ieee.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IEEE eScience paper\n",
    "\n",
    "Compare stances over time derived from tweets with stances from other sources, for example RIVM behavioral studies.\n",
    "\n",
    "This notebook reuses code from the notebooks:\n",
    "* domain-adaptation.ipynb\n",
    "* fasttext.ipynb\n",
    "* tweet-counts.ipynb\n",
    "\n",
    "RIVM measures that were evaluated:\n",
    "* Naleving gedragsregels / Houden aan gedragsregels / Voldoende afstand houden van anderen\n",
    "* Naleving gedragsregels / Houden aan gedragsregels / Mondkapje in publieke binnenruimten\n",
    "* Naleving gedragsregels / Testen / Klachten totaal (from point 12, May 2021: testen met klachten)\n",
    "* Vaccinatiebereidheid / Totaal\n",
    "* Naleving gedragsregels / Houden aan gedragsregels / Avondklok niet naar buiten geweest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import TweetTokenizer\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data/\"\n",
    "ANNOTATION_DIR = DATA_DIR + \"annotation/\"\n",
    "CURFEW = \"curfew\"\n",
    "DISTANCE = \"distance\"\n",
    "DISTANCE1000 = \"distance1000\"\n",
    "FACEMASK = \"mondkapje\"\n",
    "TESTING = \"testing\"\n",
    "VACCINATION = \"vaccin\"\n",
    "WORKFROMHOME = \"work-from-home\"\n",
    "TOPICS = [ CURFEW, DISTANCE, FACEMASK, TESTING, VACCINATION, WORKFROMHOME ]\n",
    "IDSTR = \"id_str\"\n",
    "IRRELEVANT = \"ANDERS\"\n",
    "TOTAL = \"total\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(text):\n",
    "    return(\" \".join(TweetTokenizer().tokenize(text)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(text):\n",
    "    return(tokenize(cleanup(text)).lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_annotations(annotations_in, tweets, main_annotator):\n",
    "    annotations_out = {}\n",
    "    for i, row in annotations_in.iterrows():\n",
    "        if row[0]== main_annotator:\n",
    "            id_str = row[2]\n",
    "            label = row[4]\n",
    "            if id_str in tweets.index:\n",
    "                annotations_out[id_str] = { \"label\": label, \"text\": preprocess(tweets.loc[id_str][\"text\"]) }\n",
    "    return annotations_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data_topic(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 + \"-202001-202105.csv\"\n",
    "    elif topic == CURFEW:\n",
    "        tweet_file = ANNOTATION_DIR + topic + \"-202003-202105.csv\"\n",
    "    elif topic == WORKFROMHOME:\n",
    "        tweet_file = ANNOTATION_DIR + topic + \"-202003-202105.csv\"\n",
    "    elif topic == DISTANCE:\n",
    "        tweet_file = DATA_DIR + topic + \"-tweets.csv\"\n",
    "    else:\n",
    "        print(f\"read_fasttext_data: unknown topic: {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",
    "    annotations = select_annotations(annotations, tweets, main_annotator)\n",
    "    return annotations, main_annotator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_annotations(annotations, topic):\n",
    "    pd.DataFrame([[key, annotations[key][\"label\"]] for key in annotations], columns=[\"id_str\", \"label\"]).to_csv(f\"data-{topic}.csv\", \n",
    "                                                                                                                index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_all_data(topics):\n",
    "    annotations = {}\n",
    "    for topic in topics:\n",
    "        if topic != DISTANCE1000:\n",
    "            annotations[topic], main_annotator = read_data_topic(topic)\n",
    "            print(f\"read topic {topic}: {len(annotations[topic])} annotations from {main_annotator}\")\n",
    "    return annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read topic curfew: 750 annotations from erikt@xs4all.nl\n",
      "read topic distance: 5977 annotations from erikt@xs4all.nl\n",
      "read topic mondkapje: 1011 annotations from erikt@xs4all.nl\n",
      "read topic testing: 1181 annotations from erikt@xs4all.nl\n",
      "read topic vaccin: 1340 annotations from erikt@xs4all.nl\n",
      "read topic work-from-home: 1003 annotations from erikt@xs4all.nl\n",
      "read topic distance: 5977 annotations from erikt@xs4all.nl\n"
     ]
    }
   ],
   "source": [
    "annotations = read_all_data(TOPICS+[DISTANCE])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1196"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotations[DISTANCE1000] = {}\n",
    "counter = 0\n",
    "for key in annotations[DISTANCE]:\n",
    "    if counter % 5 == 0:\n",
    "        annotations[DISTANCE1000][key] = annotations[DISTANCE][key]\n",
    "    counter += 1\n",
    "len(annotations[DISTANCE1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'curfew': 16,\n",
       " 'distance': 74,\n",
       " 'mondkapje': 47,\n",
       " 'testing': 62,\n",
       " 'vaccin': 29,\n",
       " 'work-from-home': 87,\n",
       " 'distance1000': 72}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotation_averages = {}\n",
    "\n",
    "for label in annotations:\n",
    "    counts = { \"EENS\": 0, \"ONEENS\": 0 }\n",
    "    for key in annotations[label]:\n",
    "        if annotations[label][key][\"label\"] != \"ANDERS\":\n",
    "            counts[annotations[label][key][\"label\"]] += 1\n",
    "    annotation_averages[label] =  int(0.5 + 100 * counts[\"EENS\"] / (counts[\"EENS\"] + counts[\"ONEENS\"]))\n",
    "\n",
    "annotation_averages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Run fastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fasttext\n",
    "import json\n",
    "import os\n",
    "import sys\n",
    "import tempfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEXT = \"text\"\n",
    "TEXT_DIR = DATA_DIR + \"text/\"\n",
    "CURFEW_QUERY = \"avondklok\"\n",
    "DISTANCE_QUERY = \"1[.,]5[ -]*m|afstand.*hou|hou.*afstand|anderhalve[ -]*meter\"\n",
    "FACEMASK_QUERY = \"mondkapje\"\n",
    "TEST_QUERY = r'\\btest|getest|sneltest|pcr'\n",
    "VACCIN_QUERY = \"vaccin|ingeënt|ingeent|inent|prik|spuit|bijwerking|-->|💉|pfizer|moderna|astrazeneca|astra|zeneca|novavax|biontech\"\n",
    "WORKFROMHOME_QUERY = \"(thuiswerk|thuis.werk|werk.thuis)\"\n",
    "RIVM_ROUNDS_ORG = [ \"^2020041[7-9]|^2020042[0-4]\", \"^2020050[7-9]|^2020051[0-2]\", \"^2020052[7-9]|^2020053|^20200601\", \"^2020061[7-9]|^2020062[0-1]\",\n",
    "                    \"^2020070[8-9]|^2020071[0-2]\", \"^20200819|^2020082[0-3]\",     \"^20200930|^2020100[1-4]\",          \"^2020111[1-5]\",\n",
    "                    \"^2020123|^2021010[1-3]\",      \"^2021021[0-4]\",               \"^2021032[4-8]\",                    \"^2021050[5-9]\" ]\n",
