjgVVADDBPVbeJyGh3+WzgFmCMiBQB3wK3qWpgt4wxxpgwKjOhg78bZWXAvNme108BT1VuaMYYY8rDfilqjDFRwhK6McZECUvoxhgTJSyhG2NMlLCEbowxUcISujHGRAlL6MYYU2XC+/McS+jGGFPFwpXWLaEbY0yUsIRujDFRwhK6McZECUvoxhgTJSyhG2NMlLCEbowxUcISujHGVLFgz/WsDCEldBHpIyLbRWSniEwopVxHETkjIrdUXojGGBMtwpXKHWUmdBGJAZ4G+gJtgdtFpG0J5R7GebKRMcaYKhbKEfpVwE5V/UxVTwGLgQFByv0GeBU4UInxGWOMCVEoCb0JsM8zne/O8xORJsAgYDalEJFRIpItItkHDx4sb6zGGFPDRX4sl2CdPoFRzQDuU9UzpVWkqnNUNUNVMxITE0MM0Rhjoku40nooD4nOB5p5ppsCXwaUyQAWiwhAAnC9iBSp6rLKCNIYY0zZQknoW4CWItIC+AK4DRjiLaCqLXyvRWQBsMKSuTHGVK0yE7qqFonIWJy7V2KAeaq6TURGu8tL7Tc3xhhTNUI5QkdVVwIrA+YFTeSqOuLHh2WMMaa87JeixhgTJSyhG2NMlLCEbowxUcISujHGRAlL6MYYEyUsoRtjTJSwhG6MMVHCEroxxlSR8I6GbgndGGOqnIYptVtCN8aYKGEJ3RhjqpiEaQBdS+jGGFNFwvt4C0voxhgTNSyhG2NMlLCEbowxUSKkhC4ifURku4jsFJEJQZYPEJGPRCTHfQh0VuWHaowxpjRlPuBCRGKAp4HrcJ4vukVElqtqrqfYP4DlqqoikgK8DLQOR8DGGGOCC+UI/Spgp6p+pqqngMXAAG8BVT2uqr4LuHGE/2KuMcaYAKEk9CbAPs90vjuvGBEZJCJ5wBvAyGAVicgot0sm++DBgxWJ1xhjTAlCSejBfqN61hG4qi5V1dbAQOBPwSpS1TmqmqGqGYmJieUK1BhjarrqMJZLPtDMM90U+LKkwqq6HrhcRBJ+ZGzGGBOVIjmWyxagpYi0EJFY4DZgubeAiPxMRMR9nQ7EAocrO1hjjDElK/MuF1UtEpGxwCogBpinqttEZLS7fDZwMzBMRE4D3wKDPRdJjTHGVIEyEzqAqq4EVgbMm+15/TDwcOWGZowxpjzsl6LGGBMlLKEbY0yUsIRujDFRwhK6McZECUvoxhgTJSyhG2NMlLCEbowxVcyeKWqMMaZUltCNMaaKRXIsF2OMMTWAJXRjjIkSltCNMSZKWEI3xpgoYQndGGOihCV0Y4yJEiEldBHpIyLbRWSniEwIsvwOEfnI/feuiLSv/FCNMcaUpsyELiIxwNNAX6AtcLuItA0othvoqqopOA+InlPZgRpjjCldKEfoVwE7VfUzVT0FLAYGeAuo6ruq+rU7+R7Og6SNMcYEOCGCRvCn/02AfZ7pfHdeSf4DePPHBGWMMdGoS+33uDqpGV9euD0s9YfyTNFgv1EN+udFRLrjJPSsEpaPAkYBXHrppSGGaIwx0eGC2P3AJeQ2OB6W+kM5Qs8HmnmmmwJfBhYSkRRgLjBAVQ8Hq0hV56hqhqpmJCYmViReY4wxJQgloW8BWopICxGJBW4DlnsLiMilwGvAnaq6o/LDNMYYU5Yyu1xUtUhExgKrgBhgnqpuE5HR7vLZwESgMTBLRACKVDUjfGEbY0zNVYszYao3BKq6ElgZMG+25/VdwF2VG5oxxkSX8Nzb8gP7pagxxkQJS+jGGFNFwvNYix9YQjfGmCqytn49AD6LrR2W+i2hG2NMFflH/foAFIk9gs4YY0wpLKEbY0yUsIRujDFRwhK6McZECUvoxhgTJSyhG2NMlLCEbowxUcISujHGVJGiMP9U1BK6McZUkXrfh3d4LkvoxhgTJSyhG2NMlLCEbowxVUSrQx+6iPQRke0islNEJgRZ3lpENovIdyJyb+WHaYwxpixlPrFIRGKAp4HrcB4YvUVElqtqrqfYV8A9wMBwBGmMMaZsoRyhXwXsVNXPVPUUsBgY4C2gqgdUdQtwOgwxGmOMCUEoCb0JsM8zne/OKzcRGSUi2SKSffDgwYpUYYwxpgShJPRg3fgVuplSVeeoaoaqZiQmJlakCmOMMSUIJaHnA808002BL8MTjjHGmIoKJaFvAVqKSAsRiQVuA5aHNyxjjDHlVeZdLqpaJCJjgVVADDBPVbeJyGh3+WwR+QmQDZwPfC8ivwXaquqx8IVujDE1S3h/+B9CQgdQ1ZXAyoB5sz2v/43TFWOMMSZC7JeixhgTJSyhG2NMlLCEbowxUcISujHGVJFwXxS1hG6MMVVEg/5Os/JYQjfGmChhCd0YY6KEJXRjjIkSltCNMaaK2EVRY4wxIbGEbowxUcISujHGVBHrcjHGmChhCd0YY0xILKEbY0yUCCmhi0gfEdkuIjtFZEKQ5SIiM93lH4lIeuWHaowxNZuG95f/ZSd0EYkBngb6Am2B20WkbUCxvkBL998o4JlKjtMYY0wZQjlCvwrYqaqfqeopYDEwIKDMAOB5dbwHXCAil1RyrMYYU6N9Ghsb1vpDSehNgH2e6Xx3XnnLICKjRCRbRLIPHjxY3liNMaZGm9C4JwBP6U/CUn8ozxQN1usTePdNKGVQ1TnAHICMjIxw38FjjDHVyh03PsEdYaw/lCP0fKCZZ7op8GUFyhhjjAmjUBL6FqCliLQQkVjgNmB5QJnlwDD3bpdM4Kiq7q/kWI0xxpSizC4XVS0SkbHAKiAGmKeq20RktLt8NrASuB7YCZwAfhm+kI0xxgQTSh86qroSJ2l75832vFbgPys3NGOMMeVhvxQ1xpgoYQndGGOihCV0Y4yJEpbQjTEmSohzPTMCGxY5CHxewdUTgEOVGE4kWBsir6bHD9aG6qCq42+uqonBFkQsof8YIpKtqhmRjuPHsDZEXk2PH6wN1UF1it+6XIwxJkpYQjfGmChRUxP6nEgHUAmsDZFX0+MHa0N1UG3ir5F96MYYY85WU4/QjTHGBLCEbowxUaLGJfSyHlgdKSLSTETWisgnIrJNRP7LnX+hiPxdRD51/2/kWed+tx3bRaS3Z34HEflfd9lMEQnzo2WLtSNGRD4UkRU1NP4LRORvIpLnvhedamAbfud+hj4WkZdEpG51b4OIzBORAyLysWdepcUsInVEZIk7/30RSaqiNjzqfpY+EpGlInJBdW4Dqlpj/uEM37sLuAyIBf4FtI10XG5slwDp7uuGwA6ch2o/Akxw508AHnZft3XjrwO0cNsV4y77AOiE8ySoN4G+VdiO3wN/BVa40zUt/oXAXe7rWOCCmtQGnEc37gbqudMvAyOqexuALkA68LFnXqXFDPwamO2+vg1YUkVt6AXUcl8/XO3bUBUf0krc4Z2AVZ7p+4H7Ix1XCbH+P+A6YDtwiTvvEmB7sNhxxpvv5JbJ88y/HfifKoq5KfAP4Of8kNBrUvzn4yRDCZhfk9rgez7vhTjDW69wk0q1bwOQFJAMKy1mXxn3dS2cX2ZKuNsQsGwQsKg6t6GmdbmE9DDqSHNPpdKA94GL1X16k/v/RW6xktrSxH0dOL8qzADGA9975tWk+C8DDgLz3W6juSISRw1qg6p+AUwH9gL7cZ7+tZoa1AaPyozZv46qFgFHgcZhizy4kThH3MXicVWLNtS0hB7Sw6gjSUQaAK8Cv1XVY6UVDTJPS5kfViJyI3BAVbeGukqQeRGL31UL55T5GVVNA77BOdUvSbVrg9vPPADnNP6nQJyIDC1tlSDzIv0+lKUiMUe0PSLyAFAELCojnoi2oaYl9Gr9MGoRqY2TzBep6mvu7AIRucRdfglwwJ1fUlvy3deB88OtM9BfRPYAi4Gfi8iL1Jz4fTHlq+r77vTfcBJ8TWpDT2C3qh5U1dPAa8A11Kw2+FRmzP51RKQWEA98FbbIPURkOHAjcIe6/SVU0zbUtIQeygOrI8K9kv0c8ImqPu5ZtBwY7r4ejtO37pt/m3vluwXQEvjAPTUtFJFMt85hnnXCRlXvV9WmqpqEs1/XqOrQmhK/24Z/A/tEpJU7qweQW5PagNPVkiki9d1t9wA+qWFt8KnMmL113YLz+ayKM9c+wH1Af1U94VlUPdsQzosk4fiH8zDqHThXlR+IdDyeuLJwTp8+AnLcf9fj9JH9A/jU/f9CzzoPuO3YjucOBCAD+Nhd9hRhuPhTRlu68cNF0RoVP5AKZLvvwzKgUQ1sw0NAnrv9F3DupKjWbQBewunzP41zJPoflRkzUBd4BedB9B8Al1VRG3bi9Hv7vtOzq3Mb7Kf/xhgTJWpal4sxxpgSWEI3xpgoYQndGGOihCV0Y4yJEpbQjTEmSlhCN8aYKGEJ3RhjosT/B35egeCjzljjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_predicted_df = convert(data_predicted_df)\n",
    "plot_data_predicted_ff = convert(data_predicted_ff)\n",
    "plot_data_predicted_df_inflated = convert(data_predicted_df_inflated)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_df = [x[0] for x in plot_data_predicted_df]\n",
    "y_df = [x[1] for x in plot_data_predicted_df]\n",
    "x_ff = [x[0] for x in plot_data_predicted_ff]\n",
    "y_ff = [x[1] for x in plot_data_predicted_ff]\n",
    "x_df_inflated = [x[0] for x in plot_data_predicted_df_inflated]\n",
    "y_df_inflated = [x[1] for x in plot_data_predicted_df_inflated]\n",
    "plt.plot(x_df, y_df, label=\"predicted (out-of-domain)\")\n",
    "plt.plot(x_ff, y_ff, label=\"predicted (in-domain)\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\")\n",
    "plt.plot(x_df_inflated, y_df_inflated, label=\"predicted (out-of-domain, inflated)\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting facemask with social distancing data\")\n",
    "plt.savefig(\"distancing-to-facemask.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical analysis of predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_predicted_df_dict = { x[0]:x[1] for x in plot_data_predicted_df }\n",
    "plot_data_predicted_ff_dict = { x[0]:x[1] for x in plot_data_predicted_ff }\n",
    "plot_data_predicted_df_inflated_dict = { x[0]:x[1] for x in plot_data_predicted_df_inflated }\n",
    "plot_data_gold_dict = { x[0]:x[1] for x in plot_data_gold }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson r correlation gold vs in-domain: 0.05\n",
      "Pearson r correlation gold vs out-of-domain: -0.11\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Inputs must not be empty.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-133-400f5530c8f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pearson r correlation gold vs out-of-domain:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_gold_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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      5\u001b[0m \u001b[0mvalues_df_inflated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues_gold_df_inflated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_common_key_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data_predicted_df_inflated_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_data_gold_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pearson r correlation gold vs out-of-domain (inflated):\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_gold_df_inflated\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_df_inflated\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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[0m",
      "\u001b[0;32m~/anaconda3/envs/python37/lib/python3.7/site-packages/scipy/stats/_stats_mstats_common.py\u001b[0m in \u001b[0;36mlinregress\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Inputs must not be empty.\"\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    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Inputs must not be empty."
