reddit_feature_extraction.ipynb

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    "colab": {
      "name": "reddit_feature_extraction.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Hfpqoyc2yCc5"
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      "machine_shape": "hm"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fz3Q_BgwsXj9",
        "colab_type": "text"
      },
      "source": [
        "# Import of Packages for Feature Extraction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNz6oX8_sgdn",
        "colab_type": "text"
      },
      "source": [
        "The cell below allows the user to mount GDrive to access files from there."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iyrrnLSeKiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "989e4d13-4241-49fe-968c-7a781fa34727"
      },
      "source": [
        "# Mount drive at beginning to be able to access csv of post files.\n",
        "# Google Colab paths\n",
        "# # # Mount GDrive and attach it to the colab for data I/O\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# data_folder = '/content/drive/My Drive/ML4HC_Final_Project/data/input/' #when running on Google Drive\n",
        "\n",
        "# Open Science Framework path\n",
        "data_folder = './data/input/reddit_mental_health_dataset/' #if you want to re-extract, posts are here. \n",
        "output_dir = f'./data/input/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2Raopx3sp3I",
        "colab_type": "text"
      },
      "source": [
        "Import the necessary packages from installed Colab packages. If needed, install stanza and textacy as they are not automatically included in Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlIS12eT_f1z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "outputId": "7df9667b-8568-4a98-a638-0324d35de331"
      },
      "source": [
        "import string\n",
        "import os\n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "!pip install stanza textacy\n",
        "import stanza\n",
        "import textacy\n",
        "import nltk\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: cytoolz\n",
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          ],
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        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-YcYVq_y0pI",
        "colab_type": "text"
      },
      "source": [
        "Load in all of the packages related to nltk, stemmer, liwc, and sentiment analysis. This will allow us to extract the specified features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8HmYdJY_f15",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "7eb5d729-711a-4fa1-aa73-45caa2d8050e"
      },
      "source": [
        "# Do this once at the beginning so we don't reload for each post\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('stopwords')\n",
        "stanza.download('en') # download English model\n",
        "nlp = stanza.Pipeline(lang='en', processors='tokenize,ner') # nlp = stanza.Pipeline('en') # initialize English neural pipeline with entity recog NER\n",
        "\n",
        "stemmer = SnowballStemmer(language='english')\n",
        "\n",
        "input_path_liwc = '/content/drive/My Drive/ML4HC_Final_Project/data/input/liwc_english_dictionary/' #you must obtain a LIWC license to use the dictionary\n",
        "liwc_dict = np.load(input_path_liwc + 'liwc.npy', allow_pickle=True).item()\n",
        "liwc_categories = list(liwc_dict.keys())\n",
        "liwc_categories.sort()\n",
        "\n",
        "sid = SentimentIntensityAnalyzer()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.0.0.json: 115kB [00:00, 31.4MB/s]                    \n",
            "2020-06-17 19:41:43 INFO: Downloading default packages for language: en (English)...\n",
            "Downloading http://nlp.stanford.edu/software/stanza/1.0.0/en/default.zip: 100%|██████████| 402M/402M [01:19<00:00, 5.03MB/s]\n",
            "2020-06-17 19:43:11 INFO: Finished downloading models and saved to /root/stanza_resources.\n",
            "2020-06-17 19:43:11 INFO: Loading these models for language: en (English):\n",
            "=========================\n",
            "| Processor | Package   |\n",
            "-------------------------\n",
            "| tokenize  | ewt       |\n",
            "| ner       | ontonotes |\n",
            "=========================\n",
            "\n",
            "2020-06-17 19:43:12 INFO: Use device: cpu\n",
            "2020-06-17 19:43:12 INFO: Loading: tokenize\n",
            "2020-06-17 19:43:12 INFO: Loading: ner\n",
            "2020-06-17 19:43:12 INFO: Done loading processors!\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zX0Sa7U11zgG",
        "colab_type": "text"
      },
      "source": [
        "# Methods for Feature Extraction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQ_Z0j4d4YSQ",
        "colab_type": "text"
      },
      "source": [
        "The methods below are helpful for feature extraction of word count, punctuation count, and LIWC category count."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSQBujy8_f2G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Count number times a specific phrase appears in the text.\n",
        "def count_words(doc,phrases=[]):\n",
        "    # remove punctuation except apostrophes because we need to search for things like \"don't want to live\"\n",
        "    text = re.sub(\"[^\\w\\d'\\s]+\",'',doc.lower())\n",
        "    counter = 0\n",
        "    for phrase in phrases:\n",
        "      counter += text.count(phrase)\n",
        "    return counter\n",
        "\n",
        "# Count number of times punctuation appears in the text\n",
        "def punctuation_count(doc):\n",
        "    d = {}\n",
        "    punctuation = '!\"#$%&()*+,-./:;=?@[\\]^`{|}~' #removed ', <>, _ because m_c_a_t doesnt reflect punctuation really\n",
        "    for c in doc:\n",
        "        if c in punctuation:\n",
        "            if c not in d:\n",
        "                d[c] = 0\n",
        "            d[c] += 1\n",
        "    total = np.sum(list(d.values()))\n",
        "    return total\n",
        "\n",
        "# Count the number of words that belong to LIWC categories.\n",
        "def liwc(liwc_path=None, document=None):\n",
        "    liwc_vector = []\n",
        "    # tokenize and stem\n",
        "    document_tokenized = [n.strip(string.punctuation).lower() for n in document.split()]\n",
        "    document_stemmed = [stemmer.stem(word) for word in document_tokenized]\n",
        "    for category in liwc_categories:\n",
        "        counter  = 0\n",
        "        # for each word in category, check if its in stemmed sentence list\n",
        "        counter_doc = np.sum([sum(word.rstrip()==s for s in document_stemmed) for word in liwc_dict.get(category)])\n",
        "        counter += counter_doc\n",
        "        liwc_vector.append(counter)\n",
        "    \n",
        "    return liwc_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1EwINMn4fZr",
        "colab_type": "text"
      },
      "source": [
        "The method below will create a TFIDF for train and test sets of documents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IiPugC7M_f2I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tfidf(X_train_sentences = [], X_test_sentences=[], lower_case = True, ngram_range = (1,2), max_features=512, min_df=2, max_df=0.8, model = 'vector'):\n",
        "    \"\"\"\n",
        "    TfidfVectorizer is CountVectorizer followed by TfidfTransformer, The former converts text documents to a sparse matrix of token counts.\n",
        "    This sparse matrix is then put through the TfidfTransformer which converts a count matrix to a normalized Term Frequency-Inverse Document Frquency(tf)  \n",
        "    representation which is a metric of word importance. \n",
        "    We fit_transform on train_sentences and transform on test sentences to prevent overfitting, X_test_sentences can be None\n",
        "\n",
        "    Token pattern can be included to include apostophes: token_pattern=r\"\\b\\w[\\w']+\\b\"\n",
        "    \n",
        "    model: {vector, sequential} depending on what model takes as input: vector (svm, random forest), sequential (lstm)\n",
        "    \"\"\"\n",
        "    sw = stopwords.words('english')\n",
        "\n",
        "    if model == 'sequential':\n",
        "        #         \n",
        "        vectorizer = TfidfVectorizer(lowercase=lower_case, ngram_range=ngram_range, stop_words=sw,\n",
        "                                     max_features=max_features, min_df=min_df, max_df=max_df, analyzer=lambda x: x)\n",
        "        train_vectors = vectorizer.fit_transform(X_train_sentences).toarray()\n",
        "        if X_test_sentences:\n",
        "            test_vectors = vectorizer.transform(X_test_sentences).toarray()\n",
        "\n",
        "    else:\n",
        "        # model = 'vector'         \n",
        "        vectorizer = TfidfVectorizer(lowercase=lower_case, ngram_range=ngram_range, stop_words=sw,\n",
        "                                     max_features=max_features, min_df=min_df, max_df=max_df)\n",
        "        train_vectors = vectorizer.fit_transform(X_train_sentences).toarray()\n",
        "        if X_test_sentences:\n",
        "            test_vectors = vectorizer.transform(X_test_sentences).toarray()\n",