    "RIVM_MONTHS_ORG = [ \"Apr\", \"May\", \"\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Nov\", \"Jan\", \"Feb\", \"Mar\", \"May\" ]\n",
    "RIVM_COUNTS_ORG = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ]\n",
    "RIVM_ROUNDS = [ \"^2021061[6-9]|^20210620\", \"^2021072[89]|^2021073|^20210801\", \"^2021090[89]|2021091[0-2]\", \"^2021102[0-4]\", \"^2021112[4-8]\", ]\n",
    "RIVM_MONTHS = [ \"Jun\", \"Jul\", \"Sep\", \"Oct\", \"Nov\", ]\n",
    "RIVM_COUNTS = [ 13, 14, 15, 16, 17, ]\n",
    "QUERIES = { CURFEW: CURFEW_QUERY, DISTANCE: DISTANCE_QUERY, DISTANCE1000: DISTANCE_QUERY, FACEMASK: FACEMASK_QUERY, \n",
    "            TESTING: TEST_QUERY, VACCINATION: VACCIN_QUERY, WORKFROMHOME: WORKFROMHOME_QUERY }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data_fasttext(date_pattern, query, data_directory=TEXT_DIR):\n",
    "    file_list = sorted(os.listdir(data_directory))\n",
    "    test_data = []\n",
    "    hours = 0\n",
    "    for in_file_name in file_list:\n",
    "        if re.search(date_pattern, in_file_name) and os.path.exists(data_directory + in_file_name):\n",
    "            try:\n",
    "                file_data = pd.read_csv(data_directory + in_file_name).drop_duplicates()\n",
    "                matched_text = file_data[file_data[TEXT].str.contains(query, case=False)]\n",
    "                matched_text_preprocessed = matched_text[TEXT].apply(lambda x: preprocess(x))\n",
    "                test_data.extend(list(matched_text_preprocessed))\n",
    "                hours += 1\n",
    "            except:\n",
    "                pass\n",
    "    return test_data, hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(date_pattern, query, model, data_directory=TEXT_DIR):\n",
    "    test_data, hours = read_data_fasttext(date_pattern, query, data_directory)\n",
    "    if len(test_data) <= 0:\n",
    "        return {}\n",
    "    predicted_labels = model.predict(test_data)\n",
    "    predicted_groups = pd.DataFrame(predicted_labels[0]).groupby(0).groups\n",
    "    label_counts_predicted = { label: len(predicted_groups[label]) for label in predicted_groups}\n",
    "    nbr_of_labels = sum([ label_counts_predicted[label] for label in label_counts_predicted if label != IRRELEVANT ])\n",
    "    label_percentages = { label: round(100*label_counts_predicted[label] / nbr_of_labels, 1) for label in label_counts_predicted }\n",
    "    label_percentages[TOTAL] = nbr_of_labels\n",
    "    label_percentages[\"hours\"] = hours\n",
    "    return label_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_fasttext_model(annotations):\n",
    "    _, path = tempfile.mkstemp()\n",
    "    data_file = open(path, \"w\")\n",
    "    for id_str in annotations:\n",
    "        print(f'__label__{annotations[id_str][\"label\"]} {preprocess(annotations[id_str][\"text\"])}', \n",
    "              file=data_file)\n",
    "    data_file.close()\n",
    "    model = fasttext.train_supervised(path, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")\n",
    "    os.remove(path)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relevant_tweets_count(classifications, topic):\n",
    "    return sum([ classifications[topic][key][\"total\"]*(classifications[topic][key][\"__label__EENS\"] + \n",
    "                                                       classifications[topic][key][\"__label__ONEENS\"]) / 100\n",
    "                 for key in classifications[topic] ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_classifications(topics, classifications):\n",
    "    for topic in topics:\n",
    "        print(f\"processing topic: {topic}\")\n",
    "        model_file = f\"model-ieee-{topic}.bin\"\n",
    "        if os.path.isfile(model_file):\n",
    "            model = fasttext.load_model(model_file)\n",
    "        else:\n",
    "            model = make_fasttext_model(annotations[topic])\n",
    "            model.save_model(model_file)\n",
    "        classifications[topic] = {}\n",
    "        query = QUERIES[topic]\n",
    "        for date_pattern in RIVM_ROUNDS:\n",
    "            classifications[topic][date_pattern] = classify(date_pattern, query, model)\n",
    "            print(date_pattern)\n",
    "        write_classifications(\"ieee.json\", classifications)\n",
    "        #print(f\"number of relevant tweets used: {get_relevant_tweets_count(classifications, topic)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_classifications(file_name):\n",
    "    infile = open(file_name, \"r\")\n",
    "    lines = infile.readlines()\n",
    "    infile.close()\n",
    "    classifications = json.loads(\" \".join(lines))\n",
    "    return classifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_classifications(file_name, classifications):\n",
    "    outfile = open(file_name, \"w\")\n",
    "    outfile.write(json.dumps(classifications))\n",
    "    outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = \"ieee.json\"\n",
    "\n",
    "if os.path.isfile(file_name):\n",
    "    classifications = read_classifications(file_name)\n",
    "else:\n",
    "    classifications = {}\n",
    "    compute_classifications(TOPICS, classifications)\n",
    "    write_classifications(file_name, classifications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for topic in classifications:\n",
    "    for key in classifications[topic]:\n",
    "        if \"__label__EENS\" not in classifications[topic][key]:\n",
    "            classifications[topic][key][\"__label__EENS\"] = 0\n",
    "        if \"__label__ONEENS\" not in classifications[topic][key]:\n",
    "            classifications[topic][key][\"__label__ONEENS\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    classifications = {}\n",
    "    compute_classifications(TOPICS, classifications)\n",
    "    write_classifications(file_name, classifications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(classifications[\"distance\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot\n",
    "\n",
    "Current RIVNM results: https://www.rivm.nl/gedragsonderzoek/maatregelen-welbevinden/\n",
    "Historical results: https://www.rivm.nl/gedragsonderzoek/maatregelen-welbevinden/resultaten-13e-ronde-gedragsonderzoek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = { CURFEW: CURFEW, DISTANCE: \"social distancing\", FACEMASK: \"wearing face masks\", TESTING: TESTING, \n",
    "           VACCINATION: \"vaccination\", WORKFROMHOME: \"working from home\", DISTANCE1000: \"social distancing\" }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rivm = {}\n",
    "rivm[CURFEW] = { 10: 0.932, 11: 0.900 }\n",
    "rivm[DISTANCE] = { 1: 0.707, 2: 0.663, 3: 0.625, 4: 0.608, 5: 0.587, 6: 0.616, 7: 0.638, 8: 0.665, 9: 0.667, 10: 0.678, 11: 0.658, 12: 0.658, \n",