     ]
    }
   ],
   "source": [
    "values_ff, values_gold_ff = get_common_key_values(plot_data_predicted_ff_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs in-domain:\", round(scipy.stats.linregress(np.array(values_gold_ff), np.array(values_ff)).rvalue, 2))\n",
    "values_df, values_gold_df = get_common_key_values(plot_data_predicted_df_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain:\", round(scipy.stats.linregress(np.array(values_gold_df), np.array(values_df)).rvalue, 2))\n",
    "values_df_inflated, values_gold_df_inflated = get_common_key_values(plot_data_predicted_df_inflated_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (inflated):\", round(scipy.stats.linregress(np.array(values_gold_df_inflated), np.array(values_df_inflated)).rvalue, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absolute difference gold vs in-domain: 0.07\n",
      "absolute difference gold vs out-of-domain: 0.37\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-4eecf575709e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"absolute difference gold vs in-domain:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_difference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_gold_ff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues_ff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"absolute difference gold vs out-of-domain:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_difference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_gold_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\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----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"absolute difference gold vs out-of-domain (inflated):\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_difference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues_gold_df_inflated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues_df_inflated\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\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[0m",
      "\u001b[0;32m<ipython-input-29-5296738c6f95>\u001b[0m in \u001b[0;36mabsolute_difference\u001b[0;34m(list1, list2)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist1\u001b[0m\u001b[0;34m)\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      4\u001b[0m         \u001b[0mabsolute_difference\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlist2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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----> 5\u001b[0;31m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabsolute_difference\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist1\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[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "print(\"absolute difference gold vs in-domain:\", round(absolute_difference(values_gold_ff, values_ff), 2))\n",
    "print(\"absolute difference gold vs out-of-domain:\", round(absolute_difference(values_gold_df, values_df),2)) \n",
    "print(\"absolute difference gold vs out-of-domain (inflated):\", round(absolute_difference(values_gold_df_inflated, values_df_inflated),2)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting testing data with distance data (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDT, predictionLabelsValidationDT, predictionCountsTestDT, predictionLabelsTestDT = \\\n",
    "        runFasttext(fasttext_data_list_distance, fasttext_data_list_testing, squealFlag=False, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDT, predictionLabelsValidationDT, predictionCountsTestDT, predictionLabelsTestDT = \\\n",
    "        runFasttext(fasttext_data_list_distance, \n",
    "                    fasttext_data_list_testing, \n",
    "                    squealFlag=False, \n",
    "                    dim=300, \n",
    "                    pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    store_fasttext_data(predictionLabelsTestDT, \"predictionLabelsTestDT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDT = read_fasttext_data(\"predictionLabelsTestDT.pickle\")\n",
    "predictionLabelsTestTT = read_fasttext_data(\"predictionLabelsTestTT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_factor = 3\n",
    "data_predicted_dt = make_data_predicted(predictionLabelsTestDT, tweet_ids_testing, time_factor=time_factor)\n",
    "data_predicted_tt = make_data_predicted(predictionLabelsTestTT, tweet_ids_testing, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold = []\n",
    "for tweet in fasttext_data_list_testing:\n",
    "    gold.append(tweet.split()[0])\n",
    "\n",
    "data_gold = {}\n",
    "for i in range(0, len(tweet_ids_testing)):\n",
    "    summary = str(tweet_ids_testing[i])[0:time_factor]\n",
    "    if summary not in data_gold:\n",
    "        data_gold[summary] = []\n",
    "    data_gold[summary].append(gold[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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": [
    "plot_data_predicted_dt = convert(data_predicted_dt)\n",
    "plot_data_predicted_tt = convert(data_predicted_tt)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_df = [x[0] for x in plot_data_predicted_dt]\n",
    "y_df = [x[1] for x in plot_data_predicted_dt]\n",
    "x_ff = [x[0] for x in plot_data_predicted_tt]\n",
    "y_ff = [x[1] for x in plot_data_predicted_tt]\n",
    "plt.plot(x_df, y_df, label=\"predicted (out-of-domain)\")\n",
    "plt.plot(x_ff, y_ff, label=\"predicted (in-domain)\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting testing with social distancing data\")\n",
    "plt.savefig(\"distancing-to-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Use extra data in training (ALL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runFasttextPlus(fasttextData1,\n",
    "                    fasttextData2,\n",
    "                    dim=DIM,\n",
    "                    epoch=EPOCH,\n",
    "                    lr=LR,\n",
    "                    n=N,\n",
    "                    squealFlag=True,\n",
    "                    maxTrain=None,\n",
    "                    pretrainedVectors=None,\n",
    "                    extraFasttextData=None):\n",
    "    print(f\"data size 1={len(fasttextData1)}, data size 2={len(fasttextData2)}, dim={dim}, epoch={epoch}, lr={lr}, pretrainedVectors={pretrainedVectors}\")\n",
    "    predictionCountsTest = []\n",
    "    predictionLabelsTest = []\n",
    "    predictionCountsValidation = []\n",
    "    predictionLabelsValidation = []\n",
    "    for fold in range(0,n):\n",
    "        if squealFlag:\n",
    "            clear_output(wait=True)\n",
    "            print(\"starting fold\",fold)\n",
    "        testStart1 = round(fold*len(fasttextData1)/n)\n",
    "        testEnd1 = round((fold+1)*len(fasttextData1)/n)\n",
    "        testStart2 = round(fold*len(fasttextData2)/n)\n",
    "        testEnd2 = round((fold+1)*len(fasttextData2)/n)\n",
    "        if fold < n-1: nextFold = fold+1\n",
    "        else: nextFold = 0\n",
    "        validationStart1 = round(nextFold*len(fasttextData1)/n)\n",
    "        validationEnd1 = round((nextFold+1)*len(fasttextData1)/n)\n",
    "        validationStart2 = round(nextFold*len(fasttextData2)/n)\n",
    "        validationEnd2 = round((nextFold+1)*len(fasttextData2)/n)\n",
    "        trainFile = open(TRAIN,\"w\")\n",
    "        testFile = open(TEST,\"w\")\n",
    "        validationFile = open(VALIDATION,\"w\")\n",
    "        trainData = []\n",
    "        validationData = []\n",
    "        testData = []\n",
    "        for i in range(0,len(fasttextData1)):\n",
    "            data = fasttextData1[i]\n",
    "            if i >= testStart1 and i < testEnd1: \n",
    "                pass\n",
    "            elif i >= validationStart1 and i < validationEnd1: \n",
    "                pass\n",
    "            else: \n",
    "                print(data,file=trainFile)\n",
    "                trainData.append(data)\n",
    "        for i in range(0,len(fasttextData2)):\n",
    "            data = fasttextData2[i]\n",
    "            if i >= testStart2 and i < testEnd2: \n",
    "                print(data,file=testFile)\n",
    "                testData.append(data)\n",
    "            elif i >= validationStart2 and i < validationEnd2: \n",
    "                print(data,file=validationFile)\n",
    "                validationData.append(data)\n",
    "        if extraFasttextData != None:\n",
    "            for i in range(0,len(extraFasttextData)):\n",
    "                data = extraFasttextData[i]\n",
    "                print(data,file=trainFile)\n",
    "                trainData.append(data)\n",
    "        testFile.close()\n",
    "        trainFile.close()\n",
    "        validationFile.close()\n",
    "        if not maxTrain == None and maxTrain < len(trainData):\n",
    "            trainData = ranSelect(trainData,maxTrain)\n",
    "            trainFile = open(TRAIN,\"w\")\n",
    "            for i in range(0,len(trainData)):\n",
    "                print(trainData[i],file=trainFile)\n",
    "            trainFile.close()\n",
    "        if pretrainedVectors == None:\n",
    "            model = fasttext.train_supervised(TRAIN, dim=dim, epoch=epoch, lr=lr)\n",
    "        else:\n",
    "            model = fasttext.train_supervised(TRAIN, dim=dim, epoch=epoch, lr=lr, pretrainedVectors=pretrainedVectors)\n",
    "        predictionCountsValidation.append([*model.test(VALIDATION)])\n",
    "        predictionLabelsValidation.append(model.predict(validationData, k=3))\n",
    "        predictionCountsTest.append([*model.test(TEST)])\n",
    "        predictionLabelsTest.append(model.predict(testData, k=3))\n",
    "        print(fold, end=\" \")\n",
    "        evaluate([[*model.test(TEST)]], [model.predict(testData)], fasttextData2[testStart2:testEnd2])\n",
    "        os.unlink(TRAIN)\n",
    "        os.unlink(TEST)\n",
    "        os.unlink(VALIDATION)\n",
    "    print(\"\", end=\" \")\n",
    "    evaluate(predictionCountsTest, predictionLabelsTest, fasttextData2, printMatrix=True)\n",
    "    if squealFlag:\n",
    "        clear_output(wait=True)\n",
    "        print(\"finished\")\n",
    "    return(predictionCountsValidation,predictionLabelsValidation,predictionCountsTest,predictionLabelsTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 118; precision: 0.449; recall: 0.449; fraction: 3.044\n",
      "1 cases: 118; precision: 0.424; recall: 0.424; fraction: 1.130\n",
      "2 cases: 118; precision: 0.525; recall: 0.525; fraction: 2.683\n",
      "3 cases: 118; precision: 0.517; recall: 0.517; fraction: 1.083\n",
      "4 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.897\n",
      "5 cases: 119; precision: 0.538; recall: 0.538; fraction: 0.316\n",
      "6 cases: 118; precision: 0.602; recall: 0.602; fraction: 0.500\n",
      "7 cases: 118; precision: 0.551; recall: 0.551; fraction: 0.668\n",
      "8 cases: 118; precision: 0.602; recall: 0.602; fraction: 0.857\n",
      "9 cases: 118; precision: 0.627; recall: 0.627; fraction: 0.889\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  376                77                178\n",
      "__label__REJECTS                      82                93                 35\n",
      "__label__SUPPORTS                    141                29                170\n",
      "cases: 1181; precision: 0.541; recall: 0.541; fraction: 0.841\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationETT, predictionLabelsValidationETT, predictionCountsTestETT, predictionLabelsTestETT = \\\n",
    "    runFasttextPlus(fasttext_data_list_testing, \n",
    "                    fasttext_data_list_testing, \n",
    "                    squealFlag=False, \n",
    "                    pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                    extraFasttextData=fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestETT, f\"predictionLabelsTestETT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1011, data size 2=1011, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 101; precision: 0.574; recall: 0.574; fraction: 6.667\n",
      "1 cases: 101; precision: 0.525; recall: 0.525; fraction: 0.879\n",
      "2 cases: 101; precision: 0.614; recall: 0.614; fraction: 0.649\n",
      "3 cases: 101; precision: 0.465; recall: 0.465; fraction: 2.122\n",
      "4 cases: 102; precision: 0.510; recall: 0.510; fraction: 3.028\n",
      "5 cases: 101; precision: 0.564; recall: 0.564; fraction: 0.875\n",
      "6 cases: 101; precision: 0.624; recall: 0.624; fraction: 0.383\n",
      "7 cases: 101; precision: 0.485; recall: 0.485; fraction: 0.393\n",
      "8 cases: 101; precision: 0.545; recall: 0.545; fraction: 0.421\n",
      "9 cases: 101; precision: 0.525; recall: 0.525; fraction: 0.263\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  348                78                132\n",