        "    # train_vectors = vectorizer.fit_transform(X_train_sentences.ravel()).toarray()\n",
        "    # test_vectors = vectorizer.transform(X_test_sentences.ravel()).toarray()\n",
        "    feature_names = vectorizer.get_feature_names()\n",
        "    feature_names = ['tfidf_'+n for n in feature_names]\n",
        "    if X_test_sentences:\n",
        "        return train_vectors, test_vectors, feature_names\n",
        "    else:\n",
        "        return train_vectors, feature_names, vectorizer\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7chi1lf54v2Y",
        "colab_type": "text"
      },
      "source": [
        "The method below lists the feature names based on the list of features we are trying to extract."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhCz9LXVv8In",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_NLP_features_names(features):\n",
        "    feature_names  = []\n",
        "\n",
        "    if  'readability' in features:\n",
        "      feature_names.append(['automated_readability_index', 'coleman_liau_index', 'flesch_kincaid_grade_level', 'flesch_reading_ease', \n",
        "                            'gulpease_index', 'gunning_fog_index', 'lix', 'smog_index', 'wiener_sachtextformel'])\n",
        "\n",
        "    if 'basic_count' in features:\n",
        "      #todo: should we be worried \n",
        "      feature_names.append(['n_chars', 'n_long_words', 'n_monosyllable_words', 'n_polysyllable_words', 'n_sents', 'n_syllables', 'n_unique_words', 'n_words'])\n",
        "\n",
        "    \n",
        "    if 'sentiment' in features:\n",
        "        names = ['sent_neg','sent_neu', 'sent_pos', 'sent_compound']\n",
        "        feature_names.append(names)\n",
        "        \n",
        "    if 'covid19' in features:\n",
        "        feature_names.append(['covid19_total'])\n",
        "\n",
        "    if 'economic_stress' in features:\n",
        "        feature_names.append(['economic_stress_total'])\n",
        "\n",
        "    if 'isolation' in features:\n",
        "        feature_names.append(['isolation_total'])\n",
        "\n",
        "    if 'substance_use' in features:\n",
        "        feature_names.append(['substance_use_total'])\n",
        "\n",
        "    if 'guns' in features:\n",
        "        feature_names.append(['guns_total' ])\n",
        "\n",
        "    if 'domestic_stress' in features:\n",
        "        feature_names.append(['domestic_stress_total' ])\n",
        "\n",
        "    if 'suicidality' in features:\n",
        "        feature_names.append(['suicidality_total'])\n",
        "        \n",
        "    if 'punctuation' in features:\n",
        "        feature_names.append(['punctuation'])\n",
        "        \n",
        "    if 'liwc' in features:\n",
        "        liwc_names = ['liwc_'+n for n in liwc_categories]\n",
        "        feature_names.append(liwc_names)\n",
        "\n",
        "    feature_names = [label for item in feature_names for label in item]\n",
        "    return feature_names\n",
        "\n",
        "\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ttO8xrk46wb",
        "colab_type": "text"
      },
      "source": [
        "The method below extracts out a feature vector for a specified post based on the features that have been specified."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YCgghse_f2L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def extract_NLP_features(doc, features):\n",
        "    feature_vector = []\n",
        "\n",
        "    if 'basic_count' or  'readability' in features:\n",
        "        doc_ts = textacy.make_spacy_doc(doc, lang='en')\n",
        "        ts = textacy.TextStats(doc_ts)\n",
        "\n",
        "        if  'readability' in features:\n",
        "          '''\n",
        "          described here: https://chartbeat-labs.github.io/textacy/build/html/api_reference/misc.html\n",
        "          {'flesch_kincaid_grade_level': 15.56027397260274,\n",
        "          'flesch_reading_ease': 26.84351598173518,\n",
        "          'smog_index': 17.5058628484301,\n",
        "          'gunning_fog_index': 20.144292237442922,\n",
        "          'coleman_liau_index': 16.32928468493151,\n",
        "          'automated_readability_index': 17.448173515981736,\n",
        "          'lix': 65.42922374429223,\n",
        "          'gulpease_index': 44.61643835616438,\n",
        "          'wiener_sachtextformel': 11.857779908675797}\n",
        "          '''\n",
        "          try:\n",
        "            dictionary = ts.readability_stats\n",
        "            v_sorted = dict(sorted(dictionary.items())).values()\n",
        "            feature_vector.append(v_sorted)\n",
        "          except:\n",
        "            return []\n",
        "\n",
        "        if 'basic_count' in features:\n",
        "          # https://chartbeat-labs.github.io/textacy/build/html/getting_started/quickstart.html\n",
        "          '''\n",
        "          {'n_sents': 3,\n",
        "          'n_words': 73,\n",
        "          'n_chars': 414,\n",
        "          'n_syllables': 134,\n",
        "          'n_unique_words': 57,\n",
        "          'n_long_words': 30,\n",
        "          'n_monosyllable_words': 38,\n",
        "          'n_polysyllable_words': 19}\n",
        "          '''\n",
        "          try:\n",
        "            dictionary = ts.basic_counts\n",
        "            v_sorted = dict(sorted(dictionary.items())).values() #just in case it outputs the values in a diff order each time\n",
        "            feature_vector.append(v_sorted)\n",
        "          except:\n",
        "            return []\n",
        "\n",
        "    \n",
        "    if 'sentiment' in features:\n",
        "        # don't lowercase or remove punctuation, but maybe preprocess emojis     \n",
        "        scores = sid.polarity_scores(doc)\n",
        "        names = ['sent_neg','sent_neu', 'sent_pos', 'sent_compound']\n",
        "        feature_vector.append(scores.values())\n",
        "        feature_names.append(names)\n",
        "        \n",
        "    if 'covid19' in features:\n",
        "        words = ['corona','virus','viral','covid', 'sars','influenza','pandemic', 'epidemic', \n",
        "         'quarantine','lockdown', 'distancing', 'national emergency', 'flatten', \n",
        "             'infect','ventilator', 'mask','symptomatic',\n",
        "            'epidemiolog', 'immun', 'incubation', 'transmission','vaccine', ]\n",
        "\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['covid19_total'])\n",
        "\n",
        "\n",
        "    if 'economic_stress' in features:\n",
        "      # Double check that words like \"rent\" aren't capturing too many other words like \"parent\". If so replace \"rent\" with \"pay rent\" and variations\n",
        "        words = ['unemploy','employ', 'economy','rent', 'mortgage', 'evict',  \"enough money\", \"more money\", \n",
        "                 'pay the bills', 'owe', 'debt', \"make ends meet\", \"pay the bills\", \"afford\", \"save enough\", \n",
        "                 'salary', 'wage', 'income', 'job', 'eviction']\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['economic_stress_total'])\n",
        "\n",
        "    if 'isolation' in features:\n",
        "        words=['alone', 'lonely','no one cares about me','no one cares', \"can't see anyone\", \n",
        "               \"can't see my\", \"i miss my\", \"i want to see my\", \"trapped\", \"i'm in a cage\",\n",
        "              'lonely', \"feel ignored\",\"ignoring me\" \"ugly\", 'rejected', \"avoid\", 'avoiding me', \" lack of social\",\n",
        "               'am single', 'been single', 'quarantine', 'lockdown', 'isolation', 'self-isolation']\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['isolation_total'])\n",
        "\n",
        "    if 'substance_use' in features:\n",
        "        words = ['smoke', 'cigarette', 'tobacco','wine','drink', 'beer', 'alcohol', 'drug', 'opioid', \n",
        "                 'cocaine','snort', 'vodka', 'whiskey', 'whisky', 'tequila', 'meth'] #(remove words like drug that may refer to when people are talking about possible covid19 drugs?)\n",
        "        # we could include commonly abused prescription drugs but this would also capture healthy/prescribed use: https://www.mayoclinic.org/diseases-conditions/prescription-drug-abuse/symptoms-causes/syc-20376813\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['substance_use_total'])\n",
        "\n",
        "    if 'guns' in features:\n",
        "        words = ['gun', 'pistol', 'revolver', 'semiautomatic', 'rifle', 'shoot', 'firearm', 'semi-automatic']\n",
        "        counter  = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['guns_total'])\n",
        "\n",
        "\n",
        "    if 'domestic_stress' in features:\n",
        "        words = ['divorce','domestic violence', 'abuse', 'yelling','fighting with my',\n",
        "                 \"we're fighting\",  'single mom', 'single dad', 'single parent', 'hit me', \n",
        "                 'slapped me', 'fighting', 'fight']\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter ])\n",
        "        feature_names.append(['domestic_stress_total' ])\n",
        "\n",
        "\n",
        "    if 'suicidality' in features:\n",