    "                  13: 0.640, 14: 0.635, 15: 0.593, 16: 0.576, 17: 0.628, }\n",
    "rivm[DISTANCE1000] = dict(rivm[DISTANCE])\n",
    "rivm[FACEMASK] = { 8: 0.767, 9: 0.931, 10: 0.916, 11: 0.905, 12: 0.904, 13: 0.837,  }\n",
    "rivm[TESTING] = { 6: 0.319, 7: 0.318, 8: 0.450, 9: 0.527, 10: 0.370, 11: 0.454, 12: 0.493, # measurement criteria change at point 12\n",
    "                 13: 0.466, 14: 0.408, 15: 0.346, 16: 0.289, 17: 0.348, }\n",
    "rivm[VACCINATION] = { 3: 0.742, 5: 0.650, 8: 0.583, 9: 0.803, 10: 0.859, 11: 0.876, 12: 0.915, 13: 0.952, 14: 0.958, 15: 0.955, 16: 0.956, 17: 0.963, }\n",
    "# rivm[WORKFROMHOME] = { 7: 0.724, 8: 0.767, 9: 0.803, 10: 0.824, 11: 0.784, 12: 0.768 } # old\n",
    "rivm[WORKFROMHOME] = { 7: 0.724, 8: 0.767, 9: 0.803, 10: 0.754, 11: 0.706, 12: 0.678, 13: 0.635, 14: 0.625, 15: 0.578, 16: 0.527, 17: 0.665, }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANNOTATION_MATRIX = {\n",
    "    CURFEW:       [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, ],\n",
    "    DISTANCE:     [ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n",
    "    DISTANCE1000: [ 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n",
    "    FACEMASK:     [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, ],\n",
    "    TESTING:      [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ],\n",
    "    VACCINATION:  [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, ],\n",
    "    WORKFROMHOME: [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, ]\n",
    "}                 # 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_offsets(classifications, annotation_averages):\n",
    "    offsets = {}\n",
    "    for topic in TOPICS:\n",
    "        offset = 0\n",
    "        counter = -1\n",
    "        for key in classifications[topic]:\n",
    "            counter += 1\n",
    "            if ANNOTATION_MATRIX[topic][counter] == 0:\n",
    "                continue\n",
    "            support_key = ( 100 * classifications[topic][key][\"__label__EENS\"] / \n",
    "                           ( classifications[topic][key][\"__label__EENS\"] + classifications[topic][key][\"__label__ONEENS\"] ) )\n",
    "            offset += annotation_averages[topic] - support_key\n",
    "        offsets[topic] = round(offset / len([x for x in ANNOTATION_MATRIX[topic] if x == 1]))\n",
    "    return offsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_x_y(classifications, offsets, topic):\n",
    "    if topic not in rivm:\n",
    "        print(f\"make_x_y: unknown topic {topic}!\")\n",
    "        return\n",
    "    x = range(1, 1+len(classifications[topic]))\n",
    "    for date_pattern in classifications[topic]:\n",
    "        if \"__label__EENS\" not in classifications[topic][date_pattern]:\n",
    "            classifications[topic][date_pattern][\"__label__EENS\"] = 0.0\n",
    "        if \"__label__ONEENS\" not in classifications[topic][date_pattern]:\n",
    "            classifications[topic][date_pattern][\"__label__ONEENS\"] = 0.0\n",
    "    y = [ offsets[topic] + 100*classifications[topic][date_pattern][\"__label__EENS\"] / (classifications[topic][date_pattern][\"__label__EENS\"] + \n",
    "                                                                                        classifications[topic][date_pattern][\"__label__ONEENS\"] ) \n",
    "          for date_pattern in classifications[topic]]\n",
    "    x_rivm = [ key for key in rivm[topic] ]\n",
    "    y_rivm = [ 100*rivm[topic][key] for key in rivm[topic]]\n",
    "    return x, y, x_rivm, y_rivm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single(x, y, x_rivm, y_rivm, topic):\n",
    "    plt.ylim(0, 100)\n",
    "    plt.plot(x, y, label=\"tweets\")\n",
    "    plt.plot(x_rivm, y_rivm, label=\"questionnaires\")\n",
    "    plt.plot(x, [ annotation_averages[topic] for i in x ], \"--\", color=\"grey\", label=\"annotation average\")\n",
    "    plt.legend()\n",
    "    plt.title(labels[topic])\n",
    "    plt.xticks(x, RIVM_MONTHS_ORG + RIVM_MONTHS)\n",
    "    plt.xlabel(\"months in 2020 and 2021\")\n",
    "    plt.ylabel(\"support (%)\")\n",
    "    plt.savefig(f\"ieee-{topic}.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "offsets = compute_offsets(classifications, annotation_averages)\n",
    "#offsets = { key: 0.0 for key in classifications }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABKEklEQVR4nO3dd3gVZfbA8e9JgQAJNbQAUqSXJIQiSFVRULpiQUSw113LWnBt+NO1LauIXVeFVRRQQQELSAcBpSX03nuHQIC08/tjJtdLSEhIcnOTcD7Pc5/cO+WdM5fhnpl3Zs6IqmKMMcYABPg7AGOMMQWHJQVjjDEelhSMMcZ4WFIwxhjjYUnBGGOMhyUFY4wxHpYUTJEmIpeIyAkRCfRB2yVEZJKIHBORb/O6fX8SkaEi8pW/4zD5L8jfARjjS6q6HQj1UfP9gMpABVVN9tEyjMlXdqRgiiwR8fVOT01gvSUEU5RYUjD5QkTuEJFJXp83eHe5iMgOEYl23zcUkd9E5LCIrBORm7ym6y4iy0TkuDvPUK9xtUREReQuEdkOzPAaFuROM0tEXhaR30UkXkSmiki4Vxu3i8g2ETkkIs+LyFYR6ZLB+rwEvADc7HZP3SUil4rIDHfegyIyWkTKes1TQ0TGi8gBd5r3vMbdKSJrROSIiEwRkZqZfI9p63OHu/5HROR+EWklIstF5Gi6drOK6WkR2eV+F+tE5KoMlhksIt+IyPciUkxEWovIYvffYJ+IvJVRrKaQUlV72cvnL6AOcBRnRyQC2Abs9Bp3xB1XCtgB3IHTvdkcOAg0dqftDDRzp40E9gF93HG1AAX+57ZTwmtYkDvNLGATUN8dPwt43R3XGDgBtAeKAcOAJKBLJus0FPjK63Nd4GqgOFARmAMMd8cFAnHA225sIUB7d1xvYCPQyF3n54D5mSwzbX0+ctu4BjgN/ABUAqoB+4FO2YipgftdR3i1fan3urnf0U/ASCDQHbcAGOi+DwXa+Hv7slfevexIweQLVd0MxAPRQEdgCrBbRBoCnYC5qpoK9AC2quoXqpqsqsuA74Eb3XZmqeoKVU1V1eXAN+783oaq6klVPZVJOF+o6np3/Dg3JnDOEUxS1XmqmohzJJDt4mCqulFVf1PVM6p6AHjLK7bWOMnwSTe206o6zx13P/Caqq5RpyvqVSA6s6MF18tuG1OBk8A3qrpfVXcBc3GSaVYxpeAki8YiEqyqW1V1k9cySgO/4iTRO1Q1xR2eBNQVkXBVPaGqC7P7HZmCz5KCyU+zcfb0O7rvZ+H8QHVyP4PTT3+Z2w1yVESOAgOAKgAicpmIzHS7YI7h/KCGc7YdWcSx1+t9An+diI7wnldVE4BD2V05EaksImPc7pjjOHvaabHVALZpxucfagLveK3vYUBw9vozs8/r/akMPodmFZOqbgQexTkq2O9OF+HVThuco7HXVdU7Od6Fc6S1VkQWiUiP88RpChlLCiY/pSWFDu772ZybFHYAs1W1rNcrVFUfcMd/DUwEaqhqGZxuFEm3nJyW/t0DVE/7ICIlgAoXMP+r7rKbqWpp4Dav2HYAl2Ry8nsHcF+6dS6hqvNztBbZjwlV/VpV2+MkJgXe8Jp3KvAaMF1EKnvNs0FV++N0V70BfCcipfIgVlMAWFIw+Wk2cAVQQlV34nRzdMP54V3mTjMZqC8iA90TnMHuSdRG7vgw4LCqnhaR1sCteRjfd0BPEblcRIrh7EGnTzjnE4ZzTuKYiFQDnvQa9ydO0nldREqJSIiItHPHfQQ8IyJNAESkjIjcmMt1yTImEWkgIleKSHGc8xKngFTvmVX1TZxEPD3thLyI3CYiFd3uvqPupGfNZwovSwom36jqepwfqLnu5+PAZuD3tP5qVY3HOXl6C7Abp6vnDZy+b4AHgf8TkXicPv9xeRjfKuBvwBicH/ATOCdtz2SziZeAGOAYzsnZ8V5tpwA9cU78bgd2Aje74ybgrOMYt4tnJXBt7tfo/DHhfKev45zI34uz5/9M+gZU9WWcE9nTRKQ8TiJfJSIngHeAW85z/sYUMnJ2V6ExJo2IhOLsCddT1S1+DseYfGFHCsZ4EZGeIlLS7SMfBqwAtvo3KmPyj8+Sgoh8LiL7RWSl17Dy4tyUtMH9W84dLiIyQkQ2ujfgxPgqLmOy0Bun22o3UA+na8QOp81Fw5dHCiNx+h69DQGmq2o9YLr7GZz+03ru617gQx/GZUymVPVu9+qfMqp6laqu83dMxuQnnyUFVZ2Dc721t97AKPf9KKCP1/D/qWMhUFZEqvoqNmOMMRnL7yqplVV1j/t+L06FSXBu0vG+4WinO2wP6YjIvThHE5QqVapFw4YNfRetMcYUQUuWLDmoqhUzGue30tmqqiJywX21qvoJ8AlAy5YtdfHixXkemzHGFGUisi2zcfl99dG+tG4h9+9+d/gunDIAaaq7w4wxxuSj/E4KE4FB7vtBwI