      "__label__REJECTS                      90               105                 44\n",
      "__label__SUPPORTS                     76                42                 96\n",
      "cases: 1011; precision: 0.543; recall: 0.543; fraction: 0.741\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationEFT, predictionLabelsValidationEFT, predictionCountsTestEFT, predictionLabelsTestEFT = \\\n",
    "    runFasttextPlus(fasttext_data_list_facemask, \n",
    "                    fasttext_data_list_facemask, \n",
    "                    squealFlag=False, \n",
    "                    pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                    extraFasttextData=fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestEFT, f\"predictionLabelsTestEFT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1007, data size 2=1007, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 101; precision: 0.624; recall: 0.624; fraction: 1.138\n",
      "1 cases: 100; precision: 0.690; recall: 0.690; fraction: 0.406\n",
      "2 cases: 101; precision: 0.604; recall: 0.604; fraction: 0.296\n",
      "3 cases: 101; precision: 0.594; recall: 0.594; fraction: 0.181\n",
      "4 cases: 101; precision: 0.406; recall: 0.406; fraction: 0.416\n",
      "5 cases: 100; precision: 0.580; recall: 0.580; fraction: 0.165\n",
      "6 cases: 101; precision: 0.624; recall: 0.624; fraction: 0.478\n",
      "7 cases: 101; precision: 0.574; recall: 0.574; fraction: 0.671\n",
      "8 cases: 100; precision: 0.540; recall: 0.540; fraction: 0.903\n",
      "9 cases: 101; precision: 0.485; recall: 0.485; fraction: 1.300\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  428               111                 99\n",
      "__label__REJECTS                     121               114                 30\n",
      "__label__SUPPORTS                     55                15                 34\n",
      "cases: 1007; precision: 0.572; recall: 0.572; fraction: 0.578\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationEVT, predictionLabelsValidationEVT, predictionCountsTestEVT, predictionLabelsTestEVT = \\\n",
    "    runFasttextPlus(fasttext_data_list_vaccination, \n",
    "                    fasttext_data_list_vaccination, \n",
    "                    squealFlag=False, \n",
    "                    pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                    extraFasttextData=fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestEVT, f\"predictionLabelsTestEVT-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Relevance experiments\n",
    "\n",
    "For the Relevance Only (5. RLVONLY) experiment, we need relevance preprocessing. This is a easier (binary) task which is handled here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_binary_fasttext_data(fasttext_data_list_in):\n",
    "    fasttext_data_list_out = []\n",
    "    for text in fasttext_data_list_in:\n",
    "        text = re.sub(r'__label__SUPPORTS ', r'__label__RELEVANT ', text)\n",
    "        text = re.sub(r'__label__REJECTS ', r'__label__RELEVANT ', text)\n",
    "        fasttext_data_list_out.append(text)\n",
    "    return fasttext_data_list_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasttext_data_list_facemask_binary = make_binary_fasttext_data(fasttext_data_list_facemask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1011, data size 2=1011, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020.vec\n",
      "0 cases: 101; precision: 0.663; recall: 0.663; fraction: 0.000\n",
      "1 cases: 101; precision: 0.713; recall: 0.713; fraction: 0.000\n",
      "2 cases: 101; precision: 0.703; recall: 0.703; fraction: 0.000\n",
      "3 cases: 101; precision: 0.594; recall: 0.594; fraction: 0.000\n",
      "4 cases: 102; precision: 0.686; recall: 0.686; fraction: 0.000\n",
      "5 cases: 101; precision: 0.584; recall: 0.584; fraction: 0.000\n",
      "6 cases: 101; precision: 0.634; recall: 0.634; fraction: 0.000\n",
      "7 cases: 101; precision: 0.554; recall: 0.554; fraction: 0.000\n",
      "8 cases: 101; precision: 0.604; recall: 0.604; fraction: 0.000\n",
      "9 cases: 101; precision: 0.604; recall: 0.604; fraction: 0.000\n",
      " col_0                __label__IRRELEVANT  __label__RELEVANT\n",
      "row_0                                                      \n",
      "__label__IRRELEVANT                  380                178\n",
      "__label__RELEVANT                    192                261\n",
      "cases: 1011; precision: 0.634; recall: 0.634; fraction: 0.000\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationFFB, predictionLabelsValidationFFB, predictionCountsTestFFB, predictionLabelsTestFFB = \\\n",
    "    runFasttext(fasttext_data_list_facemask_binary, fasttext_data_list_facemask_binary, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestFFB, f\"predictionLabelsTestFFB-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasttext_data_list_testing_binary = make_binary_fasttext_data(fasttext_data_list_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1181, data size 2=1181, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020.vec\n",
      "0 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.000\n",
      "1 cases: 118; precision: 0.619; recall: 0.619; fraction: 0.000\n",
      "2 cases: 118; precision: 0.686; recall: 0.686; fraction: 0.000\n",
      "3 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.000\n",
      "4 cases: 118; precision: 0.627; recall: 0.627; fraction: 0.000\n",
      "5 cases: 119; precision: 0.639; recall: 0.639; fraction: 0.000\n",
      "6 cases: 118; precision: 0.619; recall: 0.619; fraction: 0.000\n",
      "7 cases: 118; precision: 0.754; recall: 0.754; fraction: 0.000\n",
      "8 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.000\n",
      "9 cases: 118; precision: 0.610; recall: 0.610; fraction: 0.000\n",
      " col_0                __label__IRRELEVANT  __label__RELEVANT\n",
      "row_0                                                      \n",
      "__label__IRRELEVANT                  428                203\n",
      "__label__RELEVANT                    234                316\n",
      "cases: 1181; precision: 0.630; recall: 0.630; fraction: 0.000\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationTTB, predictionLabelsValidationTTB, predictionCountsTestTTB, predictionLabelsTestTTB = \\\n",
    "    runFasttext(fasttext_data_list_testing_binary, fasttext_data_list_testing_binary, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestTTB, f\"predictionLabelsTestTTB-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasttext_data_list_vaccination_binary = make_binary_fasttext_data(fasttext_data_list_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=1007, data size 2=1007, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020.vec\n",
      "0 cases: 101; precision: 0.663; recall: 0.663; fraction: 0.000\n",
      "1 cases: 100; precision: 0.680; recall: 0.680; fraction: 0.000\n",
      "2 cases: 101; precision: 0.713; recall: 0.713; fraction: 0.000\n",
      "3 cases: 101; precision: 0.673; recall: 0.673; fraction: 0.000\n",
      "4 cases: 101; precision: 0.574; recall: 0.574; fraction: 0.000\n",
      "5 cases: 100; precision: 0.570; recall: 0.570; fraction: 0.000\n",
      "6 cases: 101; precision: 0.584; recall: 0.584; fraction: 0.000\n",
      "7 cases: 101; precision: 0.515; recall: 0.515; fraction: 0.000\n",
      "8 cases: 100; precision: 0.580; recall: 0.580; fraction: 0.000\n",
      "9 cases: 101; precision: 0.594; recall: 0.594; fraction: 0.000\n",
      " col_0                __label__IRRELEVANT  __label__RELEVANT\n",
      "row_0                                                      \n",
      "__label__IRRELEVANT                  466                172\n",
      "__label__RELEVANT                    216                153\n",
      "cases: 1007; precision: 0.615; recall: 0.615; fraction: 0.000\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationVVB, predictionLabelsValidationVVB, predictionCountsTestVVB, predictionLabelsTestVVB = \\\n",
    "    runFasttext(fasttext_data_list_vaccination_binary, fasttext_data_list_vaccination_binary, squealFlag=False, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_fasttext_data(predictionLabelsTestVVB, f\"predictionLabelsTestVVB-{DIM}-{EPOCH}-{LR}.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Use relevant data only (RLVONLY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_irrelevant_annotated_data(fasttext_data_list):\n",
    "    return [annotated_text for annotated_text in fasttext_data_list if not re.search(r'^__label__IRRELEVANT ', annotated_text)]\n",
    "\n",
    "\n",
    "def remove_irrelevant_predicted_data(predictionLabelsTest, fasttext_data_list):\n",
    "    prediction_labels = get_prediction_labels_list(predictionLabelsTest)\n",
    "    assert len(prediction_labels) == len(fasttext_data_list), \"remove_irrelevant_data: lists have different lengths\"\n",
    "    fasttext_data_list_selected = []\n",
    "    for i in range(0, len(prediction_labels)):\n",
    "        if prediction_labels[i] != \"__label__IRRELEVANT\":\n",
    "            fasttext_data_list_selected.append(fasttext_data_list[i])\n",
    "    return fasttext_data_list_selected\n",
    "\n",
    "\n",
    "def restore_irrelevant_predicted_data(prediction_labels_in, prediction_labels_relevant):\n",
    "    prediction_labels = list(prediction_labels_in)\n",
    "    prediction_labels_relevant_index = 0\n",
    "    for i in range(0, len(prediction_labels)):\n",
    "        if prediction_labels[i] != \"__label__IRRELEVANT\":\n",
    "            prediction_labels[i] = prediction_labels_relevant[prediction_labels_relevant_index]\n",
    "            prediction_labels_relevant_index += 1\n",
    "    assert prediction_labels_relevant_index == len(prediction_labels_relevant), \\\n",
    "           f\"restore_irrelevant_predicted_data: length mismatch: {prediction_labels_relevant_index} {len(prediction_labels_relevant)}\"\n",
    "    return prediction_labels\n",
    "\n",
    "\n",
    "def save_fasttext_data(fasttext_data_list, file_name):\n",
    "    out_file = open(file_name, \"w\")\n",
    "    for line in fasttext_data_list:\n",
    "        print(line, file=out_file)\n",
    "    out_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_relevant_model(fasttext_data_list):\n",
    "    fasttext_data_list_selected = remove_irrelevant_annotated_data(fasttext_data_list)\n",
    "    print(f\"building relevant model from: data_size={len(fasttext_data_list_selected)}, dim={DIM}, epoch={EPOCH}, lr={LR}\")\n",
    "    tmp_file_name = str(uuid.uuid1())\n",
    "    save_fasttext_data(fasttext_data_list_distance_selected, tmp_file_name)\n",
    "    model_relevant = fasttext.train_supervised(tmp_file_name, dim=DIM, epoch=EPOCH, lr=LR, pretrainedVectors=\"twiqs-model-2020.vec\")\n",
    "    os.remove(tmp_file_name)\n",
    "    return model_relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building model from: data_size=4364, dim=300, epoch=200, lr=0.2\n"
     ]
    }
   ],
   "source": [
    "model_relevant = make_relevant_model(fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_relevant_experiment(model_relevant, result_file_name, fasttext_data_list):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = remove_irrelevant_data(predictionLabelsTest, fasttext_data_list)\n",
    "    prediction_labels_relevant = model_relevant.predict(fasttext_data_list_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list([prediction_labels_relevant]))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), fasttext_data_list)\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")\n",
    "    \n",
    "    \n",
    "def run_relevant_experiment_binary(model_relevant, result_file_name, fasttext_data_list):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = remove_irrelevant_data(predictionLabelsTest, fasttext_data_list)\n",
    "    prediction_labels_relevant = model_relevant.predict(fasttext_data_list_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list([prediction_labels_relevant]))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), make_binary_fasttext_data(fasttext_data_list))\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")\n",
    "    \n",
    "    \n",
    "def run_relevant_experiment_all(result_file_name, fasttext_data_list, fasttext_data_list_extra):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = remove_irrelevant_data(predictionLabelsTest, fasttext_data_list)\n",
    "    fasttext_data_list_extra_selected = remove_irrelevant_annotated_data(fasttext_data_list_extra)\n",
    "    predictionCountsValidationRun, predictionLabelsValidationRun, predictionCountsTestRun, predictionLabelsTestRun = \\\n",