        "        words = ['commit suicide', 'jump off a bridge', 'I want to overdose',\"i'm a burden\",\n",
        "                 \"i'm such a burden\", 'I will overdose', 'thinking about overdose', 'kill myself', \n",
        "                 'killing myself', 'hang myself', 'hanging myself', 'cut myself', 'cutting myself', \n",
        "                 'hurt myself', 'hurting myself', 'want to die', 'wanna die', \"don't want to wake up\", \n",
        "                 \"don't wake up\", 'never want to wake up', \"don't want to be alive\", 'want to be alive anymore', \n",
        "                 'wish it would all end', 'done with living', 'want it to end', 'it all ends tonight', 'end my life', \n",
        "                 'live anymore', 'living anymore', 'life anymore', 'be dead', 'take it anymore', 'think about death', \n",
        "                 'hopeless', 'hurt myself', \"no one will miss me\", \"don't want to wake up\", \n",
        "                 'if I live or die', 'i hate my life', 'shoot me', 'kill me', 'suicide',\n",
        "                 'no point']\n",
        "        counter = count_words(doc,phrases=words)\n",
        "        feature_vector.append([counter])\n",
        "        feature_names.append(['suicidality_total'])\n",
        "        \n",
        "    \n",
        "    if 'punctuation' in features:\n",
        "        count = punctuation_count(doc)\n",
        "        feature_vector.append([count])\n",
        "        feature_names.append(['punctuation'])\n",
        "        \n",
        "    if 'liwc' in features:\n",
        "        vector = liwc(liwc_path = None, document = doc)\n",
        "        feature_vector.append(vector)\n",
        "        # feature_names.append(names)\n",
        "        \n",
        "    \n",
        "        \n",
        "    feature_vector = [n for i in feature_vector for n in i]\n",
        "    return feature_vector\n",
        "\n",
        "\n",
        "\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GwNcIBXx-Kt",
        "colab_type": "text"
      },
      "source": [
        "# Extract Out Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8n1yHynY5CFZ",
        "colab_type": "text"
      },
      "source": [
        "The script below extracts out the features for sentiment, punctuation, LIWC, count based on textacy, readability features based on textacy, and counts based on the lexicons we have developed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pag0UMDI_f2O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = ['sentiment', 'punctuation', 'liwc', 'basic_count', 'readability', 'isolation', 'suicidality', 'economic_stress', 'substance_use', 'guns', 'domestic_stress']\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/raw_post/'\n",
        "output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/'\n",
        "files  = os.listdir(input_dir)\n",
        "files = [ 'depression_2019.csv',\n",
        " 'psychosis_2019.csv',\n",
        " 'anxiety_2019.csv']\n",
        "for file in files:\n",
        "    df_subreddit = pd.read_csv(input_dir + file)\n",
        "    print(df_subreddit.head())\n",
        "    \n",
        "    #pre-process to remove duplicate posters, any bots, and posts that have the word quote\n",
        "    df_subreddit = df_subreddit.drop_duplicates(subset=\"author\")\n",
        "    df_subreddit = df_subreddit[~df_subreddit.post.str.contains('(?i)bot')]\n",
        "    df_subreddit = df_subreddit[~df_subreddit.post.str.contains('(?i)quote')]\n",
        "    \n",
        "    #process all posts\n",
        "    posts = list(df_subreddit.post)\n",
        "    posts = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in posts]\n",
        "\n",
        "    feature_names = extract_NLP_features_names(features)\n",
        "    full_column_names = list(df_subreddit.columns) + feature_names\n",
        "    subreddit_features = pd.DataFrame(columns=full_column_names)\n",
        "\n",
        "    for pi in range(len(posts)):\n",
        "      post = posts[pi]\n",
        "      feature_vector = extract_NLP_features(post, features) #removed feature_names from output\n",
        "      if len(feature_vector) != 0:\n",
        "        raw_series = list(df_subreddit.iloc[pi])\n",
        "        subreddit_features = subreddit_features.append(pd.Series(raw_series + feature_vector, index=full_column_names), ignore_index=True)  \n",
        "\n",
        "    subreddit_features.to_csv(os.path.join(output_dir, os.path.splitext(file)[0] + \"_features.csv\"), index=False)       \n",
        "\n",
        "\n",
        "    \n",
        "        \n",
        "        \n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJExZVD_5Rrr",
        "colab_type": "text"
      },
      "source": [
        "The script below extracts out word counts for the topics we are interested in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeGF8ghU5Qpx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = ['covid19', 'isolation', 'suicidality', 'economic_stress', 'substance_use', 'guns', 'domestic_stress']\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/raw_post/'\n",
        "output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/phrases_folder'\n",
        "files  = os.listdir(input_dir)\n",
        "\n",
        "files = [f for f in files if '_pre.csv' in f]\n",
        "for file in files:\n",
        "    df_subreddit = pd.read_csv(input_dir + file)\n",
        "    print(df_subreddit.head())\n",
        "    posts = list(df_subreddit.post)\n",
        "    posts = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in posts] \n",
        "\n",
        "    feature_names = extract_NLP_features_names(features)\n",
        "    full_column_names = list(df_subreddit.columns) + feature_names\n",
        "    subreddit_features = pd.DataFrame(columns=full_column_names)\n",
        "\n",
        "    for pi in range(len(posts)):\n",
        "      post = posts[pi]\n",
        "      feature_vector = extract_NLP_features(post, features)\n",
        "      raw_series = list(df_subreddit.iloc[pi])\n",
        "      subreddit_features = subreddit_features.append(pd.Series(raw_series + feature_vector, index=full_column_names), ignore_index=True)  \n",
        "\n",
        "    subreddit_features.to_csv(os.path.join(output_dir, os.path.splitext(file)[0] + \"_phrases.csv\"), index=False)      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYjUK4mA6EiF",
        "colab_type": "text"
      },
      "source": [
        "The script below extracts out TF-IDF vectors for training and test data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqhXGPOM6D-D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = ['tfidf']\n",
        "\n",
        "# input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/raw_post/'\n",
        "# output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/tfidf_vector/'\n",
        "files  = os.listdir(input_dir)\n",
        "files = [f for f in files if '.csv' in f]\n",
        "end_date = datetime.datetime(2020, 1, 15)\n",
        "\n",
        "all_posts = pd.read_csv(input_dir + files[0], index_col=False)\n",
        "all_posts = all_posts[~all_posts['post'].str.contains(\"deleted\")]\n",
        "all_posts['post_stemmed'] = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in all_posts['post']]\n",
        "all_posts['post_stemmed'] = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in all_posts['post_stemmed']]\n",
        "print(all_posts.shape)\n",
        "\n",
        "for file in files[1:]:\n",
        "    df_subreddit = pd.read_csv(input_dir + file, index_col=False);\n",
        "    df_subreddit['post_stemmed'] = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in df_subreddit['post']]\n",
        "    df_subreddit['post_stemmed'] = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in df_subreddit['post_stemmed']]\n",
        "    print(df_subreddit.shape)\n",
        "    all_posts = pd.concat([all_posts, df_subreddit], axis=0)\n",
        "    print(all_posts.shape)\n",
        "\n",
        "all_posts.head()\n",
        "\n",
        "all_posts['date'] = pd.to_datetime(all_posts['date'], format=\"%Y/%m/%d\")\n",
        "\n",
        "all_posts_training = list(all_posts['post_stemmed'].loc[all_posts['date'] <= end_date])\n",
        "\n",
        "all_posts_testing = list(all_posts['post_stemmed'].loc[all_posts['date'] > end_date])\n",
        "    \n",
        "'''\n",
        "tfidf is done with the whole corpus, not for each sentence, or with the whole train set (if we're doing a split, which we may not be for cluster analysis). \n",
        "\n",
        "play with the params a bit to figure out how many max_features so that it's not too sparse and captures important words per subreddit. \n",
        "'''\n",
        "train_vectors, test_vectors, features = tfidf(X_train_sentences = all_posts_training, X_test_sentences=all_posts_testing, lower_case = True, \n",
        "                                              ngram_range = (1,2), max_features=None, min_df=2, max_df=0.8, model = 'vector')\n",
        "\n",
        "all_posts_new = all_posts\n",
        "all_posts_new = all_posts_new.reindex(all_posts_new.columns.tolist() + features, axis=1)\n",
        "all_posts_new.loc[all_posts['date'] <= end_date,features] = train_vectors\n",
        "all_posts_new.loc[all_posts['date'] > end_date, features] = test_vectors\n",