9ew293r0JqAxzz6mYyxhiTT3zWfSQi3+DUuQkXkZ3Aizh3T44TkbtwSien1cn/GbgOp3xwAk7ZZGOMMfnMZ0nBLZiVkXMe4uFeB/5QXiw3KSmJnTt3cvr06bxozhQBISEhVK9eneDgYH+HYkyBV+Se0bxz507CwsKoVasWIhdSy8wURarKoUOH2LlzJ7Vr1/Z3OMYUeEWuzMXp06epUKGCJQQDgIhQoUIFO3I0JpuKXFIALCGYs9j2YEz2FcmkYIwxJmcsKeSxo0eP8sEHH/is/eHDh5OQkOCz9o0xFzdLCnnMkoIxpjCzpJDHhgwZwqZNm4iOjuaOO+5g4sSJAPTt25c777wTgM8//5xnn30WgK+++orWrVsTHR3NfffdR0pKCgBTp06lbdu2xMTEcOONN3LixAlGjBjB7t27ueKKK7jiiitISUlh8ODBNG3alGbNmvH222/7Z6WNMUVGkbsk1dtLk1axevfxPG2zcURpXuzZJNPxr7/+OitXriQ2NpYxY8Ywd+5cevXqxa5du9izx7lJe+7cudxyyy2sWbOGsWPH8vvvvxMcHMyDDz7I6NGjue6663jllVeYNm0apUqV4o033uCtt97ihRde4K233mLmzJmEh4ezZMkSdu3axcqVziMrjh49mqfraoy5+BTppOBvHTp0YPjw4axevZrGjRtz5MgR9uzZw4IFCxgxYgSjRo1iyZIltGrVCoBTp05RqVIlFi5cyOrVq2nXznmue2JiIm3btj2n/Tp16rB582b+9re/0b17d6655pp8XT+TiZRkWDsZSkdAxYYQUtrfERmTbUU6KZxvjz4/VKtWjaNHj/Lrr7/SsWNHDh8+zLhx4wgNDSUsLAxVZdCgQbz22mtnzTdp0iSuvvpqvvnmm/O2X65cOeLi4pgyZQofffQR48aN4/PPP/flKpns+ONDmPrcX5/L1IBKjZwEUamx+74BBJfwX4zGZKJIJwV/CAsLIz4+3vO5TZs2DB8+nBkzZnDo0CH69etHv379ALjqqqvo3bs3jz32GJUqVeLw4cPEx8fTpk0bHnroITZu3EjdunU5efIku3bton79+p72w8PDOXjwIMWKFeOGG26gQYMG3Hbbbf5abZPm9DGY+x+o3QnaPAD7V8P+tbB/DWyeBSmJznQSAOVqOwkiLVFUagQV6kKgleMw/mNJIY9VqFCBdu3a0bRpU6699lo6dOjA1KlTqVu3LjVr1uTw4cN06NABgMaNG/PKK69wzTXXkJqaSnBwMO+//z5t2rRh5MiR9O/fnzNnzgDwyiuvUL9+fe699166detGREQEw4cP54477iA1NRXgnCMO4we/vwOnjsA1L0PVKGhw7V/jUpLh8GY3Uaz56++6n0Gdf0MCgiG83l9JolJj5wij7CWWLEy+kML8TPKMHrKzZs0aGjVq5KeITEGVL9tF/F4Y0dxJBP0uoBsv6TQc2nB2oti/Go5u/2saCYCwCChX00kQ6V+lq0Og7eOZ7BGRJaraMqNxthUZk1dmv+l0D13x7IXNFxwCVZo5L29n4uHAOjiw1kkQR7Y5f7fMheO7AK8dOgmE0tXOTRZpSSQswpKGyRbbSozJC4c2wdJREDMIKlyaN20WD4PqLZ1XesmJTmI46iYK79fmWRC/h3OSRplqzhFFUHEICHSGBQQ6RyFpfz3DAiEg/ed0w4uFQovBEJrho35NIWVJwZi8MPNfEFgMOj2VP8sLKgblazuvjCSfgWM7vZKFmzyO74bEE5CaApoCqanu3xTnvIb3e880GU2b4hwVLf4MbhwJl7TJn/U2PmdJwZjc2hMHK7+HDv+AsCr+jsYRVNw5Ysmro5aM7F0B426HL66Dq/8P2j4EVpG20LMyF8bk1rSXoEQ5aPeIvyPJX1Wawb2zoOF1MPVZGHubc0muKdQsKRiTG1vmwKbp0P5xCCnj72jyX0gZuOlL6PoqrP8VPukMe5b7OyqTC5YUCqFXX331rM+XX355vi178eLF/P3vf8+35RVoqjBtqHPVT+t7/B2N/4g4XUeDf4KkU/DZ1bD0S39HZXLIkkIhlD4pzJ8/P9+W3bJlS0aMGHHO8OTk5HyLocBYMwl2LYHOQ6xkBTgnm++bCzUug4kPww8PQaKVeS9sLCn4yL/+9S/q169P+/bt6d+/P8OGDaNz586k3Wx38OBBatWqBUBKSgpPPvkkrVq1IjIyko8//hiAPXv20LFjR6Kjo2natClz585lyJAhnDp1iujoaAYMGABAaGgo4Dyk/sknn/SU0h47diwAs2bNonPnzvTr14+GDRsyYMAA0m5arFWrFi+++CIxMTE0a9aMtWvXAvDnn3/Stm1bmjdvzuWXX866des8bfXo0QOAoUOHMnDgQNq1a8fAgQM5cOAAN9xwA61ataJVq1b8/vvvAMyePZvo6Giio6Np3rz5WWVACq2UZJjxMoTXh6hb/R1NwRFaEQZOgI5PQexo56jh0CZ/R2UuQNG++uiXIc4VEnmpSjO49vXzTrJkyRLGjBlDbGwsycnJxMTE0KJFi0yn/+yzzyhTpgyLFi3izJkztGvXjmuuuYbx48fTtWtXnn32WVJSUkhISKBDhw689957xMbGntPO+PHjiY2NJS4ujoMHD9KqVSs6duwIwLJly1i1ahURERG0a9eO33//nfbt2wMQHh7O0qVL+eCDDxg2bBj//e9/adiwIXPnziUoKIhp06bxz3/+k++///6cZa5evZp58+ZRokQJbr31Vh577DHat2/P9u3b6dq1K2vWrGHYsGG8//77tGvXjhMnThASEnIBX3gBFfc1HFwPN39lN4WlFxAIVz7rHDGMvxs+7gR93ofGvf0dmckG25p9YO7cufTt25eSJUsC0KtXr/NOP3XqVJYvX853330HwLFjx9iwYQOtWrXizjvvJCkpiT59+hAdHX3edubNm0f//v0JDAykcuXKdOrUiUWLFlG6dGlat25N9erVAYiOjmbr1q2epHD99dcD0KJFC8aPH++JYdCgQWzYsAERISkpKcNl9urVixIlnK6TadOmsXr1as+448ePc+LECdq1a8fjjz/OgAEDuP766z1xFFpJp2DW61CtJTTs4e9oCq56XZzupG8HOZeutnkIrn7JajgVcEU7KWSxR5/fgoKCPMXrTp8+7Rmuqrz77rt07dr1nHnmzJnDTz/9xODBg3n88ce5/fbbc7Ts4sWLe94HBgaedQ4gbZz38Oeff54rrriCCRMmsHXrVjp37pxhu6VKlfK8T01NZeHCheccCQwZMoTu3bvz888/065dO6ZMmULDhg1ztB4Fwp+fOncT9/3YrsvPStkacMevTinxhe/DrsXOzW6lI/wdmcmEnVPwgY4dO/LDDz9w6tQp4uPjmTRpEuD03y9ZsgTAc1QA0LVrVz788EPP3vj69es5efIk27Zto3Llytxzzz3cfffdLF26FIDg4OAM99w7dOjA2LFjSUlJ4cCBA8yZM4fWrVvnaB2OHTtGtWrVABg5cmS25rnmmmt49913PZ/Turg2bdpEs2bNePrpp2nVqpXnvEWhdOqoUxr70qugdgd/R1M4BBWD6950igTuWwUfdYBNM/0dlcmEJQUfiImJ4eabbyYqKoprr73W82S1J554gg8//JDmzZtz8OBBz/R33303jRs3JiYmhqZNm3LfffeRnJzMrFmziIqKonnz5owdO5ZHHnFujrr33nuJjIz0nGhO07dvXyIjI4mKiuLKK6/kzTffpEqVnN1h+9RTT/HMM8/QvHnzbF9ZNGLECBYvXkxkZCSNGzfmo48+AmD48OE0bdqUyMhIgoODufbaa7NoqQCbPwJOH4UuL/o7ksKn6Q1wz0woVRG+7Auz3nBKZ5gCxUpn54OhQ4cSGhrKE0884e9QLlp5sl3E74V3op07eC+kNLY5W+JJmPwYLB/rHHFd/ymUquDvqC4q5yudbUcKxmTX7DchNenCS2ObsxUr5ZyP6TEcts6FjzvCjkX+jsq4ivaJ5gJi6NCh/g7B5FZaaewWg31bZO5iIQIt74CIaBg3CL641jkB3ciu5vI3O1IwJjvSSmN3zKfS2BeLiOZw32yIusW5r8H4nR0pGJOV3bFuaewnIKyyv6MpekqUg97v+TsK47IjBWOyMv3/3NLYVgjQFH2WFIw5n7TS2B3+cXGWxjYXHUsKhdDWrVv5+uuvL3g6K3t9gbxLY7e6iEtjm4uKX5KCiDwmIqtEZKWIfCMiISJSW0T+EJGNIjJWRIr5I7bCIKdJIbOy1wVFSkqKv0M4m6c09jMQXASK+BmTDfmeFESkGvB3oKWqNgUCgVuAN4C3VbUucAS4K79jyyt9+vShRYsWNGnShE8++cQzPDQ0lGeffZaoqCjatGnDvn37ABg8eDB///vfufzyy6lTp46nBEZmpbCHDBnC3LlziY6O5u2332br1q106NCBmJgYYmJiPM9XSD+dd9nrw4cP06dPHyIjI2nTpg3LlztPyxo6dCh33nknnTt3pk6dOpkmkQceeICWLVvSpEkTXnzRubv3119/5cYbb/RM4728qVOn0rZtW2JiYrjxxhs5ceIE4JT+ePrpp4mJieHbb7/l008/pVWrVkRFRXHDDTeQkODU49+0aRNt2rShWbNmPPfcc55y4QD//ve/PWXH02LJtZRk51xCeAOI6p83bRpTGKhqvr6AasAOoDzO1U+Tga7AQSDInaYtMCWrtlq0aKHprV69+qzPX3zxxTmvP//8U1VVExMTMxy/bNkyVVU9efLkOeOy49ChQ6qqmpCQoE2aNNGDBw+qOiumEydOVFXVJ598Ul9++WVVVR00aJD269dPU1JSdNWqVXrppZeqqup3332nXbp00eTkZN27d6/WqFFDd+/erTNnztTu3bt7lnfy5Ek9deqUqqquX79e076X9NN5f3744Yd16NChqqo6ffp0jYqKUlXVF198Udu2baunT5/WAwcOaPny5TUxMTHTdUxOTtZOnTppXFycJiUlaY0aNfTEiROqqnr//ffrl19+qQcOHNAOHTp4hr/++uv60ksvqapqzZo19Y033vC0m/Zdqao+++yzOmLECFVV7d69u3799deqqvrhhx9qqVKlVFV1ypQpes8992hqaqqmpKRo9+7ddfbs2efEm367yNKSUaovllZdPfHC5jOmEAAWaya/q/l+pKCqu4BhwHZgD3AMWAIcVdW0Ijs7cZLHOUTkXhFZLCKLDxw4kB8hX7ARI0Z4jgZ27NjBhg0bAChWrJhnz7lFixZs3brVM0+fPn0ICAigcePGniOIzEphp5eUlMQ999xDs2bNuPHGG88qX52ZefPmMXDgQACuvPJKDh06xPHjxwHo3r07xYsXJzw8nEqVKnni8TZu3DhiYmJo3rw5q1atYvXq1QQFBdGtWzcmTZpEcnIyP/30E71792bhwoWsXr2adu3aER0dzahRo9i2bZunrZtvvtnzfuXKlXTo0IFmzZoxevRoVq1aBcCCBQs8RyG33vrXQ22mTp3K1KlTad68OTExMaxdu9bzfedY0imY+ZqVxjYXpXy/T0FEygG9gdrAUeBboFt251fVT4BPwKl9lNX0gwcPznRccHDweceXLFnyvOMzMmvWLKZNm8aCBQsoWbIknTt39pTJDg4ORtxSy5mVrwY8T0XLrrfffpvKlSsTFxdHampqrh9ic74y2wBbtmxh2LBhLFq0iHLlyjF48GDPOt5yyy289957lC9fnpYtWxIWFoaqcvXVV/PNN99kuDzv8tuDBw/mhx9+ICoqipEjRzJr1qzzxqqqPPPMM9x33305XNsM/PkpxO+G6z+x0tjmouOPE81dgC2qekBVk4DxQDugrIikJanqwC4/xJZrx44do1y5cpQsWZK1a9eycOHCHLeVWSnssLCwsx5peezYMapWrUpAQABffvml54Rt+unStz169GjASWTh4eGULl06W3EdP36cUqVKUaZMGfbt28cvv/ziGdepUyeWLl3Kp59+yi233AJAmzZt+P3339m4cSMAJ0+eZP369Rm2HR8fT9WqVUlKSvLEl9ZG2pPfxowZ4xnetWtXPv/8c885il27drF///5srUeG0kpj1+1ipbHNRckfSWE70EZESoqz23wVsBqYCfRzpxkE/OiH2HKtW7duJCcn06hRI4YMGUKbNm1y3FZmpbAjIyMJDAwkKiqKt99+mwcffJBRo0YRFRXF2rVrPXve6afzNnToUJYsWUJkZCRDhgxh1KhR2Y4rrZx3w4YNufXWW2nXrp1nXGBgID169OCXX37xdJVVrFiRkSNH0r9/fyIjI2nbtm2mz1R4+eWXueyyy2jXrt1ZD+IZPnw4b731FpGRkWzcuJEyZZx7Bq655hpuvfVW2rZtS7NmzejXr1/ungGdVhr7qhdy3oYxhZhfSmeLyEvAzUAysAy4G+ccwhicE9DLgNtU9cz52ikspbNN7iUkJFCiRAlEhDFjxvDNN9/w44/Z32/I1nbhKY3dHfp9lruAjSnAzlc62y+1j1T1RSD9tYObgZw9JswUeUuWLOHhhx9GVSlbtiyff+6D5xnMfsMtjf3PvG/bmELCCuKZQqFDhw7ExcX5bgGHNsGSUU45ZyuNbS5iRbLMhT+6xEzBla3tIX4vhNe30tjmolfkkkJISAiHDh2yxGAAJyEcOnQo68t0a7WDBxdYaWxz0Sty3UfVq1dn586dFNQb20z+CwkJoXr16llPaPckGFP0kkJwcDC1a9f2dxjGGFMoFbnuI