    "        runFasttextPlus(fasttext_data_list_selected, \n",
    "                        fasttext_data_list_selected, \n",
    "                        squealFlag=False, \n",
    "                        pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                        extraFasttextData=fasttext_data_list_extra_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list(predictionLabelsTestRun))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    store_fasttext_data(prediction_labels_relevant_complete, f\"{result_file_name}.RLV-{DIM}-{EPOCH}-{LR}.pickle\")\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), fasttext_data_list)\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.58, accuracy (phase 1 plus gold): 0.776, accuracy (phase 2): 0.55\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_facemask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.634, accuracy (phase 1 plus gold): 0.81, accuracy (phase 2): 0.525\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestFFB-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_facemask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=373, data size 2=373, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 37; precision: 0.243; recall: 0.243; fraction: 2.347\n",
      "1 cases: 38; precision: 0.395; recall: 0.395; fraction: 0.774\n",
      "2 cases: 37; precision: 0.378; recall: 0.378; fraction: 1.067\n",
      "3 cases: 37; precision: 0.432; recall: 0.432; fraction: 2.615\n",
      "4 cases: 37; precision: 0.459; recall: 0.459; fraction: 2.196\n",
      "5 cases: 38; precision: 0.553; recall: 0.553; fraction: 0.833\n",
      "6 cases: 37; precision: 0.486; recall: 0.486; fraction: 0.312\n",
      "7 cases: 37; precision: 0.432; recall: 0.432; fraction: 0.290\n",
      "8 cases: 38; precision: 0.316; recall: 0.316; fraction: 0.360\n",
      "9 cases: 37; precision: 0.486; recall: 0.486; fraction: 0.341\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   23                51                 65\n",
      "__label__REJECTS                      25                72                 32\n",
      "__label__SUPPORTS                     22                22                 61\n",
      "cases: 373; precision: 0.418; recall: 0.418; fraction: 0.747\n",
      "accuracy (phase 1): 0.592, accuracy (phase 1 plus gold): 0.783, accuracy (phase 2): 0.569\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_all(f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_facemask, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.561, accuracy (phase 1 plus gold): 0.775, accuracy (phase 2): 0.547\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.63, accuracy (phase 1 plus gold): 0.802, accuracy (phase 2): 0.555\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestTTB-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=481, data size 2=481, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 48; precision: 0.188; recall: 0.188; fraction: 2.286\n",
      "1 cases: 48; precision: 0.438; recall: 0.438; fraction: 1.950\n",
      "2 cases: 48; precision: 0.417; recall: 0.417; fraction: 3.619\n",
      "3 cases: 48; precision: 0.500; recall: 0.500; fraction: 0.672\n",
      "4 cases: 48; precision: 0.562; recall: 0.562; fraction: 0.812\n",
      "5 cases: 49; precision: 0.592; recall: 0.592; fraction: 0.737\n",
      "6 cases: 48; precision: 0.521; recall: 0.521; fraction: 0.727\n",
      "7 cases: 48; precision: 0.688; recall: 0.688; fraction: 0.980\n",
      "8 cases: 48; precision: 0.542; recall: 0.542; fraction: 0.821\n",
      "9 cases: 48; precision: 0.562; recall: 0.562; fraction: 1.250\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   65                49                 83\n",
      "__label__REJECTS                      19                74                 22\n",
      "__label__SUPPORTS                     47                20                102\n",
      "cases: 481; precision: 0.501; recall: 0.501; fraction: 1.015\n",
      "accuracy (phase 1): 0.561, accuracy (phase 1 plus gold): 0.775, accuracy (phase 2): 0.572\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_all(f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_testing, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.6, accuracy (phase 1 plus gold): 0.771, accuracy (phase 2): 0.576\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy (phase 1): 0.615, accuracy (phase 1 plus gold): 0.786, accuracy (phase 2): 0.563\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment(model_relevant, f\"predictionLabelsTestVVB-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=273, data size 2=273, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 27; precision: 0.222; recall: 0.222; fraction: 2.438\n",
      "1 cases: 28; precision: 0.179; recall: 0.179; fraction: 0.582\n",
      "2 cases: 27; precision: 0.407; recall: 0.407; fraction: 0.306\n",
      "3 cases: 27; precision: 0.370; recall: 0.370; fraction: 0.636\n",
      "4 cases: 27; precision: 0.556; recall: 0.556; fraction: 0.306\n",
      "5 cases: 28; precision: 0.464; recall: 0.464; fraction: 0.971\n",
      "6 cases: 27; precision: 0.296; recall: 0.296; fraction: 0.000\n",
      "7 cases: 27; precision: 0.259; recall: 0.259; fraction: 0.423\n",
      "8 cases: 28; precision: 0.143; recall: 0.143; fraction: 0.389\n",
      "9 cases: 27; precision: 0.370; recall: 0.370; fraction: 1.750\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   31                64                 43\n",
      "__label__REJECTS                      46                50                 11\n",
      "__label__SUPPORTS                      6                14                  8\n",
      "cases: 273; precision: 0.326; recall: 0.326; fraction: 0.540\n",
      "accuracy (phase 1): 0.608, accuracy (phase 1 plus gold): 0.768, accuracy (phase 2): 0.585\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_all(f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_vaccination, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Daume Feature Space Separation\n",
    "\n",
    "Schemes:\n",
    "1. [x] without setting initial word vectors for new vector spaces\n",
    "2. [ ] while also changing classes\n",
    "3. [x] while setting initial word vectors for new vector spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_features(fasttext_data_list_in, suffix):\n",
    "    fasttext_data_list_out = []\n",
    "    for text in fasttext_data_list_in:\n",
    "        tokens_in = text.split()\n",
    "        tokens_out = []\n",
    "        for token in tokens_in[1:]:\n",
    "            tokens_out.append(token + suffix)\n",
    "        fasttext_data_list_out.append(\" \".join(tokens_in + tokens_out))\n",
    "    return fasttext_data_list_out\n",
    "\n",
    "\n",
    "def run_relevant_experiment_expand_features(result_file_name, fasttext_data_list, fasttext_data_list_extra):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = expand_features(remove_irrelevant_data(predictionLabelsTest, fasttext_data_list), \"@1\")\n",
    "    fasttext_data_list_extra_selected = expand_features(remove_irrelevant_annotated_data(fasttext_data_list_extra), \"@2\")\n",
    "    predictionCountsValidationRun, predictionLabelsValidationRun, predictionCountsTestRun, predictionLabelsTestRun = \\\n",
    "        runFasttextPlus(fasttext_data_list_selected, \n",
    "                        fasttext_data_list_selected, \n",
    "                        squealFlag=False, \n",
    "                        pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                        extraFasttextData=fasttext_data_list_extra_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list(predictionLabelsTestRun))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), fasttext_data_list)\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_features(fasttext_data_list_in, suffix):\n",
    "    fasttext_data_list_out = []\n",
    "    for text in fasttext_data_list_in:\n",
    "        tokens_in = text.split()\n",
    "        tokens_out = []\n",
    "        for token in tokens_in[1:]:\n",
    "            tokens_out.append(token + suffix)\n",
    "        fasttext_data_list_out.append(\" \".join(tokens_in + tokens_out))\n",
    "    return fasttext_data_list_out\n",
    "\n",
    "\n",
    "def run_relevant_experiment_expand_features(result_file_name, fasttext_data_list, fasttext_data_list_extra):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = expand_features(remove_irrelevant_data(predictionLabelsTest, fasttext_data_list), \"@1\")\n",
    "    fasttext_data_list_extra_selected = expand_features(remove_irrelevant_annotated_data(fasttext_data_list_extra), \"@2\")\n",
    "    predictionCountsValidationRun, predictionLabelsValidationRun, predictionCountsTestRun, predictionLabelsTestRun = \\\n",
    "        runFasttextPlus(fasttext_data_list_selected, \n",
    "                        fasttext_data_list_selected, \n",
    "                        squealFlag=False, \n",
    "                        pretrainedVectors=\"twiqs-model-2020.vec\",\n",
    "                        extraFasttextData=fasttext_data_list_extra_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list(predictionLabelsTestRun))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    store_fasttext_data(prediction_labels_relevant_complete, f\"{result_file_name}.EXF-{DIM}-{EPOCH}-{LR}.pickle\")\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), fasttext_data_list)\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")\n",
    "    \n",
    "    \n",
    "def run_relevant_experiment_expand_features_new(result_file_name, fasttext_data_list, fasttext_data_list_extra, data_name):\n",
    "    predictionLabelsTest = read_fasttext_data(result_file_name)\n",
    "    fasttext_data_list_selected = expand_features(remove_irrelevant_data(predictionLabelsTest, fasttext_data_list), f\"@{data_name}\")\n",
    "    fasttext_data_list_extra_selected = expand_features(remove_irrelevant_annotated_data(fasttext_data_list_extra), \"@distance\")\n",
    "    predictionCountsValidationRun, predictionLabelsValidationRun, predictionCountsTestRun, predictionLabelsTestRun = \\\n",
    "        runFasttextPlus(fasttext_data_list_selected, \n",
    "                        fasttext_data_list_selected, \n",
    "                        squealFlag=False, \n",
    "                        pretrainedVectors=\"twiqs-model-2020-exf.vec\",\n",
    "                        extraFasttextData=fasttext_data_list_extra_selected)\n",
    "    prediction_labels_relevant_complete = restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), get_prediction_labels_list(predictionLabelsTestRun))\n",
    "    accuracy = compute_accuracy(prediction_labels_relevant_complete, fasttext_data_list)\n",
    "    store_fasttext_data(prediction_labels_relevant_complete, f\"{result_file_name}.EXG-{DIM}-{EPOCH}-{LR}.pickle\")\n",
    "    accuracy_phase_1 = compute_accuracy(get_prediction_labels_list(predictionLabelsTest), fasttext_data_list)\n",
    "    gold_labels_selected = [annotated_text.split(\" \")[0] for annotated_text in fasttext_data_list_selected]\n",
    "    accuracy_phase_1_plus_gold = compute_accuracy(restore_irrelevant_predicted_data(get_prediction_labels_list(predictionLabelsTest), gold_labels_selected), fasttext_data_list) \n",
    "    print(f\"accuracy (phase 1): {accuracy_phase_1}, accuracy (phase 1 plus gold): {accuracy_phase_1_plus_gold}, accuracy (phase 2): {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=371, data size 2=371, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020.vec\n",
      "0 cases: 37; precision: 0.405; recall: 0.405; fraction: 2.133\n",
      "1 cases: 37; precision: 0.378; recall: 0.378; fraction: 0.798\n",
      "2 cases: 37; precision: 0.459; recall: 0.459; fraction: 0.779\n",
      "3 cases: 37; precision: 0.351; recall: 0.351; fraction: 1.970\n",
      "4 cases: 38; precision: 0.368; recall: 0.368; fraction: 1.981\n",
      "5 cases: 37; precision: 0.378; recall: 0.378; fraction: 0.675\n",
      "6 cases: 37; precision: 0.486; recall: 0.486; fraction: 1.007\n",
      "7 cases: 37; precision: 0.486; recall: 0.486; fraction: 0.339\n",
      "8 cases: 37; precision: 0.378; recall: 0.378; fraction: 0.641\n",
      "9 cases: 37; precision: 0.432; recall: 0.432; fraction: 0.364\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   46                39                 59\n",
      "__label__REJECTS                      42                61                 19\n",
      "__label__SUPPORTS                     41                18                 46\n",
      "cases: 371; precision: 0.412; recall: 0.412; fraction: 0.819\n",