        "\n",
        "all_posts_new.to_csv(os.path.join(output_dir, \"tfidf_test.csv\"), index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8j7bMkpA5ana",
        "colab_type": "text"
      },
      "source": [
        "The two cells below will do TFIDF on the pre-pandemic mental health subreddits, and then apply that vectorizer to the entire dataset and to individual subreddits both pre- and mid-pandemic.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "evEtQcaj5W5M",
        "colab": {}
      },
      "source": [
        "features = ['tfidf']\n",
        "min_posts = 2720 # from bipolarreddit\n",
        "feats = [256, 512, 1024]\n",
        "\n",
        "for feat in feats:\n",
        "  # input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new'\n",
        "\n",
        "  files = ['suicidewatch_pre_features.csv',\n",
        "          'depression_pre_features.csv',\n",
        "          'ptsd_pre_features.csv',\n",
        "          'anxiety_pre_features.csv',\n",
        "          'socialanxiety_pre_features.csv',\n",
        "          'healthanxiety_pre_features.csv',\n",
        "          'bipolarreddit_pre_features.csv',\n",
        "          'bpd_pre_features.csv',\n",
        "          'schizophrenia_pre_features.csv',\n",
        "          'EDAnonymous_pre_features.csv',\n",
        "          'alcoholism_pre_features.csv',\n",
        "          'addiction_pre_features.csv',\n",
        "          'adhd_pre_features.csv']\n",
        "\n",
        "  # gather all posts from pre_pandemic\n",
        "  all_posts = pd.read_csv(os.path.join(input_dir, files[0]), index_col=False)\n",
        "  all_posts = all_posts.sample(min_posts)\n",
        "  all_posts['post_stemmed'] = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in all_posts['post']]\n",
        "  all_posts['post_stemmed'] = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in all_posts['post_stemmed']]\n",
        "  print(all_posts.shape)\n",
        "\n",
        "  for file in files[1:]:\n",
        "      df_subreddit = pd.read_csv(os.path.join(input_dir, file), index_col=False);\n",
        "      df_subreddit = df_subreddit.sample(min_posts)\n",
        "      df_subreddit['post_stemmed'] = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in df_subreddit['post']]\n",
        "      df_subreddit['post_stemmed'] = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in df_subreddit['post_stemmed']]\n",
        "      all_posts = pd.concat([all_posts, df_subreddit], axis=0)\n",
        "      print(all_posts.shape)\n",
        "\n",
        "  all_posts.head()\n",
        "\n",
        "  posts = list(all_posts.post_stemmed)\n",
        "\n",
        "  train_vectors, features, vectorizer = tfidf(X_train_sentences = posts, lower_case = True, \n",
        "                                  ngram_range = (1,2), max_features=feat, min_df=0.001, max_df=0.75, model = 'vector')\n",
        "\n",
        "  # input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "  # output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/tfidf_combined'\n",
        "  files  = os.listdir(input_dir)\n",
        "\n",
        "  files = [f for f in files if '.csv' in f]\n",
        "  for file in files:\n",
        "      df_subreddit = pd.read_csv(os.path.join(input_dir + file))\n",
        "      print(df_subreddit.head())\n",
        "      posts = list(df_subreddit.post)\n",
        "      if len(posts) == 0:\n",
        "        continue\n",
        "      posts = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in posts] \n",
        "      posts = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in posts]\n",
        "\n",
        "      tfidf_posts = vectorizer.transform(posts).toarray()\n",
        "      df_subreddit_new = df_subreddit\n",
        "      df_subreddit_new = df_subreddit_new.reindex(df_subreddit_new.columns.tolist() + features, axis=1)\n",
        "      df_subreddit_new.loc[:, features] = tfidf_posts\n",
        "\n",
        "      df_subreddit_new.to_csv(os.path.join(output_dir, os.path.splitext(file)[0] + \"_tfidf_{}.csv\".format(feat)), index=False)\n",
        "  \n",
        "  import pickle\n",
        "  pickle.dump(vectorizer.vocabulary_,open(os.path.join(\"/content/drive/My Drive/ML4HC_Final_Project/data/input/lda_models\", \"vec_{}.pkl\".format(feat)),\"wb\"))   "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrsiK8_ewKMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "# output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/tfidf_combined'\n",
        "files  = os.listdir(input_dir)\n",
        "\n",
        "files = [f for f in files if '.csv' in f]\n",
        "for file in files:\n",
        "    df_subreddit = pd.read_csv(os.path.join(input_dir + file))\n",
        "    print(df_subreddit.head())\n",
        "    posts = list(df_subreddit.post)\n",
        "    if len(posts) == 0:\n",
        "      continue\n",
        "    posts = [post.replace('\\n', ' ').replace('  ',' ').replace('“', '').replace('”', '') for post in posts] \n",
        "    posts = [' '.join([stemmer.stem(word.strip(string.punctuation).lower()) for word in p.split()]) for p in posts]\n",
        "\n",
        "    tfidf_posts = vectorizer.transform(posts).toarray()\n",
        "    df_subreddit_new = df_subreddit\n",
        "    df_subreddit_new = df_subreddit_new.reindex(df_subreddit_new.columns.tolist() + features, axis=1)\n",
        "    df_subreddit_new.loc[:, features] = tfidf_posts\n",
        "\n",
        "    df_subreddit_new.to_csv(os.path.join(output_dir, os.path.splitext(file)[0] + \"_tfidf_all.csv\"), index=False)      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWixVHgn5xQO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save vectorizer\n",
        "import pickle\n",
        "pickle.dump(vectorizer.vocabulary_,open(os.path.join(\"/content/drive/My Drive/ML4HC_Final_Project/data/input/lda_models\", \"vec_all.pkl\"),\"wb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiARb2qi3bwk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "76243585-897e-45a2-d13b-c844f02ace2c"
      },
      "source": [
        "file"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'depression_2018.csv'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqW-VLP6zUuK",
        "colab_type": "text"
      },
      "source": [
        "# Create Histograms of Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNJY538JTEA_",
        "colab_type": "text"
      },
      "source": [
        "The following two cells create the histograms for the number of posts that are present in each subreddit in the pre- and mid-pandemic timeframes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pod74Zn270sg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "2cd03be9-5b86-4637-d1fc-949c162eaa82"
      },
      "source": [
        "files = ['suicidewatch_pre_features.csv',\n",
        " 'depression_pre_features.csv',\n",
        " 'ptsd_pre_features.csv',\n",
        " 'anxiety_pre_features.csv',\n",
        " 'socialanxiety_pre_features.csv',\n",
        " 'healthanxiety_pre_features.csv',\n",
        " 'bipolarreddit_pre_features.csv',\n",
        " 'bpd_pre_features.csv',\n",
        " 'schizophrenia_pre_features.csv',\n",
        " 'EDAnonymous_pre_features.csv',\n",
        " 'alcoholism_pre_features.csv',\n",
        " 'addiction_pre_features.csv',\n",
        " 'adhd_pre_features.csv',\n",
        " 'mentalhealth_pre_features.csv',\n",
        " 'meditation_pre_features.csv',\n",
        " 'personalfinance_pre_features.csv',\n",
        " 'teaching_pre_features.csv',\n",
        " 'relationships_pre_features.csv',\n",
        " 'legaladvice_pre_features.csv',\n",
        " 'fitness_pre_features.csv',\n",
        " 'parenting_pre_features.csv',\n",
        " 'lonely_pre_features.csv',\n",
        " 'autism_pre_features.csv',\n",
        " 'divorce_pre_features.csv',\n",
        " 'conspiracy_pre_features.csv',\n",
        " 'guns_pre_features.csv',\n",
        " 'jokes_pre_features.csv'\n",
        "]\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "# files  = os.listdir(input_dir)\n",
        "# files = [f for f in files if '.csv' in f]\n",
        "\n",
        "file_count_dict = {}\n",
        "for file in files:\n",
        "    df_subreddit = pd.read_csv(input_dir + file, index_col=False);\n",
        "    file_count_dict[os.path.basename(file).split('_')[0]] = df_subreddit.shape[0]\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "# file_count_dict['COVID19_support'] = file_count_dict['COVID19\\_support']\n",
        "# file_count_dict.pop('COVID19\\_support', None)\n",
        "sorted_values = [x for _,x in sorted(zip(file_count_dict.keys(),file_count_dict.values()))]\n",
        "plt.bar(sorted(file_count_dict.keys()), sorted_values)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Pre-Pandemic No. of Posts')\n",
        "plt.xlabel('Subreddits')\n",
        "plt.ylabel('No. of Posts')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rnh5gp4buNK8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "0dc2f97c-5e93-43ff-c4d5-2e0eb449de52"
      },
      "source": [
        "files = ['suicidewatch_post_features.csv',\n",
        " 'depression_post_features.csv',\n",
        " 'ptsd_post_features.csv',\n",
        " 'anxiety_post_features.csv',\n",
        " 'socialanxiety_post_features.csv',\n",
        " 'healthanxiety_post_features.csv',\n",