2OMMTlnScEYY4yHJQVjjDEelhSMMcZ4WFIwxhjjYUnBGGOMhyUFY4wxHpYUjDHGeFhSMMYY42FJwRhjjIclBWOMMR6WFIwxxnhYUjDGGONhScEYY4yHJQVjjDEelhSMMcZ4WFIwxhjjYUnBGGOMhyUFY4wxHpYUjDHGeFhSMMYY42FJwRhjjIclBWOMMR6WFIwxxnhYUjDGGONhScHkq/jTSYyav5Xjp5P8HYoxJgOWFEy++mzeFl6cuIprh89l/qaD/g7HGJNOUFYTiEhLoAMQAZwCVgK/qeoRH8dmihhVZcKyXTSsEkZiciq3fvoHd7WvzZNdGxASHOjv8IwxnOdIQUTuEJGlwDNACWAdsB9oD0wTkVEickn+hGmKgqXbj7LtUAJ3tq/NT3/vwKC2Nfls3hZ6vjuPlbuO+Ts8YwznP1IoCbRT1VMZjRSRaKAesP1CFyoiZYH/Ak0BBe7ESTpjgVrAVuAmOxopWiYs20nxoACubVqFEsUCeal3U65qVJmnvltOn/d/55Gr6vFA50sJCrReTWP8JdP/far6fmYJwR0fq6rTc7jcd4BfVbUhEAWsAYYA01W1HjDd/WyKiMTkVCYv38M1TaoQFhLsGd6xfkWmPNqR7pFV+c9v6+n30QI2Hzjhx0iNubhle5dMRHqKyCwRWSgiD+Z0gSJSBugIfAagqomqehToDYxyJxsF9MnpMkzBM3Pdfo4mJHF9TLVzxpUpGcw7tzTnvVubs+XgSa4bMZf/LdiKqvohUmMubuc7pxCdbtBA4ArgcuCBXCyzNnAA+EJElonIf0WkFFBZVfe40+wFKmcS170islhEFh84cCAXYZj8NGHpLsJDi9Ohbnim0/SIjGDqYx25rHYFXvhxFbd//id7j53OxyiNMec7UnhARD4VkSru5x3AczgnnnfnYplBQAzwoao2B06SrqtInV3EDHcTVfUTVW2pqi0rVqyYizBMfjmWkMSMtfvpFRWR5fmCyqVDGHlHK17p05TFW49wzduz+TF2Vz5Faow53zmF+4D3gI9F5AXgBWABsALolYtl7gR2quof7ufvcJLEPhGpCuD+3Z+LZZgCZPKK3SSmpGbYdZQREeG2NjX5+ZEOXFoplEfGxPLw10s5mpDo40iNMefdbVPVOFXtDSwDfgQiVHWiqp7J6QJVdS+wQ0QauIOuAlYDE4FB7rBB7vJMETBh6S7qVQqlSUTpC5qvdngpvr2vLU92bcCUVXu55u05zFpn+wrG+NL5zincLyLzRWQ+UAroBpQVkSki0jGXy/0bMFpElgPRwKvA68DVIrIB6OJ+NoXctkMnWbztCH1jqiEiFzx/UGAAD11Rlx8eakfZksEM/mIRz05YQUJisg+iNcac7z6FB1U1UkSKA/NVdQwwQkS+BJ4H5uR0oaoaC7TMYNRVOW3TFEwTlu1CBPpEZ6/rKDNNIsow8eH2vPXbej6du5nfNx7kPzdF06JmuTyK1BgD5+8+2iUi/8RJAGvTBqrqEVV93OeRmUIvraxFm9oViChbItfthQQH8s/rGvHNPW1ISlFu/Gg+b/66lpNn7KjBmLxyvqTQG+ek8jzg9vwJxxQlaWUt+mbzBHN2talTgV8f7UC/FtX5YNYm2r8xg/dnbiTeKq8ak2vnSwoRqjpJVX9V1ZT0I8VR3YexmULOu6xFXgsLCebNflFMePByomuU5d9T1tH+jZmMmL6BY6f8lxx2HE7gjV/X0vs9q+dkCifJ7K5REfkWJ2n8CCzBueEsBKiLcxPbVcCLqvpb/oR6rpYtW+rixYv9tXhzHonJqbR+dRod6lXk3f7Nfb685TuP8u6Mjfy2eh9hIUHc0a42d7arRdmSxXy+7JRUZfb6/Xy5YBuz1h9AgNDiQRQLCuC7+y+nVngpn8dgzIUQkSWqmtF53cyTgjtjY2AA0A6oCiTg1Cn6GfhOVf16u6klhYJryqq93PflEr4Y3IorGlbKt+Wu2n2M92Zs5JeVewktHsSgy2tyV/s6lC+V98nh4IkzjF20g6//2M6uo6eoGFac/q0voX/rGpw8k8KNH80nNCSI7x+4nEphIXm+fGNyKsdJoaCzpFBw3f/lEhZvO8zCZ67yS9XTtXuP896Mjfy0Yg8lggMZ2LYm93SoQ3ho8Vy1q6os2nqErxZu45eVe0hKUS6/tAK3tanJ1Y0rE+y1rrE7jnLrpwupWaEUY+9rQ2mvQoDG+JMlBZOvjiUk0epf07itTU1e6NnYr7Fs3B/PezM2MjFuN8WCArjtsprc26nOBe+5x59OYsKyXYxeuJ11++IJCwmiX4vqDLisJnUrhWY63+z1B7hr5CJa1irHyDta28OETIFgScHkq9F/bOPZCSuZ/Lf2NK1Wxt/hALD5wAnem7mRH2N3ExQg9G99Cfd3upQqZc6fHFbvPs5Xf2zjh2W7SEhMoVm1MtzW5hJ6RkVQsliWDy4E4Idlu3h0bCzdmlTh/QExBAZc+E18xuQlSwomX/X7cD7HTiUx9bGOObqL2Ze2HjzJB7M2Mn7pLgJEuLlVDe7vfCnVvO6jOJ2Uwi8r9/DVwu0s2XaE4kEB9IyKYGCbmkTVKJuj5X42bwsvT17NrZddwr/6NC1w34u5uJwvKWTnGc3TVfWqrIYZA7D9UAKLtx3hqW4NCuQPX63wUrzZL4q/XVmPD2ZtYsyi7YxZtJ1+LWrQr0U1pq7ex7hFOziSkETt8FI8170R/VpUz/VVTHe1r82B+DN8NHsTFUOL89jV9fNojYzJW5kmBREJwXkkZ7iIlAPS/oeXBvL2biRTZORVWQtfq1G+JK9d34yHr6zLR7M2MXbRDr75czuBAcLVjSpzW5uaXH5pBQLysKvn6W4NOHTiDO9M30B4WHEGtqmZZ20bk1fOd6RwH/AoEIFzn0La/47jOCW1jTmLU9ZiZ56VtcgP1cqW4OU+TXnoirrM3XCADvUqZnmeIadEhNeub8bhk4m88ONKypcsRvfIqj5ZljE5db7nKbyDc6PaK6paR1Vru68oVbWkYM6xdPtRtvqgrEV+qFImhBtb1vBZQkgTFBjAe7fG0OKScjw2Npb5Gw/6dHnGXKisnqeQAlyfT7Fc9PYcO8WMtfv8HUaO+bKsRVFSolggnw1qRa3wktz75RIrh2EKlOzcVTRdRG6QgnjWsIhITE7lw1mbuHLYbO4cubhQ/kgkJqcyefkermlShTC7SStLZUoG8787L6NMiWAGf/En2w6d9HdIxgDZSwr3Ad8CiSJyXETiReS4j+O6aMzdcIBu78zhjV/XcvmlFQgOFCbG5eYR2P4xc91+jiYkcX3zwtd15C9VyoQw6s7WpKQqAz/7k/3xfq0aYwyQjaSgqmGqGqCqwapa2v18Yc9VNOfYdfQUD3y1hIGf/UlKqvLF4FZ8NrgVHetVZFLcblJTC9f9IxOW7iI8tBgd6oX7O5RCpW6lUD4f3IoD8WcY/Pkijlv5b+Nn2SpKIyK9RGSY++rh66CKsjPJKbw/cyNd/jObmev284+r6zPl0Y6eonG9oiPYc+w0i7cd8XOk2XcsIYkZa/fTK6qaX+ocFXbNLynHh7fFsH5fPPf+bzGnk86pVG9Mvsnyf7CIvA48Aqx2X4+IyGu+Dqwomr3+AN2Gz+XfU9bRsX440x7vxN+uqndWPZwujSoTEhzApELUhTR5xW4SU1K5vhBedVRQdG5QiWE3RrFw82EeGxtLSiE7UjRFR3Z2664DrlbVz1X1c6Ab0N23YRUtO48kcN+Xixn0+Z+oKiPvaMXHA1tSvVzJc6YtVTyIqxpV5ucVe0hOSfVDtBduwtJd1KsUSpMI61XMjT7Nq/Fc90b8snIvL/y4ksJcgsYUXtmr6AVlgcPu+4JR4awQOJOcwqdzNvPezI0APNm1AXd3qE3xoPNXyuwVFcFPy/fw+6ZDdKpfMT9CzbGCXtaisLm7Qx0OnDjDx7M3UzGsOI92sXIYJn9lJym8BiwTkZk4dzV3BIb4NKoiYOa6/bw0cRVbDyXQrUkVnuvRKMMjg4x0blCRsJAgJsbuLvBJobCUtShMhnRryKETiQyftoHw0OLcVoDKYagqRxOS2HPsNCfOJNOqVjnbGShislUlVUSqAq0ABRap6l5fB5YduamSOnLkyHOGNWnShFatWpGUlMTo0aPPGR8dHU10dDQJCQmMGzcuo3goE1GHf/2wBLb+SYngQGqFl6JMCee6/bZt29KgQQMOHjzI5MmTz5m/Y8eO1KlTh7179/LBl99x+GQiMTXLEeD+p7vqqquoUaMGO3bsYPr06efM361bN6pUqcLmzZuZM2fOOeN79OhBeHg469atY8GCBeeM79u3L2XKlGHlypVk9L3edNNNlCxZktjYWGJjYwHnQTLFgwJoVLU0AwYMIDg4mEWLFrFq1apz5h88eDAA8+fPZ/369WeNCw4OZsCAAQDMnj2bLVu2nDW+ZMmS3HTTTQBMmzaNnTt3njW+dOnSXH+9c5/lr7/+yt69Z2+iFSpUoGfPngBMmjSJQ4cOnTW+SpUqdOvWDYDx48dz/PjZV11Xr16dLl26ADBu3DgSEhLOGl+7dm06deoEwOjRo0lKOvsqovr163P55ZcD2dv2VGH9vniOnkqkXqUwrmzXOsttr2nTphw7dowJEyacMz47216NmrVYs3k7s6dPIykllcTkVBLdvztKXMqWUyGkxB+gmezwzFevUhgVQov5ZdvzdjFue2nrlBO5qpLqagu0x0kKQcC5W91FLlWVX1fu5dMxOwiVRG6oUJIqZUI8P+gXqkJocfbHn+FoQpJPHiWZF06cSeZ0UspZZadN3hCBepVDWbMnng37TzB30ioOTj1MaEAKTc4cI0CEAIGAACFAhHULtxG0EYrrafRIgjvenUaExVuPsDN1H4knj3Eg/gyJKakkef3ojx29lLUJayhHAq2D/7p5MkCEYkEBEALRNcpSKSCIwL2HKR4UwLZDJzl44gwVQgvm9mlyJssjBRH5AKcG0jfuoJuBTar6kI9jy1JBeZ7CjLX7eGnSarYdSuC6ZlV4tnvjXP9QJqek0ua16VxWuwLvD4jJo0jz1vM/rGTc4h0sfq6L3cXsI8cSkvhk7iYOn0zkdFIqp5NSOJWUwumkFM/nM8mp7md3WHIK2TlHXaZEMFXLhFC5dAhVSodQuUwIVcu470s778uWDM60e+jVn9fwxe9bWPRsl1yXFjf5K7dHClcCjdTNHiIyCjj3+Owi9f7Mjfx7yjrqVCzFl3e1pkO9vDkHEBQYwHXNqjJ20Q5OnEkmtHh2D+ryR2JyKpOW77ayFj5WpmQwT3ZteEHzqCqJKamcTnQShHcCSUpJJTy0OJVLh1CiWO4eDdorKoJP5mzml5V76d/6kly1ZQqO7PzSbAQuAba5n2u4wy56M9ftZ9jUdfSMiuA/N0Y5h9l5qFdUBP9bsI3fVu+lb/Pqedp2bllZi4JLRCgeFEjxoEDK4LuE3SSiNHXCSzExdrclhSIkO79iYcAaEZklIrNwbmArLSITRWSiT6MrwLYdOskj3yyjYZXSvHlDZJ4nBICYS8oRUSaESXF78rzt3LKyFkZE6BkVwcIth9h33Oo2FRXZOVJ4wedRFDIJicnc9+USRISPb2uR68PwzAQEOP/pPpu3hSMnEylXQE44p5W1GNDmEitrcZHrFR3BO9M3MHn5Hu5qX9vf4Zg8kJ2CeLOBdTg3rZUG1qnq7LSXrwMsaFSVZ8avYN2+eN65JZpLKmTv3oOc6hkVQXKq8svKAnEVMPBXWYsbYgpWl5bJf5dWDKVptdJMjN3l71BMHslO7aO7gT9xHrbTD1goInf6OrCC6ovft/Jj7G7+cXV9Ojeo5PPlNYkoTZ2KpZgYV3D+01lZC+OtV1QEcTuPsfWgPROiKMjOsf+TQHNVHayqg4AWwNO+DatgWrj5EP/6eQ1XN67Mg53r5ssyRYSekRH8seVwgei3TStr0Temmt3JagDoERkBUKiKOJrMZScpHALivT7Hu8MuKnuOneLhr5dSs3xJ/nNTFAEB+feD2Cs6AlWYvNz/J5ytrIVJL6JsCVrXKs+PcbutiF8RkJ2ksBH4Q0SGisiLwEJgvYg8LiKP+za8guFMcgoPfLWUU4kpfDywBaXz+br8Sys6XTX+fiKbqjJh2U7a1K5AhN3FbLz0io5g4/4TrNkTn/XEpkDLTlLYBPyAU+IC4EdgC86lqmG+CatgeWnSamJ3HGXYjVHUq+yfVe4VFUHcjqN+fZbvsh1H2Xoogb723ASTznXNqhIUUDgfJWvOluUlqar6ki8WLCKBwGJgl6r2EJHawBigArAEGKiqib5Y9oUYu2g7X/+xnfs7Xcq1zar6LY4eURG89staJi/fw0NX5M/5jPQmLN1F8aAArm1axS/LNwVX+VLFaF8vnElxu3nayqgXatm5+mimiMxI/8qDZT8CrPH6/AbwtqrWBY4Ad+XBMnIlbsdRnv9xFe3rhvPENf6ta1+tbAla1izHxFj/7IlZWQuTlV5REew6eoql2wvPo2TNubLTffQEzhVITwLPA7E4e/g5JiLVcZ7e9l/3s+DUWPrOnWQU0Cc3y8itQyfO8MBXS6gYWpwR/ZsXiJu0ekVHsG5fPOv25n+/7Swra2GycE2TKhQPCuBHP+24mLyRnZvXlni9flfVx4HOuVzucOApIO15kxWAo6qa7H7eCWT46yMi94rIYhFZfODAgVyGkbHklFT+9s0yDp5M5KPbWhSY0tXXNatKgOCXexYmLLOyFub8QosH0aWQPUrWnCs73UflvV7hItKVXDySU0R6APtVdUlO5lfVT1S1paq2rFjRN08le3PKOuZvOsSrfZvRrHrBefpoeGhx2tUNZ1Lcnny99O9YQhLT1+ynZ1REgThiMgVXz6gIDp5IZP6mi+6q9SIjO//Dl+B0Fy0BFgD/IHf9/e2AXiKyFefE8pXAO0BZEUk78V0d8MstvJOX7+aTOZsZ2KYm/VoUvDIOPaMi2H44gbidx7KeOI98MX+LlbUw2dK5QUXCigfZVUiFWHa6j2qrah33bz1VvUZV5+V0gar6jKpWV9VawC3ADFUdAMzEKaMBMAjn0td8tW5vPE99t5yYS8ryfI/G+b34bOnapArFAgPy7YTzmj3HeX/mRnpHR9C0WsE5ajIFU0hwIN2aVmHKyr2cTkrxdzgmB7LTfXSjiIS5758TkfEi4otHgT0NPC4iG3HOMXzmg2Vk6tipJO7/agkliwXx4W0tfFIKOy+UKRFM5wYVmbx8Nympvu1CSkpJ5cnv4ihTIpihPZv4dFmm6OgVHUH8mWRmrdvv71BMDmTnl+95VY0XkfZAF5wf6w/zYuGqOktVe7jvN6tqa1Wtq6o3quqZvFhGdqSmKo+PjWXH4QQ+vC2GyqVD8mvROdIzKoL98Wf4Y4tv+20/nr2JlbuO80qfZgWmbLcp+NrWqUB4aDHrQiqkspMU0o4BuwOfqOpPQJH6hXh3xkamr93P8z0a06pWeX+Hk6UujSpTsligTx++s25vPO9M30DPqAi62c1q5gIEBQbQvVlVpq/ZT/zpJH+HU+QkpaQyf9NBnxXIzE5S2CUiHwM3Az+LSPFszlcozFi7j+HT13N982rc3ramv8PJlhLFArm6cWV+WbmHxOS8v/QvKSWVJ76No3RIMC/1sm4jc+F6RUdwJjmV31bv83coRcLBE2f4bslOHhq9lJj/+41bP/3DZ1Vps/PktZuAbsAwVT0qIlVxbmQr9LYePMmjY2JpVKU0/+rbrFDdmt8rKoIfY3czb+MBrmxYOU/b/mTOZlbsOsaHA2IKzD0apnCJuaQc1cuV4MfY3Vyfj1etbT+UwH1fLaFHZFXu7ViH4EJ6CbWqsmr3cWas3c+MtfuJ23kUVagUVpzukVW5omEl2tf1zT1D2al9lACM9/q8B/B/DedcSkhM5v6v3EdqDvTdIzV9pUO9ipQpEczE2N15mhTW7Y1n+LT1dI+s6tdaT6ZwS3t+8ydzNnPoxBkqhBb3+TJVlWd/WMH6ffH8e8pxJsXt5s1+kURWL+vzZeeFk2eSmbfxIDPX7mfmuv3sO34GEYisXpbHutTnyoaVaBJR2uc7r9k5UihyVJUh3zuP1Bx5R2tqlPftIzV9oZhbmG5S3G5OJabkSVJLdq82CgsJ5v+s28jkUq+oCD6ctYmfV+5lYBvfd83+GLubuRsOMrRnYyLKluD5H1fS5/3fuat9bR67uj4lixW8n7tth056jgb+2HyYxJRUwooH0aF+OFc2rEznBhUJz4eE6q3gfUv5YOT8rUyM282TXRvQqb5v7orOD72iIhizaAcz1u6ne2Tu9+o/mbuZ5TuP8f6tMfmyZ2eKtoZVwqhXKZRJsbt9nhSOJiTy8uTVRNUoy8C2tQgMENpcWoHXf1nLp3O38OuqvbzWN5L2fi7TkpSSyuKtR5ixdh8z1u5n0wGnFH6diqW4vW1NrmxUiZY1y/v1kviLMim0qxvOXe1r82DnS/0dSq5cVqcCFcOKMzFuV66TwoZ98Qz/bQPXNauSJwnGGBGhd3QEw6auZ9fRU1Tz4YOZXv15DUdPJfHV9c0IdJ+KWDokmFf7NqN3VATPjF/BbZ/9Qb8W1XmueyPKlsy/c2WqyoJNh/hm0Q5mrdtP/OlkggOFNnUqMOCymlzZsBK1wkvlWzxZuSiTQv3KYQX2juULERggdG9Wla//3M7x00k5fiJcckoqT3y3nFLFA/m/3k3zOEpzMesZ5SSFyXG7ua+Tb3bCFm4+xLjFO7mvUx0aVS19zvjL6lTg50c68O6MDXw8ezOz1u1naK8mdG9W1af98/Gnk5iwbBf/W7CNjftPULZkMNc2rcKVDSvTvl44ocUL5s9vwYzKZFuv6AhGzt/K1FX7clyr6b/zthC34yjv9m+e7/2XpmirWaEUUTXKMtFHSeFMcgr/nLCCGuVL8OhVmT/zJCQ4kCe7NqR7swie/n45D3+9jB8a7eblPk2oWiZvj2A27Ivnfwu2MX7pTk4mphBZvQzDboyiR2RVQoIL/gUtlhQKueY1ylKjfAkmxu3OUVLYuD+et35bz7VNq9DDuo2MD/SKiuDlyavZuP8EdSuF5mnbH8zcxOYDJxl1Z+tsXWzROKI0Ex68nJHztzJs6jqufmsOQ65tyK2tLyEgIOdHDckpzj0Z/1uwjQWbD1EsKIAekVW5vW0tomuUzXG7/lA4L+I1HiJCz8gIft94kEMnLqwySEqq8sS3yylVzOk2Kkz3aZjCo2dkVUTI87IXG/fH88Esp1jjhVwwEhQYwN0d6jD10U5E1yjLcz+s5JZPFrLpwIkLjuFA/Bnenb6B9m/M5IHRS9l+OIGnujVgwZAreeum6EKXEMCSQpHQKzqClFTl5xUXdvvIZ/M2E7vjKC/1bkrFMOs2Mr5RqXQIbetUYFLc7jx7DkhqqvLP8SspWSwox+cHL6lQki/vas2/+0Wybl88174zl/dmbCApiwcEqSqLtx7m798s4/LXp/Of39ZTr3Ion97ekjlPXcGDnesW6qv3rPuoCGhQ2b30L24PA9vWytY8G/efYNjU9XRtUpme1m1kfKxXVARDxq9g5a7jefLgqnGLd/Dn1sO8cUOzXJ0HExFubFmDzg0qMXTSKuek+PI9vHFDJFHp9vJPJabwY6xz4nj1nuOEhQRxW5uaDGxTkzoV87ZbzJ/sSKEIEBF6RUXw59bD7D56KsvpU1KVJ7+Lo2SxQF7uY91GxveubVqV4EDJk0fJHog/w6s/r6F17fLc1LJGHkQHFcOK8/6tMXx6e0uOJiTR94PfeWXyahISk9l68CQvT17NZa9OY8j4FaSq8q++TVn4zFW82LNJkUoIYEcKRUbPqAj+89t6Ji/fzb0dz3+Vx+fztrBs+1HeuSWaSmEFu0y4KRrKlAymU/1KTIrbwzPXNsrVSd2XJ6/mdFIqr/qgXtnVjStzWZ3yvPnrWv47bwvfL93JkYQkggKEbk2rcHvbWrSqVa5I70hZUigiaoWXIqp6GSbGnT8pbDpwwrnqonFlekVF5GOE5mLXKzqCaWv28efWw7SpUyFHbcxat5+Jcbt5tEu9PL+SKU3pkGBe6dOMXlHV+Hj2JppVL8OtrS+hUgF/zkpesaRQhPSMiuCVn9aw+cCJDA9pU1KVp75bTkhwIP+ybiOTz7o0qkSJ4EAmxu3OUVJISEzmuR9WcmnFUjyQD9UIWtcuT+vaBf/5KnnNzikUIT0iIxAh04fvfPH7FpZsO8LQXo0vmr0eU3CULBbkPAdkxZ4sr/DJyDvTNrDzyCle7duM4kEF/yawwsqSQhFSpUwIrWuVZ2LcrnMu/dty8CT/nrKOLo0q0Se6mp8iNBe7XlERHElIYt6Ggxc036rdx/jvvC3c3LIGl+Ww68lkjyWFIqZXdASbDpxk9Z7jnmEpqcqT38ZRPCig0D1MyBQtHes7zwH5MTb7VyGlpCr/HL+CciWDeea6hj6MzoAlhSLn2qZVCQqQs+4eHTV/K4u3HeHFnk2obN1Gxo+KBQVwXbMqTF29j1OJKVnPAHy5YCtxO4/xfI/G+Vrd9GJlSaGIKV+qGO3rhTM5bg+qytaDJ3lzylqubFiJ62Os28j4X8+oCBISU5i+NuvnN+8+eop/T1lHx/oV7Wq5fGJJoQjqFRXBrqOnWLztCE99t5zgwACfXNNtTE5cVrsClcKKMzE261pIL05cRYqqXS2XjywpFEFXN65M8aAAHh0Ty59bD/NCj8ZUKWPdRqZgCAxwnt88a90Bjp1KynS6X1fu5bfV+3i0S/1C+cjcwsqSQhEUFhLMlQ0rsevoKa5oUDHHz1kwxld6RUWQmJLKlJV7MxwffzqJoRNX0ahqae5qXzufo7u4WVIoom5vW4uGVcJ49XrrNjIFT2T1MtSsUDLTctrDpqxjX/xpXru+GcGB9jOVn+zbLqLaXlqBXx/tmOdPlTImL6QVcZy/6SD740+fNW7Z9iP8b+E2BhXCB9QUBZYUjDF+0Ts6glSFn5b/dQd+Ukoqz4xfQeWwEP5xTeaP1zS+Y0nBGOMXdSuF0ahq6bO6kP47dwtr98bzf72bEBYS7MfoLl6WFIwxftMrKoJl24+y43AC2w8l8M5058FP1zSp4u/QLlqWFIwxftMzynnq38S43Tz7wwqCAgJ4qVdTP0d1cbPS2cYYv6leriQtapbjg5kbOZmYwku9mtg9NX5mRwrGGL/qHR3BycQUomuU5bY2Nf0dzkXPjhSMMX7VKyqCOesP8nS3BgTm4jGdJm/k+5GCiNQQkZkislpEVonII+7w8iLym4hscP+Wy+/YjDH5r2zJYvx3UEvqVQ7zdygG/3QfJQP/UNXGQBvgIRFpDAwBpqtqPWC6+9kYY0w+yvekoKp7VHWp+z4eWANUA3oDo9zJRgF98js2Y4y52Pn1RLOI1AKaA38AlVU17dbGvUDlTOa5V0QWi8jiAwcO5E+gxhhzkfBbUhCRUOB74FFVPe49Tp0HDGtG86nqJ6raUlVbVqxYMR8iNcaYi4dfkoKIBOMkhNGqOt4dvE9EqrrjqwL7/RGbMcZczPxx9ZEAnwFrVPUtr1ETgUHu+0HAj/kdmzHGXOz8cZ9CO2AgsEJEYt1h/wReB8aJyF3ANuAmP8RmjDEXtXxPCqo6D8jsDpWr8jMWY4wxZ7MyF8YYYzwsKRhjjPGwpGCMMcbDkoIxxhgPSwrGGGM8LCkYY4zxsKRgjDHGw5KCMcYYD0sKxhhjPCwpGGOM8bCkYIwxxsOSgjHGGA9LCsYYYzwsKRhjjPGwpGCMMcbDkoIxxhgPSwrGGGM8LCkYY4zxsKRgjDHGw5KCMcYYD0sKxhhjPCwpGGOM8bCkYIwxxsOSgjHGGA9LCsYYYzwsKRhjjPGwpGCMMcbDkoIxxhgPSwrGGGM8LCkYY4zxsKRgjDHGw5KCMcYYD0sKxhhjPCwpGGOM8bCkYIwxxqNAJQUR6SYi60Rko4gM8Xc8xhhzsSkwSUFEAoH3gWuBxkB/EWns36iMMebiUmCSAtAa2Kiqm1U1ERgD9PZzTMYYc1EJ8ncAXqoBO7w+7wQuSz+RiNwL3Ot+PCEi63K4vHDgYA7ntbasrcLaVl63Z20VzrZqZjaiICWFbFHVT4BPctuOiCxW1ZZ5EJK1ZW0Vmrbyuj1rq2i05a0gdR/tAmp4fa7uDjPGGJNPClJSWATUE5HaIlIMuAWY6OeYjDHmolJguo9UNVlEHgamAIHA56q6yoeLzHUXlLVlbRXCtvK6PWuraLTlIarqi3aNMcYUQgWp+8gYY4yfWVIwxhjjUeSTgoj0EREVkYY5nF9F5Cuvz0EickBEJuddlLkjIifyu00RmSUimV4Ol9vvPV1bz4rIKhFZLiKxInLO/SvZbEdF5D9en58QkaG5jC1PvnsRSXHXLe1V6zzTZvrd59X26qvtvoBuq3myfXm1V11EfhSRDSKySUTecS+eyWz6R0WkZLpheb6tZleRTwpAf2Ce+zfb3LIbACeBpiJSwv18NXapbHbk6HtPT0TaAj2AGFWNBLpw9k2OF+IMcL2IhOcmJh85parRXq+tOWwn19uriATlRTuFQR5vX4iIAOOBH1S1HlAfCAX+dZ7ZHgVKphvmt221SCcFEQkF2gN34Vziioh0FpE5IvKTW3zvIxEJcMedEJH/iEgc0NarqZ+B7u77/sA3XstoLSILRGSZiMwXkQbu8DkiEu013TwRifLhunb23osTkfdEZLD7fquIvCQiS0VkRXb33s/XZhbzZfa9ZxbfdSKyVkSWiMiIdHujVYGDqnoGQFUPqupuEWkhIrPdeaaISFW3rVnunlmsiKwUkdZebSXjXLHxWAYx1xKRGe7e4nQRuUREyojINq/to5SI7BCR4PTr686T9v329mpzjYh86u6JTvX6kc1SZuvoGpjJOkLOttfBIjJRRGYA03PRTpbbfUHaVsnb7QvgSuC0qn7htpeCs73d6W4/w9z5lovI30Tk70AEMFNEZnq145NtNTuKdFLAqZ30q6quBw6JSAt3eGvgbziF9y4FrneHlwL+UNUoVZ3n1c4Y4BYRCQEigT+8xq0FOqhqc+AF4FV3+GfAYAARqQ+EqGpcHq/fhTioqjHAh8ATPl5WZt/7Odzv9GPgWlVtAVRMN8lUoIaIrBeRD0Skk7uhvwv0c+f5nLP3xEqqajTwoDvO2/vAABEpk274u8Aod29xNDBCVY8BsUAnd5oewBRVTUo372mgr/v9XgH8R0TEHVcPeF9VmwBHgRsy+SpKyF9dRxNyuY452V4BYtzldcpFO3mx3efntprX21cTYIn3AFU9DmwH7gZqAdFp25mqjgB2A1eo6hXp2vLFtpqlop4U+uNs2Lh/07oy/nQL76Xg7P20d4enAN+nb0RVl+P8Y/bH2XvyVgb4VkRWAm/jbBQA3wI93A3sTmBkHqxPbox3/y7BWRdfyux7z0hDYLOqbnE/f+M9UlVPAC1w6l0dAMYC9wFNgd9EJBZ4DucOeLzbUNU5QGkRKevV3nHgf8Df08