      "accuracy (phase 1): 0.58, accuracy (phase 1 plus gold): 0.776, accuracy (phase 2): 0.561\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features(f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_facemask, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=481, data size 2=481, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 48; precision: 0.312; recall: 0.312; fraction: 2.800\n",
      "1 cases: 48; precision: 0.500; recall: 0.500; fraction: 0.867\n",
      "2 cases: 48; precision: 0.417; recall: 0.417; fraction: 3.800\n",
      "3 cases: 48; precision: 0.479; recall: 0.479; fraction: 0.886\n",
      "4 cases: 48; precision: 0.667; recall: 0.667; fraction: 0.645\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-450-cc4cfe11df0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_relevant_experiment_expand_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfasttext_data_list_testing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfasttext_data_list_distance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-449-201d0d6c2742>\u001b[0m in \u001b[0;36mrun_relevant_experiment_expand_features\u001b[0;34m(result_file_name, fasttext_data_list, fasttext_data_list_extra)\u001b[0m\n\u001b[1;32m     19\u001b[0m                         \u001b[0msquealFlag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m                         \u001b[0mpretrainedVectors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"twiqs-model-2020-exf.vec\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m                         extraFasttextData=fasttext_data_list_extra_selected)\n\u001b[0m\u001b[1;32m     22\u001b[0m     \u001b[0mprediction_labels_relevant_complete\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrestore_irrelevant_predicted_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_prediction_labels_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictionLabelsTest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_prediction_labels_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictionLabelsTestRun\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     23\u001b[0m     \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_accuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_labels_relevant_complete\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfasttext_data_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-167-e85d180c4d6b>\u001b[0m in \u001b[0;36mrunFasttextPlus\u001b[0;34m(fasttextData1, fasttextData2, dim, epoch, lr, n, squealFlag, maxTrain, pretrainedVectors, extraFasttextData)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfasttext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_supervised\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\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---> 70\u001b[0;31m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfasttext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_supervised\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrainedVectors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpretrainedVectors\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     71\u001b[0m         \u001b[0mpredictionCountsValidation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mVALIDATION\u001b[0m\u001b[0;34m)\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     72\u001b[0m         \u001b[0mpredictionLabelsValidation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidationData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\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~/anaconda3/envs/python37/lib/python3.7/site-packages/fasttext/FastText.py\u001b[0m in \u001b[0;36mtrain_supervised\u001b[0;34m(*kargs, **kwargs)\u001b[0m\n\u001b[1;32m    531\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmanually_set_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m     \u001b[0mft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_FastText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m     \u001b[0mfasttext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\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    534\u001b[0m     \u001b[0mft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetArgs\u001b[0m\u001b[0;34m(\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    535\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features(f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_testing, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=284, data size 2=284, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020.vec\n",
      "0 cases: 28; precision: 0.357; recall: 0.357; fraction: 2.143\n",
      "1 cases: 29; precision: 0.690; recall: 0.690; fraction: 0.700\n",
      "2 cases: 28; precision: 0.429; recall: 0.429; fraction: 9999999999.000\n",
      "3 cases: 29; precision: 0.345; recall: 0.345; fraction: 0.821\n",
      "4 cases: 28; precision: 0.464; recall: 0.464; fraction: 1.625\n",
      "5 cases: 28; precision: 0.571; recall: 0.571; fraction: 0.353\n",
      "6 cases: 29; precision: 0.517; recall: 0.517; fraction: 0.000\n",
      "7 cases: 28; precision: 0.357; recall: 0.357; fraction: 0.577\n",
      "8 cases: 29; precision: 0.345; recall: 0.345; fraction: 1.944\n",
      "9 cases: 28; precision: 0.429; recall: 0.429; fraction: 11.375\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   72                55                 19\n",
      "__label__REJECTS                      55                51                  4\n",
      "__label__SUPPORTS                     14                 9                  5\n",
      "cases: 284; precision: 0.451; recall: 0.451; fraction: 1.045\n",
      "accuracy (phase 1): 0.6, accuracy (phase 1 plus gold): 0.771, accuracy (phase 2): 0.616\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features(f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_vaccination, fasttext_data_list_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make domain word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tokens(fasttext_data_list):\n",
    "    tokens = {}\n",
    "    for text in fasttext_data_list:\n",
    "        for token in text.split()[1:]:\n",
    "            tokens[token] = True\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_distance = get_tokens(fasttext_data_list_distance)\n",
    "tokens_facemask = get_tokens(fasttext_data_list_facemask)\n",
    "tokens_testing = get_tokens(fasttext_data_list_testing)\n",
    "tokens_vaccination = get_tokens(fasttext_data_list_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_all = dict(tokens_distance)\n",
    "tokens_all.update(tokens_facemask)\n",
    "tokens_all.update(tokens_testing)\n",
    "tokens_all.update(tokens_vaccination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need to fix first line: number_of_tokens vector_length\n",
    "file_name_in = \"twiqs-model-2020.vec\"\n",
    "file_name_out = \"twiqs-model-2020-exf.vec\"\n",
    "infile = open(file_name_in, \"r\")\n",
    "outfile = open(file_name_out, \"w\")\n",
    "for line in infile:\n",
    "    token = line.strip().split()[0]\n",
    "    rest = \" \".join(line.strip().split()[1:])\n",
    "    if token in tokens_all:\n",
    "        print(f\"{token} {rest}\", file=outfile)\n",
    "        if token in tokens_distance:\n",
    "            print(f\"{token}@distance {rest}\", file=outfile)\n",
    "        if token in tokens_facemask:\n",
    "            print(f\"{token}@facemask {rest}\", file=outfile)\n",
    "        if token in tokens_testing:\n",
    "            print(f\"{token}@testing {rest}\", file=outfile)\n",
    "        if token in tokens_vaccination:\n",
    "            print(f\"{token}@vaccination {rest}\", file=outfile)\n",
    "outfile.close()\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=373, data size 2=373, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 37; precision: 0.351; recall: 0.351; fraction: 2.500\n",
      "1 cases: 38; precision: 0.474; recall: 0.474; fraction: 0.765\n",
      "2 cases: 37; precision: 0.405; recall: 0.405; fraction: 0.867\n",
      "3 cases: 37; precision: 0.459; recall: 0.459; fraction: 3.750\n",
      "4 cases: 37; precision: 0.378; recall: 0.378; fraction: 2.667\n",
      "5 cases: 38; precision: 0.395; recall: 0.395; fraction: 0.794\n",
      "6 cases: 37; precision: 0.676; recall: 0.676; fraction: 0.873\n",
      "7 cases: 37; precision: 0.486; recall: 0.486; fraction: 0.354\n",
      "8 cases: 38; precision: 0.447; recall: 0.447; fraction: 0.576\n",
      "9 cases: 37; precision: 0.486; recall: 0.486; fraction: 0.445\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   51                37                 51\n",
      "__label__REJECTS                      37                73                 19\n",
      "__label__SUPPORTS                     38                21                 46\n",
      "cases: 373; precision: 0.456; recall: 0.456; fraction: 0.919\n",
      "accuracy (phase 1): 0.592, accuracy (phase 1 plus gold): 0.783, accuracy (phase 2): 0.583\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features_new(f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_facemask, fasttext_data_list_distance, \"facemask\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=481, data size 2=481, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 48; precision: 0.354; recall: 0.354; fraction: 2.400\n",
      "1 cases: 48; precision: 0.438; recall: 0.438; fraction: 1.444\n",
      "2 cases: 48; precision: 0.396; recall: 0.396; fraction: 3.562\n",
      "3 cases: 48; precision: 0.500; recall: 0.500; fraction: 0.848\n",
      "4 cases: 48; precision: 0.729; recall: 0.729; fraction: 0.692\n",
      "5 cases: 49; precision: 0.490; recall: 0.490; fraction: 0.590\n",
      "6 cases: 48; precision: 0.417; recall: 0.417; fraction: 0.647\n",
      "7 cases: 48; precision: 0.583; recall: 0.583; fraction: 0.935\n",
      "8 cases: 48; precision: 0.583; recall: 0.583; fraction: 0.686\n",
      "9 cases: 48; precision: 0.521; recall: 0.521; fraction: 1.212\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   88                39                 70\n",
      "__label__REJECTS                      38                62                 15\n",
      "__label__SUPPORTS                     65                13                 91\n",
      "cases: 481; precision: 0.501; recall: 0.501; fraction: 0.952\n",
      "accuracy (phase 1): 0.561, accuracy (phase 1 plus gold): 0.775, accuracy (phase 2): 0.572\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features_new(f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_testing, fasttext_data_list_distance, \"testing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size 1=273, data size 2=273, dim=300, epoch=200, lr=0.2, pretrainedVectors=twiqs-model-2020-exf.vec\n",
      "0 cases: 27; precision: 0.296; recall: 0.296; fraction: 2.571\n",
      "1 cases: 28; precision: 0.464; recall: 0.464; fraction: 0.571\n",
      "2 cases: 27; precision: 0.593; recall: 0.593; fraction: 9999999999.000\n",
      "3 cases: 27; precision: 0.333; recall: 0.333; fraction: 1.273\n",
      "4 cases: 27; precision: 0.593; recall: 0.593; fraction: 0.524\n",
      "5 cases: 28; precision: 0.536; recall: 0.536; fraction: 0.397\n",
      "6 cases: 27; precision: 0.481; recall: 0.481; fraction: 0.000\n",
      "7 cases: 27; precision: 0.296; recall: 0.296; fraction: 0.564\n",
      "8 cases: 28; precision: 0.429; recall: 0.429; fraction: 6.000\n",
      "9 cases: 27; precision: 0.370; recall: 0.370; fraction: 12.250\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                   62                56                 20\n",
      "__label__REJECTS                      50                53                  4\n",
      "__label__SUPPORTS                     16                 7                  5\n",
      "cases: 273; precision: 0.440; recall: 0.440; fraction: 1.047\n",
      "accuracy (phase 1): 0.608, accuracy (phase 1 plus gold): 0.768, accuracy (phase 2): 0.616\n"
     ]
    }
   ],
   "source": [
    "run_relevant_experiment_expand_features_new(f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\", fasttext_data_list_vaccination, fasttext_data_list_distance, \"vaccination\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Multi-task learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Numeric analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "WINDOW_SIZE = 100\n",
    "\n",
    "\n",
    "def get_labels_from_fasttext_data(fasttext_data_list):\n",
    "    return([text.split()[0] for text in fasttext_data_list])\n",
    "\n",
    "\n",
    "def percentage(window):\n",
    "    total_count = 0\n",
    "    support_count = 0\n",
    "    for label in window:\n",
    "        total_count += 1\n",
    "        if label == \"__label__SUPPORTS\":\n",
    "            support_count += 1\n",
    "        else:\n",
    "            assert label == '__label__REJECTS', f\"percentage: unexpected label value: {label}\"\n",
    "    return(100 * support_count / total_count)\n",