        " 'bipolarreddit_post_features.csv',\n",
        " 'bpd_post_features.csv',\n",
        " 'schizophrenia_post_features.csv',\n",
        " 'EDAnonymous_post_features.csv',\n",
        " 'alcoholism_post_features.csv',\n",
        " 'addiction_post_features.csv',\n",
        " 'adhd_post_features.csv',\n",
        " 'mentalhealth_post_features.csv',\n",
        " 'meditation_post_features.csv',\n",
        " 'personalfinance_post_features.csv',\n",
        " 'teaching_post_features.csv',\n",
        " 'relationships_post_features.csv',\n",
        " 'legaladvice_post_features.csv',\n",
        " 'fitness_post_features.csv',\n",
        " 'parenting_post_features.csv',\n",
        " 'lonely_post_features.csv',\n",
        " 'autism_post_features.csv',\n",
        " 'divorce_post_features.csv',\n",
        " 'conspiracy_post_features.csv',\n",
        " 'COVID19_support_post_features.csv',\n",
        " 'guns_post_features.csv',\n",
        " 'jokes_post_features.csv'\n",
        "]\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "# files  = os.listdir(input_dir)\n",
        "# files = [f for f in files if '.csv' in f]\n",
        "\n",
        "start_date = datetime.datetime(2020, 3, 11)\n",
        "\n",
        "file_count_dict = {}\n",
        "for file in files:\n",
        "    df_subreddit = pd.read_csv(input_dir + file, index_col=False);\n",
        "    # df_subreddit['date'] = pd.to_datetime(df_subreddit['date'], format=\"%Y/%m/%d\")\n",
        "    # df_subreddit = df_subreddit.loc[df_subreddit['date'] >= start_date]\n",
        "    file_count_dict[os.path.basename(file).split('_')[0]] = df_subreddit.shape[0]\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "file_count_dict['COVID19_support'] = file_count_dict['COVID19']\n",
        "file_count_dict.pop('COVID19', None)\n",
        "sorted_values = [x for _,x in sorted(zip(file_count_dict.keys(),file_count_dict.values()))]\n",
        "plt.bar(sorted(file_count_dict.keys()), sorted_values)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Mid-Pandemic No. of Posts (01/01/2020-04/20/2020)')\n",
        "plt.xlabel('Subreddits')\n",
        "plt.ylabel('No. of Posts')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8I5LG5tLXTvlPSeeqk/cTBEEQdGcgeiJbmNkaZjY67R8MXG9mKwDXp32A7YAV0rYPcAq40gEOB9YD1gUOzxRPkvli7rxtO387QRAEQcbMMGftDIxLv8cBH82ln23O7cBwSUsA2wDXmtkLZvYicC2wbTo2zMxuNzMDzs7lFQRBEAwAnVYiBvxJ0kRJ+6S0xc3smfT7WWDx9Hsp4MncuU+ltFbpT5Wk90DSPpImSJowderUvtxPEARBkGPODue/sZk9Lek9wLWSHsofNDOTZB0uA2Z2KnAqwOjRozt+vSAIgtmFjvZEzOzp9Pd54A/4mMZzyRRF+vt8En8aWDp3+siU1ip9ZEl6EARBMEB0TIlIWkDSgtlvYGvgb8BlQOZhNRa4NP2+DNgzeWmtD7yczF7jga0lLZwG1LcGxqdj0yStn7yy9szlFQRBEAwAnTRnLQ78IXndzgmcZ2bXSLoLuEjS3sATwG5J/ipgDDAFeA3YC8DMXpB0NHBXkjvKzF5Iv78CnAXMB1ydtiAIgmCA6JgSMbNHgdVL0v8DbFWSbsB+FXmdAZxRkj4BWLXPhQ2CIAgaETPWgyAIgsaEEgmCIAgaE0okCIIgaEwokSAIgqAxoUSCIAiCxoQSCYIgCBoTSiQIgiBoTCiRIAiCoDGhRIIgCILGhBIJgiAIGhNKJAiCIGhMKJEgCIKgMaFEgiAIgsaEEgmCIAgaE0okCIIgaEwokSAIgqAxoUSCIAiCxoQSCYIgCBoTSiQIgiBoTCiRIAiCoDGhRIIgCILGhBIJgiAIGhNKJAiCIGhMKJEgCIKgMaFEgiAIgsaEEgmCIAgaE0okCIIgaEwokSAIgqAxHVcikoZImiTpirS/nKQ7JE2RdKGkuVP6PGl/Sjo+KpfHISn9YUnb5NK3TWlTJB3c6XsJgiAIujMQPZEDgQdz+8cBPzez9wMvAnun9L2BF1P6z5McklYGPgWsAmwLnJwU0xDgV8B2wMrA7kk2CIIgGCA6qkQkjQS2B05L+wK2BH6fRMYBH02/d077pONbJfmdgQvM7E0zewyYAqybtilm9qiZvQVckGSDIAiCAaLTPZFfAN8G3kn7iwIvmdnbaf8pYKn0eyngSYB0/OUkPyO9cE5Veg8k7SNpgqQJU6dO7es9BUEQBImOKRFJOwDPm9nETl2jLmZ2qpmNNrPRI0aMmNnFCYIgeNcwZwfz3gjYSdIYYF5gGHACMFzSnKm3MRJ4Osk/DSwNPCVpTmAh4D+59Iz8OVXpQRAEwQDQsZ6ImR1iZiPNbBQ+MP5nM9sDuAHYJYmNBS5Nvy9L+6TjfzYzS+mfSt5bywErAHcCdwErJG+vudM1LuvU/QRBEAQ96WRPpIrvABdIOgaYBJye0k8HzpE0BXgBVwqY2f2SLgIeAN4G9jOz/wFI2h8YDwwBzjCz+wf0ToIgCGZzBkSJmNmNwI3p96O4Z1VR5g1g14rzfwD8oCT9KuCqfixqEARB0AYxYz0IgiBozMwwZwXBgDHq4Ct7lXn82O0HoCRB8O4keiJBEARBY0KJBEEQBI3p1Zwl6cfAMcDrwDXAasDXzezcDpctCIKgV3ozWYa5srPU6YlsbWbTgB2Ax4H3A9/qZKGCIAiCwUEdJTJX+rs98Dsze7mD5QmCIAgGEXW8sy6X9BBuztpX0gjgjc4WKwiCIBgM1OmJHA5sCIw2s/8CrwE7dbRUQRAEwaCgjhK5zcxeyEKNmNmrwNWdLVYQBEEwGKg0Z0l6L74+x3yS1gSUDg0D5h+AsgVBEASzOK3GRLYBPoeHWD8+lz4d+G4HyxQEQRAMEiqViJmNA8ZJ+oSZXTyAZQqCIAgGCXXGREZKGibnNEl3S9q64yULgiAIZnnqKJHPp8mGW+Nrnn8WOLajpQqCIAgGBXWUSDagPgY4Oy38pBbyQRAEwWxCHSUyUdKfcCUyXtKCwDudLVYQBEEwGKgzY31vYA3gUTN7TdKiwF6dLVYQBEEwGOhViZjZO5JGAp+WBHCTmV3e8ZIFQRAEszy9mrMkHQscCDyQtgMk/bDTBQuCIAhmfeqYs8YAa5jZOwCSxgGTiAmHQRAEsz11VzYcnvu9UCcKEgRBEAw+6vREfgRMknQD7tq7KXBwR0sVBEEQDArqKJFrgZuA0Wn/O2b2bOeKFARBEAwWKs1ZknaUNBWYDNwJ/MfMLgsFEgRBEGS0GhP5AbCJmS0BfAIIj6wgCIKgG62UyNtm9hCAmd0BLDgwRQqCIAgGC62UyHskHZRtJfstkTSvpDsl3SvpfklHpvTlJN0haYqkCyXNndLnSftT0vFRubwOSekPS9oml75tSpsiKQb7gyAIBphWSuT/8N5HthX3e+NNYEszWx0Pm7KtpPWB44Cfm9n7gRfxsCqkvy+m9J8nOSStDHwKWAXYFjhZ0hBJQ4BfAdsBKwO7J9kgCIJggGi1KNWRfcnYzAx4Je3OlTYDtgQ+ndLHAUcApwA7p98Avwd+KY+zsjNwgZm9CTwmaQqwbpKbYmaPAki6IMk+0JdyB0EQBPWpO9mwEanHcA/wPO4q/A/gJTN7O4k8ha/jTvr7JEA6/jK+fsmM9MI5Vell5dhH0gRJE6ZOndoftxYEQRDQYSViZv8zszXwddrXBVbq5PValONUMxttZqNHjBgxM4oQBEHwrqTVPJED09+N+noRM3sJuAHYABguKTOjjQSeTr+fBpZO15wTD6/yn3x64Zyq9CAIgmCAaNUTydYMOalJxpJGSBqefs8HfAR4EFcmuySxscCl6fdlaZ90/M9pXOUy4FPJe2s5YAV88uNdwArJ22tufPD9siZlDYIgCJrRKuzJg5IeAZaUdF8uXfi4+Wq95L0EMC55Uc0BXGRmV0h6ALhA0jF4NODTk/zpwDlp4PwFXClgZvdLuggfMH8b2M/M/gcgaX9gPDAEOCMt3RsEQRAMEK28s3aX9F68kt6p3YzN7D5gzZL0R+nyrsqnvwHsWpHXD/AZ9MX0q4Cr2i1bEARB0D+0DMCY4mStnsxFK6bkh83svx0vWRAEQTDL02sUX0mbAWcDj+OmrKUljTWzmztctiAIgmAWp04o+OOBrc3sYQBJKwLnA2t3smBBEATBrE+deSJzZQoEwMz+js8+D4IgCGZz6vREJkg6DTg37e8BTOhckYIgCILBQh0lsi+wH3BA2v8LcHLHShQEQS1GHXxlrzKPH7v9AJQkmJ3pVYmkwIfHpy0IgiAIZtDR2FlBEATBu5tQIkEQBEFjQokEQRAEjWmkRCTt098FCYIgCAYfTXsi6tdSBEEQBIOSRkrEzH7T3wUJgiAIBh+9KhFJIyX9QdJUSc9LuljSyIEoXBAEQTBrU6cncia+2NMSwJLA5SktCIIgmM2po0RGmNmZZvZ22s4CYqHyIAiCoJYS+Y+kz0gakrbP4GufB0EQBLM5dZTI54HdgGeBZ/D1z/dqeUYQBEEwW1AndtYTNFgeNwiCIHj3U6lEJB3W4jwzs6M7UJ4gCIJgENGqJ/JqSdoCwN7AokAokSAIgtmcSiViZj/LfktaEDgQHwu5APhZ1XlBEATB7EPLMRFJiwAH4asZjgPWMrMXB6JgQRAEwaxPqzGRnwAfB04FPmRmrwxYqYIgCIJBQSsX32/gM9S/D/xL0rS0TZc0bWCKFwRBEMzKtBoTibVGgiAIgpb0Ok8kCIIgGHhGHXxlrzKPH7v9AJSkNdHbCIIgCBrTsZ6IpKWBs4HFAQNONbMTksfXhcAo4HFgNzN7UZKAE4AxwGvA58zs7pTXWHxsBuAYMxuX0tcGzgLmA64CDjQz69Q9BUEw+9BbT2BW6AXMCnSyJ/I28A0zWxlYH9hP0srAwcD1ZrYCcH3aB9gOWCFt+wCnwAw348OB9YB1gcMlLZzOOQX4Yu68bTt4P0EQBEGBjikRM3sm60mY2XTgQWApYGd8zgnp70fT752Bs825HRguaQlgG+BaM3shzVG5Ftg2HRtmZren3sfZubyCIAiCAWBAxkQkjQLWBO4AFjezZ9KhZ3FzF7iCeTJ32lMprVX6UyXpZdffR9IESROmTp3ap3sJgiAIuui4EpE0FLgY+JqZdZtfknoQHR/DMLNTzWy0mY0eMSLW0wqCIOgvOqpEJM2FK5DfmtklKfm5ZIoi/X0+pT8NLJ07fWRKa5U+siQ9CIIgGCA6pkSSt9XpwINmdnzu0GXA2PR7LHBpLn1POesDLyez13hga0kLpwH1rYHx6dg0Seuna+2ZyysIgiAYADo52XAj4LPAZEn3pLTvAscCF0naG3gCXzUR3EV3DDAFd/HdC8DMXpB0NHBXkjvKzF5Iv79Cl4vv1WkLgiAIBoiOKREzuwVQxeGtSuQN2K8irzOAM0rSJwCr9qGYQRAEQR+IGetBEARBY0KJBEEQBI0JJRIEQRA0JpRIEARB0JhQIkEQBEFjQokEQRAEjQklEgRBEDQmlEgQBEHQmFAiQRAEQWNijfUAiFXcgiBoRvREgiAIgsaEEgmCIAgaE0okCIIgaEwokSAIgqAxoUSCIAiCxoQSCYIgCBoTSiQIgiBoTMwTGWTEfI4gCGYlQokEQTAg9NYAgmgEDUbCnBUEQRA0JpRIEARB0JhQIkEQBEFjQokEQRAEjQklEgRBEDQmlEgQBEHQmFAiQRAEQWNCiQRBEASN6ZgSkXSGpOcl/S2XtoikayU9kv4unNIl6URJUyTdJ2mt3Dljk/wjksbm0teWNDmdc6IkdepegiAIgnI62RM5C9i2kHYwcL2ZrQBcn/YBtgNWSNs+wCngSgc4HFgPWBc4PFM8SeaLufOK1wqCIAg6TMeUiJndDLxQSN4ZGJd+jwM+mks/25zbgeGSlgC2Aa41sxfM7EXgWmDbdGyYmd1uZgacncsrCIIgGCAGOnbW4mb2TPr9LLB4+r0U8GRO7qmU1ir9qZL0UiTtg/dwWGaZZfpQ/CBwIg5UEDgzLQCjmZkkG6BrnQqcCjB69OgBuWbQOaICD4JZh4H2znoumaJIf59P6U8DS+fkRqa0VukjS9KDIAiCAWSglchlQOZhNRa4NJe+Z/LSWh94OZm9xgNbS1o4DahvDYxPx6ZJWj95Ze2ZyysIgiAYIDpmzpJ0PrA5sJikp3Avq2OBiyTtDTwB7JbErwLGAFOA14C9AMzsBUlHA3cluaPMLBus/wruATYfcHXagiAIggGkY0rEzHavOLRViawB+1XkcwZwRkn6BGDVvpQxCIIg6BsxYz0IgiBoTCiRIAiCoDGxxnoQBN0IF+qgHaInEgRBEDQmlEgQBEHQmDBnBUEwyxEmtcFD9ESCIAiCxoQSCYIgCBoTSiQIgiBoTIyJBLMEYQMPgsFJ9ESCIAiCxoQSCYIgCBoTSiQIgiBoTIyJdIiw8QcZ8S4E72aiJxIEQRA0JpRIEARB0JhQIkEQBEFjYkwkCILGxHhP+/T2zAbb84qeSBAEQdCYUCJBEARBY0KJBEEQBI2JMZEgSIR9PwjaJ5RIEMxChCILBhuhRIK2iEouGMy82zyjZgVCicwCxIsdBIOb2fkbjoH1IAiCoDHRE3kXM7NbR2H6CoJ3P6FE2iAqxSAIZkVmZt006M1ZkraV9LCkKZIOntnlCYIgmJ0Y1EpE0hDgV8B2wMrA7pJWnrmlCoIgmH0Y1EoEWBeYYmaPmtlbwAXAzjO5TEEQBLMNMrOZXYbGSNoF2NbMvpD2PwusZ2b7F+T2AfZJux8AHu6nIiwG/DtkQ7aDsjP7+iE7e8j2xrJmNqL0iJkN2g3YBTgtt/9Z4JcDeP0JIRuynZSd2dcP2dlDti/bYDdnPQ0sndsfmdKCIAiCAWCwK5G7gBUkLSdpbuBTwGUzuUxBEASzDYN6noiZvS1pf2A8MAQ4w8zuH8AinBqyIdth2Zl9/ZCdPWQbM6gH1oMgCIKZy2A3ZwVBEAQzkVAiQRAEQWNCiQTBuwBJc0gaNrPLEcx+hBIZIOIjH1xImn9ml6E3JJ0naZikBYC/AQ9I+tbMLlfQGkk7SnrX1L0xsN4mko4zs+/0lpbSzwO+DPwPd0ceBpxgZj8pkZ0H+AQwipzXnJkdlZP5eKuymdklJfkuB3y1JN+dSmQvAU4Hrjazd1pdS9JqJXn2uH6Srf3M6iLpq8C5ZvZiDav1HnYAACAASURBVNkRwBdLyvv5EtkNgdOAoWa2jKTVgS+Z2VdKZA8EzgSmp3PWBA42sz/1UbbWvUm6x8zWkLQHsBZwMDDRzFarkJ8f+AawjJl9UdIKwAfM7IoS2drvQpJfCliW7s/35hK5jYAjcrJyUVu+RLbsfX8ZmGxmzxdkBewBLG9mR0laBnivmd2Zk5kMVFZ4Zc8tlfceM3tV0mfw53yCmT1RloekhYEVgHlz+d5ckDkX2AC4GPcofaiqTHXqhZzs5SX39zIwAfiNmb1RdZ2+EEqkTSTdbWZrFdLuq3gBa3/kkq7B/+ETcaUDgJn9LCdzZvr5HmBD4M9pfwvgVjPboSTfe/HKYDIwozIws5tKZD8M7AWsD/wOONPMeoSIkXQGsBpwfy5PK6uUk3yvz0zSdFp/4N16cZKOwecF3Q2cAYy3ipdZ0q3AX+j5bC8ukb0Dj4RwmZmtmdL+Zmarlsjea2arS9oG+BJwKHBO8V4byNa6N0n3A2sA5+GRGm7KrlPxHC5Mz2BPM1s1KZVbzWyNEtla70KSPQ74JPAAXc/XKhoqDwFfp+f/4j8lslfile0NKWnzdN5ywFFmdk5O9hT8XdzSzD6YKvM/mdk6OZll08/90t/s/D1SGXpEAZd0H7A6/r6fhTcAdjOzzUpkvwAciE96vgd/dreZ2ZYlssOA3fFnbHgD43wzm16Q67VeyMmeAIwAzk9JnwSmpfyHmdlni+f0CwMxLf7dsAH74hXxa8B9ue0xvNVYds79wFz4R7hZSru3QvZvbZTlT8ASuf0l8IqmTPaOBve6EN6DehK4FX/R58odf6DNZ/ZqG8/saOArwIJ4z21fvMIokxWwDR54cwrwQ+B9JXL3tHHvd6S/k3JpVf+z+9LfE4CPFc9rKlv33oAD8AgNVyX5ZYG/tMhzQt17q/suJJmHgXnaeb41ZccDi+f2F09pixS/F+DuNv5vPZ57dn5VOnAYsHcvspPxHsg9aX8l4JIW97co8DXgceBq4BHgqwWZduqFu6rSgPvr5tPu9q6xyw0A5wE7Apemv9m2tpl9puKc3+AvyALAzaklNK1C9lZJH6pZlqXN7Jnc/nPAMhWyJ0g6XNIGktbKtqqMJS0KfA74AjAJr/TWAq7Nid1WM+R+