XRFvjaff8lf20TY4Gb3fe3uJ/TE+BVEVkOTMOp41XZHbdFVWPd9+f77r27j/oCDXKxjjnZXgF+U9XDuWwnL7b7fNtW83r7ykJn4GNVTXbnP3y+iX20rWapwNy8ltdEpDzOoVwzEVGcG+IU+Mn96y3t82k3UWRkIjAM5x+2gtfwl4GZqtpXnJODswBUNUFEfsPZa74JZ8PzpWTOTvIh6cafcf+mkP1/96zaPMd5vvcfL7StNO6/ySxgloisAB4CVqlq28xmyeLzcGAp8EU2Fj8R5we/PM6/4YwMphmAc4TTQlWTRGQrf63fGa/pUoDsdh8JuVvHC9peXSczWI4vtvsCsa2myePtazXQz3ukiJQGLgG2ZjcmL8PJ2201S0X5SKEf8KWq1lTVWqpaA9gCdABai1NOIwAns847X0Ouz4GXVHVFuuFl+OsE3OB04/4LjAAWqeqRHK5Hdm0DGotIcXfP5So/tZnZ9x6QSVvrgDry19U2N3s3JiINRKSe16BoYA1QUZyThIhIsIh47/He7A5vDxxzD6093D20cTjnPNLMxz3/gfMjP9ed9gROCZZ3gMmZ7DSUAfa7CeEKzlOB8gKsIxfrSM6214z4YrsvKNuqL7av6UBJEbndnSYQ+A/OEdMU4D5xTuSn7UABxANhGcXng201S0U5KfQHJqQb9r07fBHwHs4//pYMpjuHqu50+//SexN4TUSWkW6vRlWXAMfJXpbPEXcDO6OqO3A2npXu32V+ajOz7/2WjNpS1VM4fbO/isgSnP8g3v/JQoFRIrLa7Z5pjNOH3Q94Q5yLAmKBy73mOe3+e3zE2f+ZvP0Hp/Rwmr8Bd7jLGAg84jVuLHAb6Q7H074nnH7dlu5e5u04/e25os4zRXK8jjnZXjOJI8+2+wK4rUIeb1/qlIjoC9woIhuA9TjnnP6Jkyy3A8vddm91Z/sEZ/v3PtHsLdfb6gVR1YvqhXMYPDmflhXhbhQBPlxGFM45kgLdZhbLC3X/CvAB8Fgu2poFtMynuPP1eyosr8y2+yKyrebb9uWvV1E+UvAr9/DxD+BZVU310TLuxznp9VxBbjMb7nFP6K3C6Zb4OB+XnSN++p4KvMy2+yK0rRZ5VhDPGGOMhx0pGGOM8bCkYIwxxsOSgjHGGA9LCqZQEZGyIvKg1+ezat5cYFu9RGTIBUxfQ0RmupcvrhKRR7zGlReR38SpjPmbiJRzhw8Qp0bNCnFqBEV5zdNNnPpbGy8kjgshIiNFpF8Gw/8tTr2p5eKU1SjrNe4ZN6Z1ItI1G+t+ozssVc5TjdQUDpYUTGFTFue+hlxT1Ymq+voFzJIM/ENVGwNtgIdEpLE7bggwXZ3KmNPdz+DcB9NJVZvh3AX8CXhuanofuBbn2vj+Xm3lh9+ApurUz1kPPOPG1RjnnpImQDfgAzfW8637Spz6YXPyMX7jI5YUjE+IU8lxrbunul5ERotIFxH53d2bbu1OV15EfnD3WBeKSKQ7fKiIfC5OVcrN4lSTBHgduFScwnH/doeFish37vJGizjF6ETk9bSbkkRkWAYxDhaR99z3I8Wp0DrfXd45e9equkdVl7rv43Fufqzmju4NjHLfjwL6uNPN17/u6l3IXzV0WgMb1anBlYhTI6p3BjHeIyKLRCRORL4Xt+5+ZvGK4z13L38aUCmjfx9VnapuDZ50cfUGxqjqGXXqUW0EWp9v3VV1jaquy2g5pvCxpGB8qS7O3ZgN3detOMW7nsC5wxPgJWCZu8f6T5wCYGkaAl1xfkBfFKfI2hBgkzqF4550p2uOU5O+MVAHaCciFXDuLG3itv1KNuKt6sbXAyf5ZEqcshzN+atyaGVV3eO+38tfBfG83QX84r6vxtl1+3fyV4LxNl5VW6lqFM4PsfcdtBnFm1ZMrzHO3dXed+Jm5s4LiSuDdTdFiCUF40tbVHWFexPTKpzuFQVW8Ff1y/Y4lR5R1RlABXEKiAH85O6xHgT2k/EPLTh3tO50lxPrtn0Mp7zAZyJyPZCQjXh/UNVUVV19nmWlPS/ie+BRdSpZnsVdR003zxU4P+hPZyMOb01FZK44JTQGcHZV04zi7Qh8o6opqrqbLIqiicizOF1Do7MTTFbrbgo/SwrGl7wrhKZ6fU4le9Uv01cYzWyec6Zzu0ZaA9/h7En/eoHLk4wmcI9WvsephT/ea9Q++etBLFVxkljaPJE4dW96q+ohd/AuoIbX/NXJ+MlmI4GH3XMSL3F29c8s4z0fcR5C0wMYoH/dxZppXOdZd1OEWFIw/jYXZw8YEemM84CV8+2BZlpR0pu7R1tGVX/GeXpVrp96556r+AxYo6pvpRs9ERjkvh+EUyocEbkE5/kAA9V56FCaRUA9car1FsM5uTsxg8WGAXvcH+QB2QhzDnCziAS6ySn9g1vS1qUb8BTQS1W9j6Im4jxYp7iI1MZ5SNCfWay7KUKK7PMUTKExFPhcnIqPCfz1w5ohVT3knqxeidMP/lMmk4YBP4rz1DABHs+DWNvhVKVcIU6tJoB/uonndWCciNyFU8b5Jnf8CzjPIfjAPf+drKotVTVZRB7GKaccCHyuqqsyWObzOH33B9y/WSXECTjPs1iNU5FzQSbTvQcUx3mQDMBCVb1fVVeJyDh3/mTgIVVNEadMdIbrLiJ9cZ4GVhH4SURiVbVrFnGaAspqHxljjPGw7iNjjDEelhSMMcZ4WFIwxhjjYUnBGGOMhyUFY4wxHpYUjDHGeFhSMMYY4/H/dEu3uLmVtuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for topic in TOPICS:\n",
    "    x, y, x_rivm, y_rivm = make_x_y(classifications, offsets, topic)\n",
    "    plot_single(x, y, x_rivm, y_rivm, topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multi(ax, x, y, x_rivm, y_rivm, offsets, topic, counter):\n",
    "    ax.set_ylim(0, 100)\n",
    "    ax.plot(x, y, label=\"tweets\")\n",
    "    ax.plot(x_rivm, y_rivm, label=\"questionnaires\")\n",
    "    ax.plot(x, [ annotation_averages[topic] for i in x ], \"--\", color=\"grey\", label=\"annotation average\")\n",
    "    ax.legend()\n",
    "    ax.set_title(labels[topic])\n",
    "    if counter >= 3:\n",
    "        ax.set_xticks(x)\n",
    "        x_labels = []\n",
    "        for i in range(0, len(RIVM_MONTHS_ORG + RIVM_MONTHS)):\n",
    "            if i % 3 == 0:\n",
    "                x_labels.append((RIVM_MONTHS_ORG + RIVM_MONTHS)[i])\n",
    "            else:\n",
    "                x_labels.append(\"\")\n",
    "        ax.set_xticklabels(x_labels)\n",
    "        ax.set_xlabel(\"months in 2020 and 2021\")\n",
    "    if counter % 3 == 0:\n",
    "        ax.set_ylabel(\"support (%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erikt/.local/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,8))\n",
    "gs = fig.add_gridspec(2, 3, hspace=0.3, wspace=0.3)\n",
    "axes = gs.subplots(sharex='col', sharey='row')\n",
    "\n",
    "for i in range(0, len(TOPICS)):\n",
    "    topic = [TOPICS[0], TOPICS[1], TOPICS[3], TOPICS[4], TOPICS[2], TOPICS[5], ][i]\n",
    "    axes_x = i % 3\n",
    "    axes_y = int(i/3)\n",
    "    x, y, x_rivm, y_rivm = make_x_y(classifications, offsets, topic)\n",
    "    plot_multi(axes[axes_y, axes_x], x, y, x_rivm, y_rivm, offsets, topic, i)\n",
    "fig.savefig(f\"ieee.png\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Compare graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(x, y, x_rivm, y_rivm):\n",
    "    data = { x[i]: y[i] for i in range(0, len(x)) }\n",
    "    data_rivm = { x_rivm[i]: y_rivm[i] for i in range(0, len(x_rivm)) }\n",
    "    differences = [ abs(data[key] - data_rivm[key]) for key in data_rivm ]\n",
    "    delta =  np.average(differences)\n",
    "    std = np.std(differences)\n",
    "    r = np.corrcoef([data[i] for i in x_rivm], [data_rivm[i] for i in x_rivm])[0][1]\n",
    "    return delta, std, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        curfew: delta: 78.0±2.0; r: nan; tweets: total: 76721; relevant: 19250 (25%); irrelevant: 57500 annotation: relevant: 27% support: 16%\n",
      "      distance: delta: 13.0±7.0; r: 0.1; tweets: total: 287898; relevant: 212525 (74%); irrelevant: 75372 annotation: relevant: 76% support: 74%\n",
      "     mondkapje: delta: 43.0±11.0; r: 0.2; tweets: total: 412394; relevant: 155383 (38%); irrelevant: 257018 annotation: relevant: 45% support: 47%\n",
      "       testing: delta: 21.0±16.0; r: -0.2; tweets: total: 651992; relevant: 176288 (27%); irrelevant: 475613 annotation: relevant: 47% support: 62%\n",
      "        vaccin: delta: 55.0±13.0; r: 0.2; tweets: total: 2235353; relevant: 577802 (26%); irrelevant: 1657676 annotation: relevant: 35% support: 29%\n",
      "work-from-home: delta: 20.0±8.0; r: 0.2; tweets: total: 46067; relevant: 11864 (26%); irrelevant: 34213 annotation: relevant: 34% support: 87%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erikt/anaconda3/envs/python37/lib/python3.7/site-packages/numpy/lib/function_base.py:2534: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "/home/erikt/anaconda3/envs/python37/lib/python3.7/site-packages/numpy/lib/function_base.py:2535: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "for topic in TOPICS:\n",
    "    x, y, x_rivm, y_rivm = make_x_y(classifications, offsets, topic)\n",
    "    delta, std,r = evaluate(x, y, x_rivm, y_rivm)\n",
    "    nbr_of_relevant_tweets = round(sum([classifications[topic][key][\"total\"] * \n",