    "\n",
    "\n",
    "def make_graph_data_make_relevant(label_list):\n",
    "    label_list_relevant = [ label for label in label_list if label != '__label__IRRELEVANT' ]\n",
    "    window = []\n",
    "    graph_data_relevant = len(label_list_relevant) * [None]\n",
    "    for i in range(0, len(label_list_relevant)):\n",
    "        window.append(label_list_relevant[i])\n",
    "        if len(window) >= WINDOW_SIZE:\n",
    "            index = round((i - WINDOW_SIZE + 1) * (len(label_list_relevant)-1)/(len(label_list_relevant)-WINDOW_SIZE))\n",
    "            assert index >= 0, f\"make_graph_data_make_relevant: index ({index}) cannot be negative!\"\n",
    "            graph_data_relevant[index] = percentage(window)        \n",
    "        while len(window) >= WINDOW_SIZE:\n",
    "            window.pop(0)\n",
    "    return graph_data_relevant\n",
    "            \n",
    "    \n",
    "def make_graph_data_fill_data(label_list, graph_data_relevant):\n",
    "    graph_data = len(label_list) * [None]\n",
    "    index_relevant = 0\n",
    "    for i in range(0, len(label_list)):\n",
    "        if label_list[i] != '__label__IRRELEVANT':\n",
    "            graph_data[i] = graph_data_relevant[index_relevant]\n",
    "            index_relevant += 1\n",
    "    assert index_relevant == len(graph_data_relevant), f\"make_graph_data: unexpected length: {index_relevant} {len(graph_data_relevant)}\"\n",
    "    return graph_data\n",
    "   \n",
    "    \n",
    "def make_graph_data_interpolate_center(graph_data):\n",
    "    for i in range(0, len(graph_data)):\n",
    "        if graph_data[i] != None and i+1 < len(graph_data) and graph_data[i+1] == None:\n",
    "            for j in range(i+1, len(graph_data)):\n",
    "                if graph_data[j] != None:\n",
    "                    break\n",
    "            if j < len(graph_data) and graph_data[j] != None:\n",
    "                for k in range(i+1, j):\n",
    "                    graph_data[k] = graph_data[i] + (k-i) * (graph_data[j] - graph_data[i])/(j - i)\n",
    "\n",
    "    return graph_data\n",
    "    \n",
    "    \n",
    "def make_graph_data_interpolate_start(graph_data):\n",
    "    for i in range(0, len(graph_data)):\n",
    "        if graph_data[i] != None:\n",
    "            break\n",
    "    if i > 0 and i+1 < len(graph_data):\n",
    "        for j in range(i-1, -1, -1):\n",
    "            graph_data[j] = graph_data[i] + (i - j) * (graph_data[i] - graph_data[i+1]) \n",
    "    return graph_data\n",
    "\n",
    "\n",
    "def make_graph_data_interpolate_end(graph_data):\n",
    "    for i in range(len(graph_data)-1, -1, -1):\n",
    "        if graph_data[i] != None:\n",
    "            break\n",
    "    if i > 0 and i < len(graph_data)-1:\n",
    "        for j in range(i+1, len(graph_data)):\n",
    "            graph_data[j] = graph_data[i] + (j - i) * (graph_data[i] - graph_data[i-1]) \n",
    "    return graph_data\n",
    "\n",
    "\n",
    "def make_graph_data_interpolate(graph_data):    \n",
    "    graph_data = make_graph_data_interpolate_center(graph_data)\n",
    "    graph_data = make_graph_data_interpolate_start(graph_data)\n",
    "    graph_data = make_graph_data_interpolate_end(graph_data)\n",
    "    return graph_data\n",
    "\n",
    "\n",
    "def make_graph_data(label_list):\n",
    "    graph_data_relevant = make_graph_data_make_relevant(label_list)\n",
    "    graph_data = make_graph_data_fill_data(label_list, graph_data_relevant)\n",
    "    graph_data = make_graph_data_interpolate(graph_data)\n",
    "    return graph_data\n",
    "\n",
    "\n",
    "def get_r_value(list1, list2):\n",
    "    return round(scipy.stats.linregress(np.array(list1), np.array(list2)).rvalue, 3)\n",
    "\n",
    "\n",
    "def absolute_difference(list1, list2):\n",
    "    absolute_difference = 0\n",
    "    for i in range(0, len(list1)):\n",
    "        absolute_difference += abs(list1[i]-list2[i])\n",
    "    return(round(0.01*absolute_difference/len(list1), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 Face masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_data_facemask_gold = make_graph_data(get_labels_from_fasttext_data(fasttext_data_list_facemask))\n",
    "graph_data_facemask_FFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestFFT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_facemask_DFT = make_graph_data(get_prediction_labels_list([model_distance.predict(fasttext_data_list_facemask)]))\n",
    "graph_data_facemask_EFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestEFT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_facemask_RLV = make_graph_data(read_fasttext_data(\"predictionLabelsTestFFT-300-200-0.2.pickle.RLV-300-200-0.2.pickle\"))\n",
    "graph_data_facemask_EXG = make_graph_data(read_fasttext_data(\"predictionLabelsTestFFT-300-200-0.2.pickle.EXG-300-200-0.2.pickle\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.877, 0.533, 0.435, 0.743, 0.806)"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(get_r_value(graph_data_facemask_gold, graph_data_facemask_FFT), \n",
    " get_r_value(graph_data_facemask_gold, graph_data_facemask_DFT),\n",
    " get_r_value(graph_data_facemask_gold, graph_data_facemask_EFT),\n",
    " get_r_value(graph_data_facemask_gold, graph_data_facemask_RLV),\n",
    " get_r_value(graph_data_facemask_gold, graph_data_facemask_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.08, 0.177, 0.143, 0.133, 0.125)"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(absolute_difference(graph_data_facemask_gold, graph_data_facemask_FFT), \n",
    " absolute_difference(graph_data_facemask_gold, graph_data_facemask_DFT),\n",
    " absolute_difference(graph_data_facemask_gold, graph_data_facemask_EFT),\n",
    " absolute_difference(graph_data_facemask_gold, graph_data_facemask_RLV),\n",
    " absolute_difference(graph_data_facemask_gold, graph_data_facemask_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIME_FILE = \"csv/time_data.mondkapje-202003-202103-1000.csv\"\n",
    "\n",
    "time_data_facemask_all = pd.read_csv(TIME_FILE, index_col=\"id_str\").to_dict(orient=\"series\")['created_at']\n",
    "time_data_facemask = []\n",
    "for tweet_id in tweet_ids_facemask:\n",
    "    try:\n",
    "        time_data_facemask.append(datetime.datetime.strptime(time_data_facemask_all[tweet_id], \"%a %b %d %H:%M:%S %z %Y\"))\n",
    "    except:\n",
    "        print(f\"key not found {tweet_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%m\"))\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_gold, fmt=\"-\", label=\"gold\")\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_FFT, fmt=\"-\", label=\"TGTONLY\")\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_DFT, fmt=\"-\", label=\"SRCONLY\")\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_EFT, fmt=\"-\", label=\"ALL\")\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_RLV, fmt=\"-\", label=\"RLVONLY\")\n",
    "ax.plot_date(time_data_facemask, graph_data_facemask_EXG, fmt=\"-\", label=\"FEATAUG\")\n",
    "plt.ylim(0, 100)\n",
    "plt.title(\"face masks\")\n",
    "plt.xlabel(\"months in the years 2020 and 2021\")\n",
    "plt.ylabel(\"percentage support\")\n",
    "plt.legend()\n",
    "plt.savefig(\"clin-facemask.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_data_testing_gold = make_graph_data(get_labels_from_fasttext_data(fasttext_data_list_testing))\n",
    "graph_data_testing_FFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestTTT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_testing_DFT = make_graph_data(get_prediction_labels_list([model_distance.predict(fasttext_data_list_testing)]))\n",
    "graph_data_testing_EFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestETT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_testing_RLV = make_graph_data(read_fasttext_data(\"predictionLabelsTestTTT-300-200-0.2.pickle.RLV-300-200-0.2.pickle\"))\n",
    "graph_data_testing_EXG = make_graph_data(read_fasttext_data(\"predictionLabelsTestTTT-300-200-0.2.pickle.EXG-300-200-0.2.pickle\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.949, 0.819, 0.882, 0.856, 0.848)"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(get_r_value(graph_data_testing_gold, graph_data_testing_FFT), \n",
    " get_r_value(graph_data_testing_gold, graph_data_testing_DFT),\n",
    " get_r_value(graph_data_testing_gold, graph_data_testing_EFT),\n",
    " get_r_value(graph_data_testing_gold, graph_data_testing_RLV),\n",
    " get_r_value(graph_data_testing_gold, graph_data_testing_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.093, 0.169, 0.097, 0.087, 0.102)"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(absolute_difference(graph_data_testing_gold, graph_data_testing_FFT), \n",
    " absolute_difference(graph_data_testing_gold, graph_data_testing_DFT),\n",
    " absolute_difference(graph_data_testing_gold, graph_data_testing_EFT),\n",
    " absolute_difference(graph_data_testing_gold, graph_data_testing_RLV),\n",
    " absolute_difference(graph_data_testing_gold, graph_data_testing_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIME_FILE = \"csv/time_data.testing-202003-202012.csv\"\n",
    "\n",
    "time_data_testing_all = pd.read_csv(TIME_FILE, index_col=\"id_str\").to_dict(orient=\"series\")['created_at']\n",
    "time_data_testing = []\n",
    "for tweet_id in tweet_ids_testing:\n",
    "    try:\n",
    "        time_data_testing.append(datetime.datetime.strptime(time_data_testing_all[tweet_id], \"%a %b %d %H:%M:%S %z %Y\"))\n",
    "    except:\n",
    "        print(f\"key not found {tweet_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%m\"))\n",
    "ax.plot_date(time_data_testing, graph_data_testing_gold, fmt=\"-\", label=\"gold\")\n",
    "ax.plot_date(time_data_testing, graph_data_testing_FFT, fmt=\"-\", label=\"TGTONLY\")\n",
    "ax.plot_date(time_data_testing, graph_data_testing_DFT, fmt=\"-\", label=\"SRCONLY\")\n",
    "ax.plot_date(time_data_testing, graph_data_testing_EFT, fmt=\"-\", label=\"ALL\")\n",
    "ax.plot_date(time_data_testing, graph_data_testing_RLV, fmt=\"-\", label=\"RLVONLY\")\n",
    "ax.plot_date(time_data_testing, graph_data_testing_EXG, fmt=\"-\", label=\"FEATAUG\")\n",
    "plt.ylim(0, 100)\n",
    "plt.title(\"testing\")\n",
    "plt.xlabel(\"months in the years 2020 and 2021\")\n",
    "plt.ylabel(\"percentage support\")\n",
    "plt.legend()\n",
    "plt.savefig(\"clin-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3 Vaccination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_data_vaccination_gold = make_graph_data(get_labels_from_fasttext_data(fasttext_data_list_vaccination))\n",
    "graph_data_vaccination_FFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestVVT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_vaccination_DFT = make_graph_data(get_prediction_labels_list([model_distance.predict(fasttext_data_list_vaccination)]))\n",
    "graph_data_vaccination_EFT = make_graph_data(get_prediction_labels_list(read_fasttext_data(f\"predictionLabelsTestEVT-{DIM}-{EPOCH}-{LR}.pickle\")))\n",
    "graph_data_vaccination_RLV = make_graph_data(read_fasttext_data(\"predictionLabelsTestVVT-300-200-0.2.pickle.RLV-300-200-0.2.pickle\"))\n",
    "graph_data_vaccination_EXG = make_graph_data(read_fasttext_data(\"predictionLabelsTestVVT-300-200-0.2.pickle.EXG-300-200-0.2.pickle\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.361, 0.586, 0.592, 0.528, -0.824)"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(get_r_value(graph_data_vaccination_gold, graph_data_vaccination_FFT), \n",
    " get_r_value(graph_data_vaccination_gold, graph_data_vaccination_DFT),\n",
    " get_r_value(graph_data_vaccination_gold, graph_data_vaccination_EFT),\n",
    " get_r_value(graph_data_vaccination_gold, graph_data_vaccination_RLV),\n",
    " get_r_value(graph_data_vaccination_gold, graph_data_vaccination_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.078, 0.26, 0.149, 0.107, 0.102)"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(absolute_difference(graph_data_vaccination_gold, graph_data_vaccination_FFT), \n",
    " absolute_difference(graph_data_vaccination_gold, graph_data_vaccination_DFT),\n",
    " absolute_difference(graph_data_vaccination_gold, graph_data_vaccination_EFT),\n",
    " absolute_difference(graph_data_vaccination_gold, graph_data_vaccination_RLV),\n",
    " absolute_difference(graph_data_vaccination_gold, graph_data_vaccination_EXG)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "TIME_FILE = \"csv/time_data.vaccin-202001-202101-1000.csv\"\n",
    "\n",
    "time_data_vaccination_all = pd.read_csv(TIME_FILE, index_col=\"id_str\").to_dict(orient=\"series\")['created_at']\n",
    "time_data_vaccination = []\n",
    "for tweet_id in tweet_ids_vaccination:\n",