9swuo/4z28nMTjaz6WY2zcxOoSIys/nX8Wza3gYWBn4v6ccF0SskjalRXoAnk0nLJM0l6ZvAgxWyEyX9CRgDjJe0ILneXh9ka92bmZ1oZkuZ2RhznsB7pVW8JWk+Uo9P0vuAN6uEa74LAI/ijaVKcu/dDZJ+UvN9XNrMnsvtP5/SXgD+W5D9b1oaIru3EVQ/XyUzVbazIdXjw9MlHYLH5bsyjWVU3esblkxGkuYxN1N9oOTiO0v6A3BjymtdM9sO7/F8oyDeTr0wNJnxsussAwxNu2/VzKN9OqWd3o0bPiv+oT7mMWdF+gPpH/0w3lqfTGq9lsj+Em+RfS5tVwMnVcj+CHgKuAk3C9wA/LlC9g+pHIeQ6+mkYxNyvzfDu9gty4p3ocFbjj22ijLcipsXhuAf9h64yaUodyDexR8P7EpqHadz/lGQnY5XKG+k39OBaRXXXwz4La6YnwfOBRatkJ0Dr1SH5+5ztZqyi7aQrXVveMs8G7cAX1Nn7xbv3kfSezA13ePjwOZ9eRfS/sV4b+k3wInZVpC5ocVW9T6eDFwBjE3bZSltAeCGguwe6fhTwA/Su7lrRb5rA/em+38CNz2tVSH7XuAgYJO0vwxuDqx6ZsPxMZ+b8QbnVSVy44BNK/LYKv2djH9bD+AKs069MAb4Z3qmN6Z72z49r69VvRd93WJMpE0kXYp3Of9ZQ/awsnQrHxRbtkK2agDv48AmafdmM/tDhdwUYGXz9VZ6K+8YM7uqkDaPmb1ZSJuCf1jFcZYnCnJXmNkOkh7DW4jqfmulg6mj8BZv1lK8Bf8AHi/IHYkPSvZ4PpI+aGZVvYd+o8Gga93B51r3Julq3Jb+PfPxljlxU01lyzX1LtbH/xe3m1lpqHBJW5jZDWXHSmTHlqWb2bgS2eXN7NHe0lK68EHl7F34K3CxVVRaklYCtsLv7fre3gFJC6VyvtyL3HvxtYsMNw8920o+nbMZbgq8pvjtqYajSVV9kNHiHZsHN6MBPGwdGkzvds1QIu0h6Wbcs+ZO3NYPVHo75bum8wI7AA9a9QD06nQphr+Y2b39UN4/AvtYwZulQrZsALws7TYz26CvZesLktbH7bzT0/4w4INmdkeF/E7Apmn3RivxSEpy44ADzeyltL8w8LOy/1mbg67tDD6fY4VB0Iq0u8xsHUmTrMsJ4B4rGShPxz6Gt/pfTvvD8Z7IH3MybXsApvPmBlZMuw+bWdHclMmVvU8TzWztVtftjXbeB3X3lPs/ktOLlXvKfQEfD/kzrpw2w8fozsjJDDOzaZIWKSubufktn2c7zjntvucb0tOT6+wy2f5iUAdgnEkcWlfQCh4Ukn6Kmyh6kF7sLwLZR3qupFPN7KQS2Y8Dx+FeWqLLTbJsHspw4CFJd5Gzf+crr9TSWgqYT9KadPUYhgFl8yUmyd2XLy/k2a2CaTX2kuTvLrm35fGeyPp4y+824OslLdVT8I8/45WStCzPY4F1cBMOwIGSNjKzQ0qKtVqmQFIZX0zPpIy3zcwk7Yx7R50uae8K2Y/i7rSVYxA5VimUfwhuginyaupZZOMA6+NmxioOz/dYzewlSYcDf8zJ7NjifKPr/cyXb3PcRPM4/u4sLWlsvpeVegmrAAsVFNUwcu6whXzbec9rvw/A583sBLmn3KL4eMc5uMNKkW8Ba1ryHkvP+1bcay7jPLyBOJGSHjewfDp3X9xp5H2pAZKxIN7LKqOd9/wc4H24eW5GQwUIJTIrYe5GuTheKQHcWaeVn5gfd/8rY298VcZXYUbL9TaghxIBfgzsWNNkc3gNmW3wsZWRwPG59GnAd0vk58OVx9a5tLIKJlOi8wKjcTu08Jb7BNx9s8h5wK+Aj6X9T+Eui+sV5JQ3a5jZO8mcU8YYYA1L8x1Sb2MSbu8vMoekhS3N0Uity6p884Oum/Qy6JoNPrcayD4Ef97zSZpGV2X0FuURWQ/CxwHeJ+mvuHvnLlX5Uz543O3ezGyvFudX8TNga0suwJJWxP9necX3AbyiHU53RTUdbzyV0c573s77kD3XMcDZZnZ/Mp2V8Z9Uxnx5u7kjW3KtN7Pleinjefj45Y9wd/8ZeRZ7K/mytnFfo3HT9cCal8oGSmKr3oDd8AGrcbiGfwzYpUI2Gxy7D3f3fR7Yv4XsvLn9efFJVWWyf22jvAsAc6TfKwI7UXDRzMl+okPP7BLgQ7n9VYHfV8iWDdD3cNVMeR6AV8xz4YPRf6zKk9xAPj4AXjU4uSfwEO5qfHT6/dkK2XYGXXsdfM7J/qjmc10OVwKrpGc6F7BOC/kz8EbC+9J2PHBWhWztQfuK/1nV892gjfemnfe8nffhTLzX8QjesFsQn79VJns23uA4Am+Q3Y2bLg8CDiqRXwqfw7VptlXkuzGwV/q9GLBcP9zX7yg4QQzEFmMibSKfvPcRS70PuSvhdVYywaswOPY28JyZvV2R70G4B0pmbvgo/oH/IieTmQE2wyuwP9LCnJTOmYiPsyyMd5nvAt4ysz1KZN+Le7YsaWbbyd14NzCz0wtyPwaOAV4HrsF7Fl83s3Mr7u1+MyuaaHqkpfTjgJfwlqzh4wgLAz9J9/hCknsPXhFvmeSuxwfge/QKJe0OHIt7rQj/uA82swsLcnPgZrSXUr7gYwgPlN1XOmdZYAUzu04+eW+IFSaMJbl2Bp/nAD6NVyxHS1oarxzuLMhNxF2in077mwK/soqBdXl4lEOBD6eka4FjLPV+C7K1B+3lk0/fwT3ZIHnXWW4cSdJJtJ5MekBJvidQ/z1v532YA5+k+ai5SW9RYCkzu69EtmVP3syOzMnWGvdKeY7GzZsrSloS+J2ZbUSBivs60MymlsjekO7rTipM150glEibSJqc/5DSC3lviw83P1h+c9mLmpNdC2+hgA+sTyocP7NF0czKB3/vNrO15KE05jOzH6tiVnPdikNdM/E/hpsoDkr3VjVT+nzcCSFfyQw1s91LZB/L7idLKtxjD4+uOkhagu4myFIPm/wgdY08vwjsg/dy3icPI/JrM9uqSRlz+fY6+zrJrYO7vO6I28h/BOxgZk/25fop79qD9nKPoP3IvbvAyZYb/6lSohkVyrTsfS99z9tB0vXF/1FZWoN8H8bH1FqOe0m6B3fOuTv3bKsG1jcys7/2lpbSezh0QHl0iv4kxkTa5xpJ4+keWuCqMsGSwfLfFgfLC54dj6ctO7aI5Wyl1sxeLUkb4BV3NuhbNbFqMTO7KNnmMV858n8lctl7sz3egnq52qQM+CznffGuOLgP/SkVsivjg48b44rkL8ApVnBVlDRvup9V6B6nKN/6XcnMHsoN8D+V/i4paUkrGdgHrpf0CXymcW8trP1w18870rUfSS3HHqjLzbkbFUpxvaT4JyWZF+XeT8Vz75J0AG6aeQP4cEUL9Rdm9jWVx1aqaqnWHrRPlebxdB9PK8r0UBK90c77rhrx0dI7Mz+wWFLMeQeSpQr5NXlmvY57Jd4yM5OUPdsFWsieRM9B9LK0jiuLKkKJtImZfSuZlbJW16lWMUeDeoPlRc+ODJHz7Ejnn9hL2XqYBPCwCocAfzAfQFyerlhERepWHFfIYyC9DuybPuBKf/SkAH6ett4Yhw/oZ/f6adwuvVtB7hx8vGIb4ChcSRYHYA/Cewo94gzh99gjphEe1+og4H+SsnsyK/cIetPM3soUaOq5VSme0bnf8+KTCEtdQull9nVJxTY//n86XVJZBZfFiPppxfXK6HXQXtJFZrabKgIbVrSsRwDfwRsLeeW/ZU7m26nHXGoCq3jPL8UbHNeRizFV4Ev497Ak/r1lTMcn8OZp8sxeA+6RdD3dzUnF8l4k6TfA8NSb/TzuajyD1PDbEBiRTN0Zw/CJuHnZW8xsY/WMP9fKm63fCHNWA9LYwXr4h105+Sh9XOtYVyiEeZN83TAGxfwyk8BG+EeY2fR3xeNZfblJvrn818IV3Kp4aPERuNNAma14EeBlM/tfGgsYVnwOVZVLRkUl84CZrVwjbZKZrZmZASTNhZsA1699w31EPjb0Ej4Y/1W8B/WAmX2v5vml8yPkATs/ibc2x+GV9/fN7HfpeKnZIqOqRZoaP1f2Zm7Jyc+Je1WJkrkfkpYws2fUxkRZeeiXC4Fv4jG5xgJTrftEux3N7PIqE1iF6atyfkyJ7FeBuemlt9subZb3I7h3o/C4d9cWjm+GB5z8MvDr3KHpwOVm9khfytqfhBJpE9WYfJST7XWwPCdb204r6XZgY0uD9GUVaMPueMuKQ9KWZvZnVUxIs57zRLLKpRg19TMuXho19Vx8zsXtaX89YD8z27Mgd6eZrSuf/PkVPMbUnWXmIUm74jOHp0v6Pl45H22FMaecfN2JiXPgvc0ZlQFwWpkZTN3nzMyB90z2bTGOVGv2tdpwN09jDFvi5sQL8WdS6uiR5GtNXFN7yyNMNLO182MASuMvJbK7ZoqzVVpKPwYPj1NqWi7I/g7vuWXzhj4NLGRmxd4uaiN0fZKfDw+1XxrxuF0kLVumjFvID8E96/L/s16ja/SFUCJtIh8829AKk4/MrEegtXS8t8HyzE57A97yyNtprzGzlSiQyrCBdXkqLYyHsPhATmZtM5tY1WrNt1brKgdJR5rZ4WpzwFMlg9UqzNrN9VrmwpXYP9P+sni8smJP5Au42+yHcJfLocChZvabkutnvZWNca+ynwCHmVlx7gnqOTFxdzxWVNmcktrIPWeyj+1tfOzrp2b295xMuzOfd0v3ciP+3mwCfMvMft+iHHMB2+E9nY2Ba83sCyVypRPXykxJxf9lSqsaKL7dzNaXjyueCPwLd/d+X818e6Sl9Om4O/ubeKypSlNO3d5uSm8ndP2OuPlrbjNbTtIaeANzp3S8bbNTMv99m55jf2Xh5b+KuyE/R/clGkrXl+kvYkykfXqdfFTgMbzSmBMf5F7Lug/oFu20mRKZRk87bcax+KzxvMvqEXkBM5uY/tYZbNsM71mVzVaeMYnQzDJ3x6PM7LG8kHzxqyqknEeJyqOm9lgLpUVmc+ABFF/EW9W9eWxlH//2+BjWlanlWkbtiYlttlKvoPtsZsMnKM5vZvektNrjY4nv4ebSbu7mQKUSMbP/yr3wDJ80+lE8Sm+RXieuqWsG9vKqPwP7GHnMqm/gptNheCWdz3c7/P+wlLqPAw7Dv6Wy+1qwqpwl3C1p/UJvd0KF7MtmdnXNfI/AHS1uTGW6Rz4GmZVx4wZl/S3ea9yBnPmvQvZA3G24VX3U70RPpE0knY23fi/FP8Sd6ZpQiJkdn5M9Gp8J/g+6KgWrakVYSYiTFuVYEp8p/SDek/mXdQ8zUTUekVV0jVsnFS3EyvhHktbGJ7otlJJewkNPlHlH1S3DBDMb3bskSLoCX3fjI7gp63Xc9FPm5nwfHk8q6+Utgpu0ylrV7bRSz8Mr5svw/8EO+DszCvdwK4avr3Nf7bqbZz2QzfGK7iLcdbhHxZxMPgdY9yUHijIL4XN42pmB3Styt/g1cIeJfBDT6Xj03tIVH1VjVcEk9yBdvV3wSaIP4wrKUq81e793wweyL6H7YHlZyJ6sl5V3i+7RI5P0YTO7rpA21srHTtox/92Az2GrNFF2guiJtM8/0pZxafpb1rrYDV9IqE4s/3ckDbfugf92N7OTi4KqWEGN7t5GtVv2uXzPwWfUZwH6lsWjyW6V9tuOfwQzekWrq2bU1JpcJ1/r40K6B8Isq7x2A7bFzUcvyeeMVK1F/iN69vJ6jN0k2mmljsTDjb8CIJ9wdmXKfyIe4oN0bG/LTfBMdu7vW25iW6Lobv4pPKxGFXviz+tL1vvg+mL4mu2VE9fS//Fl3OSH3L15Xnxdi6Fltnh5SJRT8MWmVpUvs7yTmR2Ty/de4F5J51lFIMeSfOt8Exnb1siy6NGXb7BUefbdL+nTwBD5nKED8DhbRQ6Tu5F/EzfDnoY/4zI36Oz+n5G0PW7+62buVJf31qPAjfIVIfP/s0rX6/4geiINkUfTNCuZnZyTuRgfPK0TQbeHd0nZWEJKn4zb7W83n/S3EvBDMysd01DNwVdJX8Jb1gfhfvPfAr5hZpen4zvj5o+d8BZ1xnTgAjMr+2Cy6/+QXmbCt4O6JiXmKTUlyRdfesrM3pQHC1wNj5n0UlE2ydedmHgs9VupD+GhX/6b9ufBew0rFf/PqdcyHB+0XxSfAHqTmX2zJN+P0xUq/S+Wi8jbF9TGxLU0FnA8bpJ9HjfvPWjlEQluwt+r31jvyw+3sx57W99EJ5B7KX6Prphy4/GIAMU5TsLNeV9KSYeZ2fmUIGkH3HtsabrMf0ea2WU5mdqz6juCDXCclcG+4S2SyXRNDLwXX6mvSvZp/GW6LNsqZCeTlHraH0LFkpZ0LXl5D2lZ0haytWN9JfmN8dbPM8B7K2Rqxz9K8lenctyb9uekIi5Yh/5n96Rrvh/4Oz4Y3WOxoCR7Od6yXqBGvjeUbFULLB2Kx106PG0TcFPNAsBvS+Q/Cfw7/e82Khy7Jf2djo+dTc9tL6f/8Vdy8plc6dbi/hbHe7Q7AO9pIXcvruwmpf0tgNN7eXfzy9iWLl+MzwPaDo/iu2i29fWbaPPdORCvuIX3GO7Gg00W5YZQWCirRZ6L4KbEa3BX+oPJffsF2XmblHsgt5legMG24XbsTXL7G1MdbO5+vEu7BT54vRlprfUS2Z+kF2urtF2Er2NRJltrBbUke2++AsDnflStPf1ZvJLdHTfr3A2sXiJ3Jj7G0W1r8cxqVxw1nv+W6e/Hy7aKc7J1sr9NWsOa6rXQN8NDiTyBD1DvUvUhA8vXScsdG50qpQOB0S3kVsDNIL9J/99fA/O38YwWxd2zi+ntrF/fTqDRbO32e+kK9ln1jl2Ne31l/5NdSEEeS2TbWY+99jfR5vuWNXy2SddYheo11q/HXYV7y/Pv+JgguHPDiZSs3pmOT8GdFI7FHUMq88djoQ3P7S+Mz0Hp0zPo9X46fYF321ZW+bR4qe5qI9850kf9+7R9CQ9i19t5m+Hmpbkrjk8uuU5VdOA/0l3hrEtJZY+vNpdte6TylkakTfI3pootqzjWx80zTZ7/kenvmSVbqSLDw5Lsjrf6lktpf+vlOkPwgfiLqF5Kt8f/nYposG3e40N0LZOamT7aalVTEs21rGJvUdm30/i4Drftn4SPz5zQolJcPsm/hvfSbwFGVcgeizeuNsAdItaiYhnbdr6JNp/jfenvCcDH0u+qBsil+GD96bSI1IzPIymmlUb7zeTTd3Yybv2o6rmVfaulZe3PLcZE2kTSL/DWQz7K7Buk4IKWs4dLOh63lV9GLzbzDpb3J/gYQD7W131WMhGs4vy5rRfHgOQVdIuZbVhxvPZM+E6QxmC+DNxmZucnd+TdzOy4Cvn5cHfnbNb4FWb21dzxzMHgx3QfoB+Gz9PoMRbQZnmHmdm0QtqKlptT0jDfW/G1Wi7A393d8YmcPf5v7Xh+yWM/vY43UPbAvfDOtRYeWumcOaz1mOINJclmJd6NSb7fJ9rJ50QtBSyHr2I5BPfWK4s0MLYsDyv3uqq7VPJIfP7PZun6L+Df2o9KZCfiiu6faX9ZPNxRy8Xh+kookTapeLEzur3gdT4CNYg/1C7JEyQ/+Fq1HnuvQewqzvsAHk7j/SXHhuAmvZNoEUKjXeSTPA+nK3TFLbhpptRHXjVnEku6CO+BXYN7Mt1kac5ITqaRg0E7qAPLnKr7+vWGm0l6rF+fZGs3PtTejPV58B7sKLrf21FN7imXb0cm2qmNsPFt5NnOUsnv4Ms3/NDMLi0eL8huiy9edhPMmHy6j5mVrqbaX4QS6SCShphZVTC4TKbt+EOdIrVU/0LPeQ8XF+TyM24N/3APtur1t+80s3X7uazX4rbvfHj5zc3swyWyLWcSF2S3wdeHafl/S7IbmNltfbmPinxrzxbvJG00PtqZsX4NPvhffMd6BMlsx6tP0hQ82Gm/T7Rro9dQy5tMNUPGJ9nV8YbSprhZ6xG8YVPq2ShpMdxcDO6p9u/ertFXQom0iaTDytLLWlKSHsVDc5xh9Zb47HfUxjrVZW7GLfJdhO4Tu6zsw0qyP8fDmRTndPRlsmEPt9CiCSaXPhH367/RKtxK1UZcMDWLMlsb+WS4fl/mVDXC57eZ34wZ63SfO7UgvirhZ0rOKXXnrci/nYWxbqADE+3a7DXUmnya7mtXS3OGapRhKK5INsHjzmFmpY1O1Zxw2Z/EZMP2ya8CNy/u/lilIFbHJ4CdnrrFZ+Dmjhn2bvWMo9ONssq+TdpZp/oKSWOslyB2am9iF7g5AHwGcoa1kK/DnyR9Ch/4Bvfyqeq2/9d6rnnyTkFmU7pCvxjMCDWS/c33sr6DP9d/AKWzp/vI3/AV/Spnizek1/D5Ld7HssZHkzXDb5X0ITObXKO8dde3gc5NtPsoHkqkTuTjupNP64aMR9IEYB7cW+8v+AB8qXWiwXfZL0RPpI8kG+94M9u8F7nN8I9uOO7NdLSZTckdPxqvNM7BP9g9cA+b0p5PG+X7q5Usu1khWyuIXTsTu7IxETOrs5ZIbXJl/V8q5xx0KfhuZZZ0Ou5+eTBujz8AX2f+yzmZb9BTeZB+d6uMJD2ALzF7Nd2DZmYXbxzyI+V/Ax1Y5lT9HD5fbQSMzI35zYm3lB/F760yDI+kG/H/17Xmi3StDxxnZpuVyB5eUYY+TbSr02tQmyFS2hyAH2ElC41VlGOmTLiMnkjfmR/X/D1IFej2+Mp+o/BQCr/Fu6VXASvmxHey7rGcTpGv595IieTMMhMkXUiNdarNbMESM1UZb5jZG5KQNI/56oGlUYzN1xvZnXoLUtXG2gti91V8JvGbuCIfj0fzzTM0/f0A/iFeildwO+KVeZ5TcKW0PN0XN6oKlNguR/Tx/CoyZ4aXJK2Kh88vXYmxJsWAkd2WMqb7c2g7DA81FsaacbHOzcqu02toK0RKmbJowRypEVQn2kPt77I/CSXSJuruRTUEf7GrPEsewWcx/8S6e+z8XtKmBdlX5YsR5d0vX6U5+Yi8r9EVigF6mmeAyu7wrfjkxzxPSRqOK6ZrJb2IT0yr4q+Sfkn/jolshPvFvyrpM7gr7i+s4NKZFPmVZrYFrkhKySoh+foka1lyPZV0BB7jKi97EnCSpFPMbN+m99CiLJ1a5vTUZDM/FK+ch9KwkQJgZjukv60iOGdUuvKWkf5vm6Wt1cJYjdbNaYMs0kQl6d2qjTyu1o/oubpjWePjLNK4UNr/O/4dlSmRdr/LfiHMWW1S8KJ6G3iuajBPHoSu7uDZKGq6X3aKJt3hZKZbCF/7pHQ+idr0969Z1vvwMafV8A/tNHzuR5mp43p8NnuvgR+LnjPJXHmfVawX0wmS2eYk4IP4CnxDgFf7YXysX1H3hbZ6YN3nTD1Gz95KTrQ0HlavXn1KSyuojVhf7SJf3z6zGlS6p6um27mkW5Lcz/HG3l74nJkeCl0pYq+6Rwbu1QGmznfZX0RPpH3mpHswv09