    "                                        (classifications[topic][key][\"__label__EENS\"] + \n",
    "                                         classifications[topic][key][\"__label__ONEENS\"]) for key in classifications[topic]])/100)\n",
    "    nbr_of_irrelevant_tweets = round(sum([classifications[topic][key][\"total\"] * classifications[topic][key][\"__label__ANDERS\"] for key in classifications[topic]])/100)\n",
    "    nbr_of_tweets = sum([classifications[topic][key][\"total\"] for key in classifications[topic]])\n",
    "    annotation_counts = pd.Series([ annotations[topic][key][\"label\"] for key in annotations[topic] ]).value_counts().to_dict()\n",
    "    print(f\"{topic:>14s}: delta: {round(delta)}±{round(std)}; r: {r:0.1f};\", end=\" \")\n",
    "    print(f\"tweets: total: {nbr_of_tweets}; relevant: {nbr_of_relevant_tweets} ({round((100*nbr_of_relevant_tweets)/(nbr_of_relevant_tweets+nbr_of_irrelevant_tweets))}%); irrelevant: {nbr_of_irrelevant_tweets}\", \n",
    "          end=\" \")\n",
    "    print(\"annotation: relevant: \" + str(round(100 * (annotation_counts[\"EENS\"] + annotation_counts[\"ONEENS\"])/len(annotations[topic]))) + \"%\", end=\" \")\n",
    "    print(\"support: \" + str(round(100 * annotation_counts[\"EENS\"]/(annotation_counts[\"EENS\"] + annotation_counts[\"ONEENS\"]))) + \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| topic             |  tweets | relevant | annotated | relevant | support | time frame          | F     | delta          | r    |\n",
    "| -----             |  ------ | -------- | --------- | -------- | ------- | ------------------- | ----- |--------------- | ---  |\n",
    "| social distancing | 251,625 |      58% |     5,977 |      76% |     74% | Feb 2020 - Jul 2020 | 0.723 | 0.1&plusmn;0.1 | -0.3 |\n",
    "| testing           | 124,392 |      28% |     1,181 |      47% |     62% | Mar 2020 - Dec 2020 | 0.496 | 0.1&plusmn;0.1 | -0.1 |\n",
    "| work from home    |  37,486 |       9% |     1,003 |      34% |     87% | Mar 2020 - May 2021 | 0.492 | 0.1&plusmn;0.0 | -0.7 |\n",
    "| vaccination       |  64,927 |      15% |     1,340 |      35% |     29% | Jan 2020 - May 2021 | 0.354 | 0.5&plusmn;0.1 | -0.0 |\n",
    "| wearing facemasks |  98,702 |      23% |     1,011 |      45% |     47% | Mar 2020 - Mar 2021 | 0.472 | 0.5&plusmn;0.1 | -0.1 |\n",
    "| curfew            |  62,843 |      14% |       750 |      27% |     16% | Mar 2020 - May 2021 | 0.413 | 0.8&plusmn;0.0 | -1.0 |\n",
    "| social distancing | 204,574 |      47% |     1,186 |      75% |     72% | Feb 2020 - Jul 2020 | 0.685 | 0.1&plusmn;0.1 | -0.4 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Get dates of annotated social distancing tweets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_data = pd.read_csv(DATA_DIR + \"distance-tweets.csv\")\n",
    "time_data = pd.read_csv(\"csv/first_tweet_ids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = {}\n",
    "time_data_index = 0\n",
    "for i, row in tweet_data.iterrows():\n",
    "    while str(row[\"id_str\"]) >= str(time_data.iloc[time_data_index+1][\"id_str\"]):\n",
    "        time_data_index += 1\n",
    "    while str(row[\"id_str\"]) < str(time_data.iloc[time_data_index][\"id_str\"]):\n",
    "        time_data_index -= 1\n",
    "    if str(row[\"id_str\"]) < str(time_data.iloc[time_data_index+1][\"id_str\"]) and str(row[\"id_str\"]) >= str(time_data.iloc[time_data_index][\"id_str\"]):\n",
    "        month = time_data.iloc[time_data_index][\"file_name\"].split(\"/\")[8]\n",
    "        if month not in months:\n",
    "            months[month] = 0\n",
    "        months[month] += 1\n",
    "    else:\n",
    "        print(f\"cannot happen: {row['id_str']} {time_data_index} {time_data.iloc[time_data_index]['id_str']}\")\n",
    "        break\n",
    "months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluate fastText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_fasttext_data(annotations):\n",
    "    _, path = tempfile.mkstemp()\n",
    "    data_file = open(path, \"w\")\n",
    "    for row in annotations:\n",
    "        print(f'__label__{row[\"label\"]} {preprocess(row[\"text\"])}', file=data_file)\n",
    "    data_file.close()\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_fasttext_model_np(annotations):\n",
    "    path = store_fasttext_data(annotations)\n",
    "    model = fasttext.train_supervised(path, dim=300, pretrainedVectors=\"twiqs-model-2020.vec\")\n",
    "    os.remove(path)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_label_prefix(label):\n",
    "    return re.sub(\"__label__\", \"\", label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic = DISTANCE1000\n",
    "predicted_labels = []\n",
    "for train, test in sklearn.model_selection.KFold(n_splits=10).split(annotations[topic]):\n",
    "    annotations_nparray = np.array([annotations[topic][key] for key in annotations[topic]])\n",
    "    model = make_fasttext_model_np(annotations_nparray[train])\n",
    "    test_data = [ row[\"text\"] for row in annotations_nparray[test] ] \n",
    "    predicted_labels.extend(model.predict(test_data)[0])\n",
    "    print(\"*\", end=\"\")       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct = 0\n",
    "found = 0\n",
    "target = 0\n",
    "gold_labels = [ annotations[topic][key][\"label\"] for key in annotations[topic] ]\n",
    "for i in range(0, len(predicted_labels)):\n",
    "    if remove_label_prefix(predicted_labels[i][0]) != \"ANDERS\":\n",
    "        if remove_label_prefix(predicted_labels[i][0]) == gold_labels[i]:\n",
    "            correct += 1\n",
    "        found += 1\n",
    "    if gold_labels[i] != \"ANDERS\":\n",
    "        target += 1\n",
    "precision = correct/found\n",
    "recall = correct/target\n",
    "f = 2 * precision * recall / (precision + recall)\n",
    "print(f\"precision: {precision:0.3f}; recall: {recall:0.3f}; f: {f:0.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CURFEW: precision: 0.497; recall: 0.353; f: 0.413 \\\n",
    "DISTANCE: precision: 0.711; recall: 0.735; f: 0.723 \\\n",
    "DISTANCE1000: precision: 0.667; recall: 0.704; f: 0.685 \\\n",
    "FACEMASK: precision: 0.516; recall: 0.435; f: 0.472 \\\n",
    "TESTING: precision: 0.523; recall: 0.471; f: 0.496 \\\n",
    "VACCINATION: precision: 0.398; recall: 0.319; f: 0.354 \\\n",
    "WORKFROMHOME: precision: 0.544; recall: 0.449; f: 0.492"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Classify with one user one vote"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Optimize fastText parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python37",
   "language": "python",
   "name": "python37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}