    "    try:\n",
    "        time_data_vaccination.append(datetime.datetime.strptime(time_data_vaccination_all[tweet_id], \"%a %b %d %H:%M:%S %z %Y\"))\n",
    "    except:\n",
    "        print(f\"key not found {tweet_id}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%m\"))\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_gold, fmt=\"-\", label=\"gold\")\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_FFT, fmt=\"-\", label=\"TGTONLY\")\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_DFT, fmt=\"-\", label=\"SRCONLY\")\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_EFT, fmt=\"-\", label=\"ALL\")\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_RLV, fmt=\"-\", label=\"RLVONLY\")\n",
    "ax.plot_date(time_data_vaccination, graph_data_vaccination_EXG, fmt=\"-\", label=\"FEATAUG\")\n",
    "plt.ylim(0, 100)\n",
    "plt.title(\"vaccination\")\n",
    "plt.xlabel(\"months in the years 2020 and 2021\")\n",
    "plt.ylabel(\"percentage support\")\n",
    "plt.legend()\n",
    "plt.savefig(\"clin-vaccination.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Old code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDT = read_fasttext_data(\"predictionLabelsTestDT.pickle\")\n",
    "predictionLabelsTestTT = read_fasttext_data(\"predictionLabelsTestTT.pickle\")\n",
    "predictionLabelsTestET = read_fasttext_data(\"predictionLabelsTestET.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_factor = 3\n",
    "data_predicted_dt = make_data_predicted(predictionLabelsTestDT, tweet_ids_testing, time_factor=time_factor)\n",
    "data_predicted_tt = make_data_predicted(predictionLabelsTestTT, tweet_ids_testing, time_factor=time_factor)\n",
    "data_predicted_et = make_data_predicted(predictionLabelsTestET, tweet_ids_testing, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold = []\n",
    "for tweet in fasttext_data_list_testing:\n",
    "    gold.append(tweet.split()[0])\n",
    "\n",
    "data_gold = {}\n",
    "for i in range(0, len(tweet_ids_testing)):\n",
    "    summary = str(tweet_ids_testing[i])[0:time_factor]\n",
    "    if summary not in data_gold:\n",
    "        data_gold[summary] = []\n",
    "    data_gold[summary].append(gold[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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": [
    "plot_data_predicted_dt = convert(data_predicted_dt)\n",
    "plot_data_predicted_tt = convert(data_predicted_tt)\n",
    "plot_data_predicted_et = convert(data_predicted_et)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_dt = [x[0] for x in plot_data_predicted_dt]\n",
    "y_dt = [x[1] for x in plot_data_predicted_dt]\n",
    "x_tt = [x[0] for x in plot_data_predicted_tt]\n",
    "y_tt = [x[1] for x in plot_data_predicted_tt]\n",
    "x_et = [x[0] for x in plot_data_predicted_et]\n",
    "y_et = [x[1] for x in plot_data_predicted_et]\n",
    "plt.plot(x_dt, y_dt, label=\"predicted (out-of-domain)\", c=\"C0\")\n",
    "plt.plot(x_tt, y_tt, label=\"predicted (in-domain)\", c=\"C1\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\", c=\"C2\")\n",
    "#plt.plot(x_et, y_et, label=\"predicted (added data)\", c=\"C3\")\n",
    "plt.legend()\n",
    "plt.title(\"predicting testing with social distancing data\")\n",
    "plt.savefig(\"distancing-to-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inflate predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__SUPPORTS': 0.28789161727349705, '__label__IRRELEVANT': 0.5342929720575783, '__label__REJECTS': 0.17781541066892464}\n",
      "0.6176470588235294\n"
     ]
    }
   ],
   "source": [
    "freqs = get_freqs(gold)\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDT = read_fasttext_data(\"predictionLabelsTestDT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__label__REJECTS': 0.26418289585097376, '__label__SUPPORTS': 0.42675698560541914, '__label__IRRELEVANT': 0.3090601185436071}\n",
      "0.6190476190476191\n"
     ]
    }
   ],
   "source": [
    "FACTOR = 0.7957\n",
    "\n",
    "predictionLabelsTestDTinflated = list(predictionLabelsTestDT)\n",
    "freqs = get_freqs([y[0] for x in inflate_plus(predictionLabelsTestDTinflated, '__label__REJECTS', '__label__SUPPORTS', FACTOR) for y in x[0]])\n",
    "print(freqs)\n",
    "print(freqs['__label__REJECTS']/freqs['__label__SUPPORTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_predicted_dt_inflated = make_data_predicted(inflate(predictionLabelsTestDTinflated, \n",
    "                                                         '__label__REJECTS', \n",
    "                                                         '__label__SUPPORTS', \n",
    "                                                         FACTOR), \n",
    "                                                         tweet_ids_testing, \n",
    "                                                         time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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": [
    "plot_data_predicted_dt = convert(data_predicted_dt)\n",
    "plot_data_predicted_tt = convert(data_predicted_tt)\n",
    "plot_data_predicted_et = convert(data_predicted_et)\n",
    "plot_data_predicted_dt_inflated = convert(data_predicted_dt_inflated)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_dt = [x[0] for x in plot_data_predicted_dt]\n",
    "y_dt = [x[1] for x in plot_data_predicted_dt]\n",
    "x_tt = [x[0] for x in plot_data_predicted_tt]\n",
    "y_tt = [x[1] for x in plot_data_predicted_tt]\n",
    "x_et = [x[0] for x in plot_data_predicted_et]\n",
    "y_et = [x[1] for x in plot_data_predicted_et]\n",
    "x_dt_inflated = [x[0] for x in plot_data_predicted_dt_inflated]\n",
    "y_dt_inflated = [x[1] for x in plot_data_predicted_dt_inflated]\n",
    "\n",
    "#plt.plot(x_dt, y_dt, label=\"predicted (out-of-domain)\", c=\"C0\")\n",
    "#plt.plot(x_tt, y_tt, label=\"predicted (in-domain)\", c=\"C1\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\", c=\"C2\")\n",
    "plt.plot(x_et, y_et, label=\"predicted (added data)\", c=\"C3\")\n",
    "plt.plot(x_dt_inflated, y_dt_inflated, label=\"predicted (out-of-domain, inflated)\", c=\"C4\")\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"predicting COVID-19 testing with social distancing data\")\n",
    "plt.savefig(\"distancing-to-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing domain-dependent words (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 118; precision: 0.364; recall: 0.364; fraction: 0.462\n",
      "1 cases: 118; precision: 0.407; recall: 0.407; fraction: 0.614\n",
      "2 cases: 118; precision: 0.432; recall: 0.432; fraction: 0.719\n",
      "3 cases: 118; precision: 0.390; recall: 0.390; fraction: 0.465\n",
      "4 cases: 118; precision: 0.331; recall: 0.331; fraction: 0.626\n",
      "5 cases: 119; precision: 0.387; recall: 0.387; fraction: 0.553\n",
      "6 cases: 118; precision: 0.432; recall: 0.432; fraction: 0.194\n",
      "7 cases: 118; precision: 0.390; recall: 0.390; fraction: 0.099\n",
      "8 cases: 118; precision: 0.432; recall: 0.432; fraction: 0.991\n",
      "9 cases: 118; precision: 0.314; recall: 0.314; fraction: 1.359\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  197                98                336\n",
      "__label__REJECTS                      70                51                 89\n",
      "__label__SUPPORTS                     92                38                210\n",
      "cases: 1181; precision: 0.388; recall: 0.388; fraction: 0.477\n"
     ]
    }
   ],
   "source": [
    "predictionCountsValidationDTD, predictionLabelsValidationDTD, predictionCountsTestDTD, predictionLabelsTestDTD = \\\n",
    "        runFasttext(fasttext_data_list_distance, fasttext_data_list_testing, squealFlag=False, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_tokens(tweet_list):\n",
    "    counts = {}\n",
    "    for tweet in tweet_list:\n",
    "        for token in tweet.split()[1:]:\n",
    "            if not token in counts:\n",
    "                counts[token] = 0\n",
    "            counts[token] += 1\n",
    "    return(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196888 52387\n"
     ]
    }
   ],
   "source": [
    "counts_train = count_tokens(fasttext_data_list_distance)\n",
    "counts_test = count_tokens(fasttext_data_list_testing)\n",
    "counts_train_total = sum(counts_train.values())\n",
    "counts_test_total = sum(counts_test.values())\n",
    "print(counts_train_total, counts_test_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_infrequent_tokens(tweet_list_in, counts_train, counts_test, counts_train_total, counts_test_total, minimum_frequency=0):\n",
    "    tweet_list_out = []\n",
    "    token_count = 0\n",
    "    for tweet in tweet_list_in:\n",
    "        accepted_tokens = []\n",
    "        tweet_tokens = tweet.split()\n",
    "        for token in tweet_tokens[1:]:\n",
    "            if token in counts_train and token in counts_test and \\\n",
    "               counts_train[token] / counts_train_total > minimum_frequency and \\\n",
    "               counts_test[token] / counts_test_total > minimum_frequency:\n",
    "                accepted_tokens.append(token)\n",
    "        token_count += 1 + len(accepted_tokens)\n",
    "        if len(accepted_tokens) == 0:\n",
    "            accepted_tokens = [ \"DUMMY\" ]\n",
    "        tweet_list_out.append(\" \".join(tweet_tokens[:1]+accepted_tokens))\n",
    "    print(token_count)\n",
    "    return(tweet_list_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137571\n",
      "36390\n"
     ]
    }
   ],
   "source": [
    "MINFREQ = 0.0002\n",
    "fasttext_data_list_distance_filtered = remove_infrequent_tokens(fasttext_data_list_distance, counts_train, counts_test, counts_train_total, counts_test_total, minimum_frequency=MINFREQ)\n",
    "fasttext_data_list_testing_filtered = remove_infrequent_tokens(fasttext_data_list_testing, counts_train, counts_test, counts_train_total, counts_test_total, minimum_frequency=MINFREQ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDTD, predictionLabelsValidationDTD, predictionCountsTestDTD, predictionLabelsTestDTD = \\\n",
    "        runFasttext(fasttext_data_list_distance_filtered, fasttext_data_list_testing, squealFlag=False, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    store_fasttext_data(predictionLabelsTestDTD, \"predictionLabelsTestDTD0.0002A.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDTD = read_fasttext_data(\"predictionLabelsTestDTD0.0002A.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  method | train | test | accuracy | fraction | comment |\n",
    "| ------- | ----- | ---- | -------- | -------- | ------- |\n",
    "| none        | 196888 | 52387 | 0.385 | 0.478 | |\n",
    "| M = 0       | 171520 | 49477 | 0.379 | 0.650 | |\n",
    "| M = 0.00001 | 170682 | 48139 | 0.375 | 0.650 | |\n",
    "| M = 0.0001  | 145771 | 39891 | 0.379 | 0.688 | |\n",
    "| M = 0.0002  | 137571 | 36390 | 0.390 | 0.833 | |\n",
    "| M = 0.0003  | 127440 | 34457 | 0.359 | 0.776 | |\n",
    "| M = 0.0004  | 119718 | 32913 | 0.346 | 0.764 | |\n",
    "| M = 0.001   |  99965 | 28123 | 0.327 | 0.186 | |\n",
    "| M = 0.01    |  46634 | 12909 | 0.289 | 0.006 | |\n",
    "| M = 0.1     |   5731 |  1181 | 0.288 | 0.000 | |\n",
    "| M = 0       | 171520 | 52387 | 0.381 | 0.635 | |\n",
    "| M = 0.00001 | 170682 | 52387 | 0.374 | 0.637 | |\n",
    "| M = 0.0001  | 145771 | 52387 | 0.378 | 0.665 | |\n",
    "| M = 0.0002  | 137571 | 52387 | 0.395 | 0.860 | |\n",
    "| M = 0.0003  | 127440 | 52387 | 0.358 | 0.764 | |\n",
    "| M = 0.0004  | 119718 | 52387 | 0.345 | 0.769 | |\n",
    "| M = 0.001   |  99965 | 52387 | 0.324 | 0.193 | |\n",
    "| M = 0.01    |  46634 | 52387 | 0.290 | 0.007 | |\n",
    "| M = 0.1     |   5731 | 52387 | 0.288 | 0.000 | |\n",
    "| M = 0       | 171520 | 49477 | 0.393 | 0.736 | +model |\n",
    "| M = 0.0002  | 137571 | 36390 | 0.385 | 0.841 | +model |\n",
    "| M = 0.0002  | 137571 | 52387 | 0.380 | 0.734 | +model |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_predicted_dtd = make_data_predicted(predictionLabelsTestDTD, tweet_ids_testing, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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": [
    "plot_data_predicted_dt = convert(data_predicted_dt)\n",
    "plot_data_predicted_tt = convert(data_predicted_tt)\n",
    "plot_data_predicted_et = convert(data_predicted_et)\n",
    "plot_data_predicted_dt_inflated = convert(data_predicted_dt_inflated)\n",
    "plot_data_predicted_dtd = convert(data_predicted_dtd)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_dt = [x[0] for x in plot_data_predicted_dt]\n",
    "y_dt = [x[1] for x in plot_data_predicted_dt]\n",
    "x_tt = [x[0] for x in plot_data_predicted_tt]\n",
    "y_tt = [x[1] for x in plot_data_predicted_tt]\n",