IqgrmN5+kA6gx7yIpi537u7DyyUonkXPVBA40s6dKxNvuDtf5SNttqdXkbTMz+ZyNX5rZ6ZL2rpB9BZgsdwvO94TKvKjOBu6U1G01yn4sdx1+iTtk/A43jexJd9NnW6j7Gt096MPgc2bGmRcv5724klgNjw22Qe4adXorRer0YH+C95THWM0JtO2QvvFx+ExxAUvLw7aXeTxdgLudfyLt75HKXnQ7n8/MrpekNEh+hNyDsKxX+GpSTpbKsz7uIl1V3o2BFczsTPm8r6XwkDUdI5RI+1wMjJb0fnxiz6W4bXhMieylqrUCaAAAF7xJREFUeKV9HTmXvzyqcBPNqKjo2uHMVL5d0/5nUtpHSmQ71h2Wh7Euupb2ZYLZ9OS18xlgU7n321wVspdQYr4rw8x+kAZTN0lJe1nFMrqdxMymqGue0ZmSShfGqkk740e1yRoHki7BTYCT0/6qtBjXUX031DpefUvIJ2buJOkCejo59DU6xM/wgIsPp7KviE/ALFs7ZwkzOzq3f4ykT5bIvZne10ck7Y+HfxlaIgdtjAsl54LRuPnvTPx7OJeuBmRHCHNWmyhNrJL0beB1MztJ1SHb63Q7M0+NjXAb6YVpf1fggbwHUcPyloWYH9DusKRf4w4IW+Bmp13wEOtVPYc6eb4XXxv7LjP7i6Rl8MmGpbO6k0liJbwSerjTXfy+kMZlPow/q2dxr73PWcV67DMbSfdbIdRLWVpKL3VDbWralLQLPvdlY3quTtgnk2nKv2xRqaqJlMfjThh5t/N1rRDCX9I6uGv1cDwo5kLAjy2ttFiS75zUiPYg6R5gTTymW+WiWP1NKJE2kXQH8At8oGtHM3tM1eshHIMHoms57yLJ3g5snI2vqI/ul7l8r8dbJVn4it3x1nVxsLxjqMulNPs7FI/cukmvJ/fP9cfgEXH/gX+Iy+ELM9VdUGpASeNuz+HjIV/HK5mTLecS3jDffp1smMv3fNzclI8eMNTMdi+RbXctnFo9WEmHFnoB/YKkM/A5Rfl7G1L2zNTldp7NQap0O69x3ZZuuVbiXZmNIeUaugvgCjrWWJ/F2AsP5veDpECWw+d2lHEg8F1Jb9HlXln1Mi2MB/DLfOuHprS+8nl8TOTneCv81nQPA8nr6e9r8mV9/wMs0ZcM1cZMfHzBpC2ySli+SNWVtF4FcKZhXZPJ3gD603W118mGDdkLj0B9YNq/GXeDLqP2uFtVD7Ygkw3uX1k20N8P5qx9gf3wuUXg5ukeq42ma7U0G6rCgyx3ft6TLPOufA+wIT4RFvxZ3Eq5efYiSb8Bhkv6Iv7t/1+rMvUH0RPpZyRdbGaf6F2yx3l74XbkG2DGsqxHWHs+5bMkkg7FFdlWeBRZA04zs0P7kOcUas7EV2FNaknCzWk91qmeFVDPGExApQtoO/n262TDQt51A1z+AVc6X8PHNl7EJ372GFOs04NVGwFRBwJJO+HfLniYnStyx0o9yDLKnFQk/QkYa2mte/mqm2eZ2TYlssfh469b43XIeODDnXA46HbdUCL9S3F8pNVLVXLukvjCUA/iLbB/VQw4tlOecbg3Vn7t9p/11YTRh/LMgy/y1Kd11tXeTPxT8Ar5IlyB7Yqv+3AdlJsGZiaqGYOpQb6ZueNmPObVs7gy7aty2gn3kuo1wGXhvGzc7eoyO7+kO8xsvWTq/Tjeg73fSqJF9zdqEF1bvlzyOviEYnDT8QQza+oQgaQHzeyDuf058GfwwRLZ2oEw+5MwZ/U/M164kpfqQEkblb1UVQOO9D3uzWqWcz82sxcl9XAC6ASt7LqS+lp5156Jj9vTn8MnrgFMxdeEyeJkzVJKhPoxmNqlXycb5jgcD59/I4CZ3ZPMvD2QdI6ZfTbJ3ZSl4Y2nIlfIvQV/TFdkgNOqCiH3CitO4GsaPj8zzbUz034MsIalpQNSA66HV53am2x4vaTxdA/Jf10hvxmBMOXzpzIWxOebdZRQIp2l1kuVOJCuAcctsgHHfijDHJIWNrMXUxkWYeD+7zu2ONbXynsYNWfim9lAjwE1ImfTv0G+nkfLGEztYmZZBXwTfQ/NkqcswGWViaPoxTWEcndZ8PD9++Lu1rfh4xGlYy1y99bN8Yr5Knxt9lvweT9tk5mP8LXqe6yVggfhLGM4XeOaC1XInEnXZMMtSJMNK8qxf2qMZSa8U61nSP4mgTD7jVAi/U9xRm6dlwo6F/fmZ8Btkn6XyrYL8IN+yLdXOll5t5O33Lf/FGBxM1tV0mp4rLJi/KyZTVsxmNpF5ZMOX8aX9L2nD1nfL+nTwJDUyj4AH/zNX/sQ4Lv4BNwsirWAt/D5VmWMw8OlnJj2P40rhd1KZHfBIxhMMrO95GuRnFsi1y4foafC2K4kDbwSn5TGabJxzbIGYzuTDbPedWWDK5mGX8bNZwOPdXj93dltwycmZb93xyfrnYV/EI8Bn6w47w+4wjkC9265FLiqn8q0MrB/2laeCc9kIdxDakLafgYs1Mc8R6Zn9nzaLgZGVsjehJtbJuXSWq6xPpPfoeXrpDXI9zw89tLP0vYwPiv+LuDbfch3frxhclfajgHmqZD9URv5PlAnLaXfmf5OxHupAh7qwz3tC0zGXXTvy22P4Uv/Vp23BL7i5U7AeytkbsV7Hpekb/Jj+PyPvMwt6e90YFpumw5Mm9nvaLeyzuwCDJYNtx8fBdyPa/2pwO34JLBW5/X6UpWcs1mSn7sP5V2k1TbAz+5i3FV1+bQdDlzSxzyvxc0Ac6btc3jI8DLZu9LfvBK5Z2a/Uy3u7e6StIn9kO/N+PyNbH9oUrDzVVXONfMdjSv0SaninYzHG6uSXwp3W9002yrkzgXWz+2vB5xdIXsy3gj7Mh74dBJwZh/uaSE8XNH5uFNGtlV+O3hk5zGFtFNL5NZJz34kbtq6JH+fg20L76yaSLoU/1Cuw7vTC+Cxcr4PPG1m3604r9bSmh0obzHgXfaPLl2ys8NlaTRrvr/ylIcx2R/4nfkkrF2Avc1su6bX7wRpHGwVfCD5W7lDw4BvWckM8Dbzfwj4kCVPqOQpd6+ZrVT0Kmwz34eBb+KLac1Y7MtKFk9Kziafot5KgQ/iM7WzyMzL4L2nt6lYgySdNwoYZn1YB70kz/fQfRD8nyUyjwJPAn+2rqjQPTym2rzu+rg3VhZVekHcmnBH0zz7mxgTqc8oMzsr/T4+zT04Os3veAC393ZDXUtr3k/Xx2V4i7CjWC7gneqtEdJJXpe0sZndksqzEV0TEJvyH3kI+PxM/CoX2P1wu/tKkp7GTRJ79PH6neADuDfQcLo7JUwHvtgP+f8WuCM1iEjXOE8+s/mBPuQ71cwuryn7MeqvFLhtO4UoNtgkbdrXBpukHXFT7JK42XRZ3AW/TKG/hM+FOlE+sbDb8sBqFrb+FHyZg4xXS9JmKtETqYmkW3G78S3JL34/SxN+JD1sJaHCVQgrPjOocB2+1QY27Mka+JjQQnhP6AV8AlXjlqI8NMhJeKTYbCb+V83syZxMcSB5PnKhKKzvS6d2BEkbmNltHcp7NF0B+f5qZsV4U03y3ApX4sWFm8pCc7S1vngbZai9Fnqb+d6LOzRcZz5RcwvgM1YS903dw7V/DvgGsLCZjUxp/9/euQfbVZZn/PcEtFEucYSWW9MBKaCRgAUs2DIdwNJSEUgiWKDemLGptKPVjoI2iGBmaL380QKdUUFLO00RaUthYMak0qBgtFxSSgW5VKLOECylbYwKGkmf/vF+O2effdY+Z619zznvbyYze6/9rbW/c7LP/tb7ve/7PMfbfkANZOu7RNxD7/1oQkYi9XkXcH2pPnmYkBRAIbf8F13OeZJQ0hzbIsLwSodr46j8OVbSvuX59jlOqcNHiYWovXT5k5T/l0JLhqLTrfCtzHQrHDtqU3RWuEFOw/0rOkNEo9tdpMIlHWa7X6nwiwhxyxcxPeKuqiiq7S/ekCZe6E34qe3/lrRI0iLbGyX9WZexn2o9sH1DaVT8g7ZjrV6X+wnx1lbp/x6Ej3oVTyrsJFqlzb9PfK9MDLmI1KTcNc8wyHH4H1898wxgeH8wTRiLZWY76jDrUZjyzDDracgxrQUEwPb/dDZRuoFb4YTQd1QwGxqeVPhrqyLxLszpFNgjw7ph26aQW/kKsE7SM3Q4jqp4zQM3a7rf/BYiV9TJnYRKcysaewmwgSg26ORdxPfLZcTCfCewuvcfZ/DkIjIAJF1k+y8rXqr6gxn1/uFYLDM7qGvW04QmTZQHEP0ILXaUYxOFh6+TtpIiFV7eb2tJ1PbLJknLbM+ZV7H9V6qps9WQYd2wnUMIYb6P+NwuYaYddhOveQjZn13bebZ/KOmlVW9u+xmiEGFiyUVkMFxJ3NlNo/NLQdJSRvyBsL2yPLyiNEEtAb44yjlQ36ynCe1NlBB6WN2aKCfBrbA2ZYv0UmbKYvQrgbPDtiW1tsz26vN6LU4ivsC3EF/grQrAKn2ps4htxxcDh6mmzlYNhhLh2G6POioXeTfzmodwKzzORYFA0vF0FJpIusT2x9XFtG7EuxmzkotITTRdk2baS8xyV1u+EM4jEo8HE2XCY6EqcTciNkg6n+lmPev7uaDtv5Z0P1Nd3Ku63Ql7QtwKG7COiNTOJLYz3k70JfXLsKTCm1RRXcFMna2+y81LhFPLC70OCm+Q9i9vMRVl2F28QWqW9L+X2PraWq53IFEU0E5LnXqoW5yDIKuzaiLpPwkfhv/tfImodjq4bew+hOrohcSH+h+ITvWfH9F0JwpNmfXsJH5fPZv1LAQkPWD7+PYqHHXI2fdx7dNpkwq3/U/9XrPh+3/d9kkdlUx9VxupwgudKLwYejl92xxqV4gpZPhbeaS+Frxxk5FIfW4nun1naAxJuqvj0DNE9c9lhHyBJa3sPG+h4DnMepIZtL5QnlY4+20llAb6piwaI104OphTZ6tHmnihN0LSycARpaJtf2CfLhVttSrEygJyMW0WEZI+XbWQlC3oqu2skfqkzEZGIkNA0nuJ3MdexAf5JkKSY2Rd4pOApFeWarDKxij37zo3L5H0RkKxdinRC7MvcKXtnvb8K7Zmdr3EiCPBkkBew5T68npgbb+luVXRzIAinF0VbbaPVHj+3OwKL5u6PTCSricqyVo5lrcCO22/s2Js+yK4mChOecH2JT39QEMgF5GGSFpO1MQDfNP2N2YZ+wpiMbmA6Bj/CHCL7ceHPtEJQNJnbK+uuJtqfXlNzN1UMhpKs+MaQpeqtRNSmYRveN1OL/S3AIvcv3/8g5SKtm7bb23J70MIJeFZK8Qk/ZvtY+c6Nsuc7rU9o91gXOR2Vk0kLSGa1ZYSap4Clkv6LnBOVQOd7SeJxr6rFIY5FxJeB0N3ZpsEbLfq2d9ANEmdTPyxdfWFSHZtxewO0vW9sI4Kna0BUNsLvSF1Ktpaye8HqFchtlPS4ba/Va75CtocLNvp6DtZRERFs1lKjJyMRGoi6Wqiv+CStk7TRcCfEv4A7644p3bUMp+R9AVCxrrl8HghIQVf5Qux4JH0ZUKA8dNtd7/fsH30eGfWP5LusX3ykN/j5YQtQF8CjJJEuEAeQviK/AlR0fa3tq+pGL8X0dy7szzfg5DEf65j3GlEiXmr8/xQomJwhl+8pgup/pQoHPioiw7dJJCRSH1+neiSblcp/T9Jf0xIX+9ilqjlO8CKqqhlnnO07WVtzzdK6kfwb77zUtv3arpT4AvjmsyA+UjJCcyps9WEUtxyNvGd9gDwjKRNtt/X6zVLBHIe8EfETdBRwOWzVLTV7UTfDziaWDxWEPpv3+9yzUuBL9reLunDhPDic13GjoVKS8akkh22Z/whl2OdScG1RIh7hO2VtlcQOZH7GZGr4ISxWSFpDYCkE9kN6t/HyLOSDmdKR+tc4OnZT9ltuAh4DdFbclb518THvBtLys3ZKsJz5ERCUbdfNgPbbH/A9vvnKIme0YlOGHZ18uEy130Je9xr6b69e1lZQE4meqKun2XsWMhIpD6LFdpMnfa3YqZ4Wu2oZT6jEKAzUYmyqeSPTDRjPTrOuU04u4t0fS800dlqwp6SDiK8ftYM8LonAr9TdhF2da93KQTo7EQ/gWrLg1b+40zgOtt3SOqW72oydizkIlKf7xG+At1ea6dr1CJpnIq+o2YQd5gLkacIGZ2NRH/IdqJrvVOzaXekts5WQ64kyoXvsX1fSVY/MYDr/maDsX/IVCc6hKtplbzPU0U54HTgYwpzsG67Qk3GjoVcRGpi+5QGw5tELfMWVzjbJbW4lTA42kw0Gs4nauts1aUksJe2X6NURr6p+1n1aPgZPowoB/4FYlvtRKr7c95MbOd90va2EkF9oGJc07FjIauzaiJp1WyvtycGS5Kv6y/W9qmDm1ky35gvlVhVKMzEZtDvDcck9E60+kdK/mItITR5ecnPzFsyEqnPWbO8Ns2Ap2HUkiSdbJK03Pa8y58NMTr9qqRrCXWI9tzFKFURJj5/MQwyEqmJpANtd+Y+uo29xPbHy+PzbN/c9tpVtmf4sSdJWyHCnkQ135MMaMtnvlNUEToZqSqCpNuJfNbpRCnu88C9dTvRd1dyEamJpO8RXbY3An9ve9ssYzfbPq7zcdXzJGnRbaunReaYJpuiC3YG8O+2nyj5i+W2N4x5akMlt7PqcwhRuns+IWPydWJBudV2Zxmfujyuep4kQC4S/SDpAEJi6GDbvyVpGfA6258d1RxKZ3r7tvbTzJ/+nq5MVKnYJGN7p+31ti8iOtE/R1hnbpG0rnN4l8dVz5Mk6Z8biBLflq/P44T5UzJkchHpAds7COOZbxI1/K/qGHKspO1FgvuY8rj1fPmIp5skC4H9bX+BIupY+rQqRQ2TwZLbWQ3QlEf6BUx5hZxte1r3te09xjC9JFnI/EjSfkxJxZxEdz2qZIBkYr0mkjYReZGbgRttPzDmKSVJUijGZ9cArwYeBn4WOLdfJd9kbjISqc8HgbuLsufekvb2HA5mSZKMjEeAWwiF2x8A/0jkRZIhk5FIAyRdDHyI2MoS8WH9mO1BmN8kSdIjXTxrXmb7vPHNamGQkUhNJK0BfhU4pejytBzJ/lzSy+eJ61yS7K6kZ82YyOqs+rwNWNVaQGCXyNuby2tJkoyP9KwZExmJ1Me2f1xx8HlJg/SKTpKkOccz5VkDoaT7WEtKJiVjhkcuIvV5StLrbd/ZflDS61kAXalJMuGcMe4JLFQysV4TSa8mfB7uITycAU4g8iTn2H54XHNLkiQZF7mI1ETSLwIHAkcStegQZYWPAU/b/ta45pYkSTIuchGpSZF5/lCnx4Ok5cBVtmfzG0mSJJmXZHVWfQ6oMgkqxw4d/XSSJEnGTy4i9XnZLK+9ZGSzSJIkmSByEanP/ZJ+t/OgpHcylWhPkiRZUGROpCbF9OYWYAfTq7NeDKysa52bJEkyn8hFpCGSTgWOLk8ftv3P45xPkiTJOMlFJEmSJOmZzIkkSZIkPZOLSJIkSdIzuYgkSQ0krZH0sKSHJD1YVGK7jb1C0vsH+N43SDq34vgppQkWSWdL+mB5vELSss7xSTIMUoAxSeZA0uuANwLH2f6JpP2Jqrx+rrmn7RcGMkHA9m3AbeXpCuB2QpYnSYZKRiJJMjcHAc/a/gmA7Wdtb5X07bKgIOkESXe1nXOspK9JeqLVX1Qih7sl3QY8ImkPSZ+QdF+JcH6vjJOkayU9JulLwM+1LirpDEmPStoMrGo7/o5yzq8AZwOfKBHT4ZLeI+mR8h6fH+6vKlloZCSSJHOzAbhc0uPAl4CbbH95jnOOAU4irJT/VdId5fhxhAvfFkmrge/bfq2knwG+KmkD8EvAUcAy4AAiovicpMXAdcBpwH8AN3W+qe1NZZG63fbfAZRtrsNKFDWb8kKSNCYjkSSZA9s/JEyPVgP/Bdwk6R1znHar7edtPwtsBH65HL/X9pby+DeAt0l6EPgXYD/gCODXgBtt77S9FWj1Ir0S2GL7CUdt/t/U/BEeAtZJegswsC20JIGMRJKkFrZ3AncBdxW3vLcTX8itG7HFnad0ef6jtmMC3m17fftASW8YxJzbOJNYmM4C1khaPsh8TLKwyUgkSeZA0lGSjmg79BrgO8C3iQgF4E0dp50jabGk/YBTgPsqLr0euFjSi8r7HClpL+ArwG+XnMlBwKll/KPAoZIOL88v6DLlHwD7lGsuApba3ghcCiwB9p77p06SemQkkiRzszdwTcknvEDkI1YDrwI+K2ktEaW08xCxjbU/sLYk4o/sGHM9YSOwWZKIrbIVhEbbaUQu5LvA1wBs/7jkUe6Q9BxwN2Wx6ODzwHWS3gOcX+a4hIh8rra9rddfRJJ0krInSZIkSc/kdlaSJEnSM7mIJEmSJD2Ti0iSJEnSM7mIJEmSJD2Ti0iSJEnSM7mIJEmSJD2Ti0iSJEnSM/8PnYQshDCym+wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FcWEJUg2S8V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b2b72a50-bb2b-475c-acf4-ac78557f9e81"
      },
      "source": [
        "files = ['suicidewatch_pre_features.csv',\n",
        " 'depression_pre_features.csv',\n",
        " 'ptsd_pre_features.csv',\n",
        " 'anxiety_pre_features.csv',\n",
        " 'socialanxiety_pre_features.csv',\n",
        " 'healthanxiety_pre_features.csv',\n",
        " 'bipolarreddit_pre_features.csv',\n",
        " 'bpd_pre_features.csv',\n",
        " 'schizophrenia_pre_features.csv',\n",
        " 'EDAnonymous_pre_features.csv',\n",
        " 'alcoholism_pre_features.csv',\n",
        " 'addiction_pre_features.csv',\n",
        " 'adhd_pre_features.csv',\n",
        " 'mentalhealth_pre_features.csv',\n",
        " 'meditation_pre_features.csv',\n",
        " 'personalfinance_pre_features.csv',\n",
        " 'teaching_pre_features.csv',\n",
        " 'relationships_pre_features.csv',\n",
        " 'legaladvice_pre_features.csv',\n",
        " 'fitness_pre_features.csv',\n",
        " 'parenting_pre_features.csv',\n",
        " 'lonely_pre_features.csv',\n",
        " 'autism_pre_features.csv',\n",
        " 'divorce_pre_features.csv',\n",
        " 'conspiracy_pre_features.csv',\n",
        " 'guns_pre_features.csv',\n",
        " 'jokes_pre_features.csv',\n",
        " 'suicidewatch_post_features.csv',\n",
        " 'depression_post_features.csv',\n",
        " 'ptsd_post_features.csv',\n",
        " 'anxiety_post_features.csv',\n",
        " 'socialanxiety_post_features.csv',\n",
        " 'healthanxiety_post_features.csv',\n",
        " 'bipolarreddit_post_features.csv',\n",
        " 'bpd_post_features.csv',\n",
        " 'schizophrenia_post_features.csv',\n",
        " 'EDAnonymous_post_features.csv',\n",
        " 'alcoholism_post_features.csv',\n",
        " 'addiction_post_features.csv',\n",
        " 'adhd_post_features.csv',\n",
        " 'mentalhealth_post_features.csv',\n",
        " 'meditation_post_features.csv',\n",
        " 'personalfinance_post_features.csv',\n",
        " 'teaching_post_features.csv',\n",
        " 'relationships_post_features.csv',\n",
        " 'legaladvice_post_features.csv',\n",
        " 'fitness_post_features.csv',\n",
        " 'parenting_post_features.csv',\n",
        " 'lonely_post_features.csv',\n",
        " 'autism_post_features.csv',\n",
        " 'divorce_post_features.csv',\n",
        " 'conspiracy_post_features.csv',\n",
        " 'COVID19_support_post_features.csv',\n",
        " 'guns_post_features.csv',\n",
        " 'jokes_post_features.csv'\n",
        "]\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "\n",
        "data = pd.read_csv(os.path.join(input_dir, files[0]))\n",
        "data_agg = data\n",
        "\n",
        "for file in files[1:]:\n",
        "    data = pd.read_csv(os.path.join(input_dir, file));\n",
        "    data_agg = pd.concat([data_agg, data], axis=0, ignore_index=True)\n",
        "\n",
        "print(data_agg.author.unique().shape)\n",
        "print(data_agg.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(555349,)\n",
            "(647653, 95)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJBaczy-4Auu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c4ff7934-718a-44cb-cda8-152a35331ba9"
      },
      "source": [
        "files = ['suicidewatch_pre.csv',\n",
        " 'depression_pre.csv',\n",
        " 'ptsd_pre.csv',\n",
        " 'anxiety_pre.csv',\n",
        " 'socialanxiety_pre.csv',\n",
        " 'healthanxiety_pre.csv',\n",
        " 'bipolarreddit_pre.csv',\n",
        " 'bpd_pre.csv',\n",
        " 'schizophrenia_pre.csv',\n",
        " 'EDAnonymous_pre.csv',\n",
        " 'alcoholism_pre.csv',\n",
        " 'addiction_pre.csv',\n",
        " 'adhd_pre.csv',\n",
        " 'mentalhealth_pre.csv',\n",
        " 'meditation_pre.csv',\n",
        " 'personalfinance_pre.csv',\n",
        " 'teaching_pre.csv',\n",
        " 'relationships_pre.csv',\n",
        " 'legaladvice_pre.csv',\n",
        " 'fitness_pre.csv',\n",
        " 'parenting_pre.csv',\n",
        " 'lonely_pre.csv',\n",
        " 'autism_pre.csv',\n",
        " 'divorce_pre.csv',\n",
        " 'conspiracy_pre.csv',\n",
        " 'guns_pre.csv',\n",
        " 'jokes_pre.csv',\n",
        " 'suicidewatch_post.csv',\n",
        " 'depression_post.csv',\n",
        " 'ptsd_post.csv',\n",
        " 'anxiety_post.csv',\n",
        " 'socialanxiety_post.csv',\n",
        " 'healthanxiety_post.csv',\n",
        " 'bipolarreddit_post.csv',\n",
        " 'bpd_post.csv',\n",
        " 'schizophrenia_post.csv',\n",
        " 'EDAnonymous_post.csv',\n",
        " 'alcoholism_post.csv',\n",
        " 'addiction_post.csv',\n",
        " 'adhd_post.csv',\n",
        " 'mentalhealth_post.csv',\n",
        " 'meditation_post.csv',\n",
        " 'personalfinance_post.csv',\n",
        " 'teaching_post.csv',\n",
        " 'relationships_post.csv',\n",
        " 'legaladvice_post.csv',\n",
        " 'fitness_post.csv',\n",
        " 'parenting_post.csv',\n",
        " 'lonely_post.csv',\n",
        " 'autism_post.csv',\n",
        " 'divorce_post.csv',\n",
        " 'conspiracy_post.csv',\n",
        " 'COVID19_support_post.csv',\n",
        " 'guns_post.csv',\n",
        " 'jokes_post.csv'\n",
        "]\n",
        "\n",
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/raw_post'\n",
        "\n",
        "data = pd.read_csv(os.path.join(input_dir, files[0]))\n",
        "data_agg = data\n",
        "\n",
        "for file in files[1:]:\n",
        "    data = pd.read_csv(os.path.join(input_dir, file));\n",
        "    data_agg = pd.concat([data_agg, data], axis=0, ignore_index=True)\n",
        "\n",
        "print(data_agg.author.unique().shape)\n",
        "print(data_agg.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(636743,)\n",
            "(1115854, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GsYn-BHF6Mbj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d00a5e7a-1d49-4759-b7ab-54ca9cce49cf"
      },
      "source": [
        "327289 + sum(file_count_dict.values())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "647653"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hfpqoyc2yCc5",
        "colab_type": "text"
      },
      "source": [
        "# Entity Analysis + Computation Complexity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGI-01BW_f19",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "post = \"Mayor Cuomo may lockdown NYC. I live in Brooklyn.\"\n",
        "post = nlp(post)\n",
        "print(*[f'token: {token.text}\\tner: {token.ner}' for sent in post.sentences for token in sent.tokens], sep='\\n')\n",
        "print(post.ents)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNzk0tbd_f2A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extract location or other entities\n",
        "def extract_entity(doc, entity = 'GPE'):   \n",
        "    # classes although they have different code names like GPE and ORG: (PERSON, LOCATION, ORGANIZATION, MISC), numerical (MONEY, NUMBER, ORDINAL, PERCENT), and temporal (DATE, TIME, DURATION, SET) entities (12 classes).\n",
        "    # Adding the regexner annotator and using the supplied RegexNER pattern files adds support for the fine-grained and additional entity classes EMAIL, URL, CITY, STATE_OR_PROVINCE, COUNTRY, NATIONALITY, RELIGION, (job) TITLE, IDEOLOGY, CRIMINAL_CHARGE, CAUSE_OF_DEATH, HANDLE (12 classes) for a total of 24 classes.\n",
        "    \n",
        "    # Extract locations (don't lowercase doc, won't work for \"nyc\", only \"NYC\")\n",
        "    result = []\n",
        "    doc = nlp(doc) #nlp was loaded once above\n",
        "    entitities = doc.ents\n",
        "\n",
        "    if entity == 'all':\n",
        "        result = [n.text for n in entitities]\n",
        "        return result\n",
        "    else:\n",
        "        for ent in entitities:\n",
        "            if ent.type ==entity:\n",
        "                # append 'NYC'                 \n",
        "                result.append(ent.text)\n",
        "        return result\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQFHkf3Z_f2D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "0347085a-cbcc-4a73-b530-15d14dad53de"
      },
      "source": [
        "\n",
        "\n",
        "print(extract_entity(\"I live in Brooklyn, NYC\", entity = 'GPE'))\n",
        "print(extract_entity(\"I live in Brooklyn, Cuomo's town\", entity = 'PERSON'))\n",
        "print(extract_entity(\"I live in Brooklyn, Cuomo's town, on April 21st, 1990\", entity = 'all'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Brooklyn', 'NYC']\n",
            "['Cuomo']\n",
            "['Brooklyn', 'Cuomo', 'April 21st, 1990']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cMLgxoXqTQ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Measure computation complexity\n",
        "features = ['sentiment', 'punctuation', 'liwc', 'basic_count', 'readability',  'tfidf', 'covid19', 'suicidality', 'economic_stress', 'substance_use', 'guns', 'domestic_stress']\n",
        "\n",
        "post = \"I'm feeling terrible, what do you think I should do? I think I should call someone!\"\n",
        "\n",
        "for feature in features:  \n",
        "  print('====='+feature)\n",
        "  \n",
        "  %time feature_vector = extract_NLP_features(post, [feature])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkyjXIb5pKMl",
        "colab_type": "text"
      },
      "source": [
        "# Filter Posts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuDQtuvopNTi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/old/'\n",
        "output_dir = '/content/drive/My Drive/ML4HC_Final_Project/data/input/feature_extraction/new/'\n",
        "files = [f for f in os.listdir(input_dir) if '.csv' in f]\n",
        "\n",
        "\n",
        "for f in files:\n",
        "  df_subreddit = pd.read_csv(os.path.join(input_dir, f))\n",
        "  #pre-process to remove duplicate posters, any bots, and posts that have the word quote\n",
        "  df_subreddit = df_subreddit.drop_duplicates(subset=\"author\")\n",
        "  df_subreddit = df_subreddit[~df_subreddit.post.str.contains('(?i)bot')]\n",
        "  df_subreddit = df_subreddit[~df_subreddit.post.str.contains('(?i)quote')]\n",
        "\n",
        "  df_subreddit.to_csv(os.path.join(output_dir, f), index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}