    "x_et = [x[0] for x in plot_data_predicted_et]\n",
    "y_et = [x[1] for x in plot_data_predicted_et]\n",
    "x_dt_inflated = [x[0] for x in plot_data_predicted_dt_inflated]\n",
    "y_dt_inflated = [x[1] for x in plot_data_predicted_dt_inflated]\n",
    "x_dtd = [x[0] for x in plot_data_predicted_dtd]\n",
    "y_dtd = [x[1] for x in plot_data_predicted_dtd]\n",
    "\n",
    "plt.plot(x_dt, y_dt, label=\"predicted (out-of-domain)\", c=\"C0\")\n",
    "plt.plot(x_tt, y_tt, label=\"predicted (in-domain)\", c=\"C1\")\n",
    "plt.plot(x_gold, y_gold, label=\"gold\", c=\"C2\")\n",
    "#plt.plot(x_et, y_et, label=\"predicted (added data)\", c=\"C3\")\n",
    "#plt.plot(x_dt_inflated, y_dt_inflated, label=\"predicted (out-of-domain, inflated)\", c=\"C4\")\n",
    "plt.plot(x_dtd, y_dtd, label=\"predicted (out-of-domain, no domain words)\", c=\"C6\")\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"predicting COVID-19 testing with social distancing data\")\n",
    "plt.savefig(\"distancing-to-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and testing on only relevant data (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4364"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fasttext_data_list_distance_relevant = [ line for line in fasttext_data_list_distance if not re.search(\"^__label__IRRELEVANT\", line) ]\n",
    "len(fasttext_data_list_distance_relevant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.604\n",
      "1 cases: 118; precision: 0.585; recall: 0.585; fraction: 1.217\n",
      "2 cases: 118; precision: 0.636; recall: 0.636; fraction: 1.166\n",
      "3 cases: 118; precision: 0.593; recall: 0.593; fraction: 0.697\n",
      "4 cases: 118; precision: 0.610; recall: 0.610; fraction: 1.067\n",
      "5 cases: 119; precision: 0.580; recall: 0.580; fraction: 0.549\n",
      "6 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.441\n",
      "7 cases: 118; precision: 0.695; recall: 0.695; fraction: 0.588\n",
      "8 cases: 118; precision: 0.525; recall: 0.525; fraction: 2.698\n",
      "9 cases: 118; precision: 0.576; recall: 0.576; fraction: 0.549\n",
      " col_0                __label__IRRELEVANT  __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                                        \n",
      "__label__IRRELEVANT                  442                50                139\n",
      "__label__REJECTS                      85                95                 30\n",
      "__label__SUPPORTS                    152                22                166\n",
      "cases: 1181; precision: 0.595; recall: 0.595; fraction: 0.807\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    predictionCountsValidationT, predictionLabelsValidationT, predictionCountsTestT, predictionLabelsTestT = \\\n",
    "        runFasttext(fasttext_data_list_testing, fasttext_data_list_testing, squealFlag=False, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    store_fasttext_data(predictionLabelsTestT, \"predictionLabelsTestT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestT = read_fasttext_data(\"predictionLabelsTestT.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "fasttext_data_list_testing_relevant = []\n",
    "i = 0\n",
    "for fold in predictionLabelsTestT:\n",
    "    for prediction_order_list in fold[0]:\n",
    "        if prediction_order_list[0] != \"__label__IRRELEVANT\":\n",
    "            tweet = re.sub(\"^__label__IRRELEVANT\", \"__label__SUPPORTS\", fasttext_data_list_testing[i])\n",
    "            fasttext_data_list_testing_relevant.append(tweet)\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4364, 502)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fasttext_data_list_distance_relevant), len(fasttext_data_list_testing_relevant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 cases: 50; precision: 0.660; recall: 0.660; fraction: 2.144\n",
      "1 cases: 50; precision: 0.800; recall: 0.800; fraction: 0.826\n",
      "2 cases: 51; precision: 0.725; recall: 0.725; fraction: 0.779\n",
      "3 cases: 50; precision: 0.820; recall: 0.820; fraction: 1.092\n",
      "4 cases: 50; precision: 0.660; recall: 0.660; fraction: 2.452\n",
      "5 cases: 50; precision: 0.840; recall: 0.840; fraction: 1.568\n",
      "6 cases: 50; precision: 0.760; recall: 0.760; fraction: 1.185\n",
      "7 cases: 51; precision: 0.627; recall: 0.627; fraction: 0.664\n",
      "8 cases: 50; precision: 0.740; recall: 0.740; fraction: 1.631\n",
      "9 cases: 50; precision: 0.580; recall: 0.580; fraction: 6.000\n",
      " col_0              __label__REJECTS  __label__SUPPORTS\n",
      "row_0                                                 \n",
      "__label__REJECTS                 71                 54\n",
      "__label__SUPPORTS                86                291\n",
      "cases: 502; precision: 0.721; recall: 0.721; fraction: 1.372\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    predictionCountsValidationDTR, predictionLabelsValidationDTR, predictionCountsTestDTR, predictionLabelsTestDTR = \\\n",
    "        runFasttext(fasttext_data_list_distance_relevant, fasttext_data_list_testing_relevant, squealFlag=False, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    store_fasttext_data(predictionLabelsTestDTR, \"predictionLabelsTestDTR.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionLabelsTestDTR = read_fasttext_data(\"predictionLabelsTestDTR.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1181"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictionLabelsTestDTRrestored = []\n",
    "fold_count = 0\n",
    "for fold in predictionLabelsTestT:\n",
    "    predictionLabelsTestDTRrestored.append(([[]]))\n",
    "    tweet_count = 0\n",
    "    for prediction_order_list in fold[0]:\n",
    "        if prediction_order_list[0] == \"__label__IRRELEVANT\":\n",
    "            predictionLabelsTestDTRrestored[-1][0].append(prediction_order_list)\n",
    "        else:\n",
    "            if tweet_count >= len(predictionLabelsTestDTR[fold_count][0]):\n",
    "                break\n",
    "            predictionLabelsTestDTRrestored[-1][0].append(predictionLabelsTestDTR[fold_count][0][tweet_count])\n",
    "            tweet_count += 1\n",
    "    for i in range(len(predictionLabelsTestDTRrestored[-1][0]), len(fold[0])):\n",
    "        predictionLabelsTestDTRrestored[-1][0].append(fold[0][i])\n",
    "    fold_count += 1\n",
    "sum([len(fold[0]) for fold in predictionLabelsTestDTRrestored])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_predicted_dtr = make_data_predicted(predictionLabelsTestDTRrestored, tweet_ids_testing, time_factor=time_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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": [
    "plot_data_predicted_dt = convert(data_predicted_dt)\n",
    "plot_data_predicted_tt = convert(data_predicted_tt)\n",
    "plot_data_predicted_et = convert(data_predicted_et)\n",
    "plot_data_predicted_dt_inflated = convert(data_predicted_dt_inflated)\n",
    "plot_data_predicted_dtd = convert(data_predicted_dtd)\n",
    "plot_data_predicted_dtr = convert(data_predicted_dtr)\n",
    "plot_data_gold = convert(data_gold)\n",
    "\n",
    "x_gold = [x[0] for x in plot_data_gold]\n",
    "y_gold = [x[1] for x in plot_data_gold]\n",
    "x_dt = [x[0] for x in plot_data_predicted_dt]\n",
    "y_dt = [x[1] for x in plot_data_predicted_dt]\n",
    "x_tt = [x[0] for x in plot_data_predicted_tt]\n",
    "y_tt = [x[1] for x in plot_data_predicted_tt]\n",
    "x_et = [x[0] for x in plot_data_predicted_et]\n",
    "y_et = [x[1] for x in plot_data_predicted_et]\n",
    "x_dt_inflated = [x[0] for x in plot_data_predicted_dt_inflated]\n",
    "y_dt_inflated = [x[1] for x in plot_data_predicted_dt_inflated]\n",
    "x_dtd = [x[0] for x in plot_data_predicted_dtd]\n",
    "y_dtd = [x[1] for x in plot_data_predicted_dtd]\n",
    "x_dtr = [x[0] for x in plot_data_predicted_dtr]\n",
    "y_dtr = [x[1] for x in plot_data_predicted_dtr]\n",
    "\n",
    "plt.plot(x_gold, y_gold, label=\"gold\", c=\"C2\")\n",
    "#plt.plot(x_dt, y_dt, label=\"predicted (out-of-domain)\", c=\"C0\")\n",
    "#plt.plot(x_tt, y_tt, label=\"predicted (in-domain)\", c=\"C1\")\n",
    "#lt.plot(x_et, y_et, label=\"predicted (added data)\", c=\"C3\")\n",
    "#plt.plot(x_dt_inflated, y_dt_inflated, label=\"predicted (out-of-domain, inflated)\", c=\"C4\")\n",
    "plt.plot(x_dtd, y_dtd, label=\"predicted (no domain words)\", c=\"C6\")\n",
    "plt.plot(x_dtr, y_dtr, label=\"predicted (relevant data)\", c=\"C7\")\n",
    "\n",
    "plt.ylim(0.3, 1.0)\n",
    "plt.legend()\n",
    "plt.title(\"predicting COVID-19 testing with social distancing data\")\n",
    "plt.savefig(\"distancing-to-testing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical analysis of predictions (old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_data_predicted_dt_dict = { x[0]:x[1] for x in plot_data_predicted_dt }\n",
    "plot_data_predicted_tt_dict = { x[0]:x[1] for x in plot_data_predicted_tt }\n",
    "plot_data_predicted_dt_inflated_dict = { x[0]:x[1] for x in plot_data_predicted_dt_inflated }\n",
    "plot_data_predicted_dtd_dict = { x[0]:x[1] for x in plot_data_predicted_dtd }\n",
    "plot_data_predicted_dtr_dict = { x[0]:x[1] for x in plot_data_predicted_dtr }\n",
    "plot_data_predicted_et_dict = { x[0]:x[1] for x in plot_data_predicted_et }\n",
    "plot_data_gold_dict = { x[0]:x[1] for x in plot_data_gold }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson r correlation gold vs in-domain: 0.93\n",
      "Pearson r correlation gold vs out-of-domain: 0.72\n",
      "Pearson r correlation gold vs out-of-domain (inflated): 0.73\n",
      "Pearson r correlation gold vs out-of-domain (no domain data): 0.59\n",
      "Pearson r correlation gold vs out-of-domain (relevant data): 0.71\n",
      "Pearson r correlation gold vs out-of-domain (extra data): 0.87\n"
     ]
    }
   ],
   "source": [
    "values_tt, values_gold_tt = get_common_key_values(plot_data_predicted_tt_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs in-domain:\", round(scipy.stats.linregress(np.array(values_gold_tt), np.array(values_tt)).rvalue, 2))\n",
    "values_dt, values_gold_dt = get_common_key_values(plot_data_predicted_dt_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain:\", round(scipy.stats.linregress(np.array(values_gold_dt), np.array(values_dt)).rvalue, 2))\n",
    "values_dt_inflated, values_gold_dt_inflated = get_common_key_values(plot_data_predicted_dt_inflated_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (inflated):\", round(scipy.stats.linregress(np.array(values_gold_dt_inflated), np.array(values_dt_inflated)).rvalue, 2))\n",
    "values_dtd, values_gold_dtd = get_common_key_values(plot_data_predicted_dtd_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (no domain data):\", round(scipy.stats.linregress(np.array(values_gold_dtd), np.array(values_dtd)).rvalue, 2))\n",
    "values_dtr, values_gold_dtr = get_common_key_values(plot_data_predicted_dtr_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (relevant data):\", round(scipy.stats.linregress(np.array(values_gold_dtr), np.array(values_dtr)).rvalue, 2))\n",
    "values_et, values_gold_et = get_common_key_values(plot_data_predicted_et_dict, plot_data_gold_dict)\n",
    "print(\"Pearson r correlation gold vs out-of-domain (extra data):\", round(scipy.stats.linregress(np.array(values_gold_et), np.array(values_et)).rvalue, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absolute difference gold vs in-domain: 0.07\n",
      "absolute difference gold vs out-of-domain: 0.18\n",
      "absolute difference gold vs out-of-domain (inflated): 0.12\n",
      "absolute difference gold vs out-of-domain (no domain data): 0.16\n",
      "absolute difference gold vs out-of-domain (relevant data): 0.15\n",
      "absolute difference gold vs out-of-domain (extra data): 0.08\n"
     ]
    }
   ],
   "source": [
    "print(\"absolute difference gold vs in-domain:\", round(absolute_difference(values_gold_tt, values_tt), 2))\n",
    "print(\"absolute difference gold vs out-of-domain:\", round(absolute_difference(values_gold_dt, values_dt),2)) \n",
    "print(\"absolute difference gold vs out-of-domain (inflated):\", round(absolute_difference(values_gold_dt_inflated, values_dt_inflated),2))\n",
    "print(\"absolute difference gold vs out-of-domain (no domain data):\", round(absolute_difference(values_gold_dtd, values_dtd),2))\n",
    "print(\"absolute difference gold vs out-of-domain (relevant data):\", round(absolute_difference(values_gold_dtr, values_dtr),2))\n",
    "print(\"absolute difference gold vs out-of-domain (extra data):\", round(absolute_difference(values_gold_et, values_et),2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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