Elmo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import math\n",
    "from numpy.linalg import norm\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances, manhattan_distances\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import wordnet as wn\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from spacy.lang.en import English\n",
    "from spacy.tokenizer import Tokenizer\n",
    "from allennlp.modules.elmo import Elmo, batch_to_ids\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FDA_Chapter</th>\n",
       "      <th>FDA_Section</th>\n",
       "      <th>FDA_Code</th>\n",
       "      <th>FDA_Subpart</th>\n",
       "      <th>FDA_Description</th>\n",
       "      <th>matched</th>\n",
       "      <th>score</th>\n",
       "      <th>HC_Code</th>\n",
       "      <th>HC_Chapter</th>\n",
       "      <th>HC_Section</th>\n",
       "      <th>HC_Subpart</th>\n",
       "      <th>HC_Description</th>\n",
       "      <th>truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Buildings and Facilities</td>\n",
       "      <td>\"211.56\"</td>\n",
       "      <td>Sanitation.</td>\n",
       "      <td>(a) Any building used in the manufacture, proc...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.773877</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR TYPE A...</td>\n",
       "      <td>Construction and Maintenance of Facilities and...</td>\n",
       "      <td>\"226.20\"</td>\n",
       "      <td>Buildings.</td>\n",
       "      <td>Buildings in which Type A medicated article(s)...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.724563</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Buildings and Facilities</td>\n",
       "      <td>\"211.58\"</td>\n",
       "      <td>Maintenance.</td>\n",
       "      <td>Any building used in the manufacture, processi...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.717186</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>\"211.63\"</td>\n",
       "      <td>Equipment design, size, and location.</td>\n",
       "      <td>Equipment used in the manufacture, processing,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.742327</td>\n",
       "      <td>C.02.005</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>The equipment with which a lot or batch of a d...</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Records and Reports</td>\n",
       "      <td>\"211.182\"</td>\n",
       "      <td>Equipment cleaning and use log.</td>\n",
       "      <td>A written record of major equipment cleaning, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.628404</td>\n",
       "      <td>C.02.007</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>(1) Every person who fabricates or packages/la...</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         FDA_Chapter  \\\n",
       "0  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "1  CURRENT GOOD MANUFACTURING PRACTICE FOR TYPE A...   \n",
       "2  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "3  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "4  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "\n",
       "                                         FDA_Section   FDA_Code  \\\n",
       "0                           Buildings and Facilities   \"211.56\"   \n",
       "1  Construction and Maintenance of Facilities and...   \"226.20\"   \n",
       "2                           Buildings and Facilities   \"211.58\"   \n",
       "3                                          Equipment   \"211.63\"   \n",
       "4                                Records and Reports  \"211.182\"   \n",
       "\n",
       "                             FDA_Subpart  \\\n",
       "0                            Sanitation.   \n",
       "1                             Buildings.   \n",
       "2                           Maintenance.   \n",
       "3  Equipment design, size, and location.   \n",
       "4        Equipment cleaning and use log.   \n",
       "\n",
       "                                     FDA_Description  matched     score  \\\n",
       "0  (a) Any building used in the manufacture, proc...        1  0.773877   \n",
       "1  Buildings in which Type A medicated article(s)...        1  0.724563   \n",
       "2  Any building used in the manufacture, processi...        1  0.717186   \n",
       "3  Equipment used in the manufacture, processing,...        1  0.742327   \n",
       "4  A written record of major equipment cleaning, ...        1  0.628404   \n",
       "\n",
       "    HC_Code HC_Chapter                    HC_Section  HC_Subpart  \\\n",
       "0  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "1  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "2  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "3  C.02.005      Drugs  Good Manufacturing Practices   Equipment   \n",
       "4  C.02.007      Drugs  Good Manufacturing Practices  Sanitation   \n",
       "\n",
       "                                      HC_Description truth  \n",
       "0  The premises in which a lot or batch of a drug...     Y  \n",
       "1  The premises in which a lot or batch of a drug...     Y  \n",
       "2  The premises in which a lot or batch of a drug...     Y  \n",
       "3  The equipment with which a lot or batch of a d...     Y  \n",
       "4  (1) Every person who fabricates or packages/la...     Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('review.xlsx')\n",
    "data.drop(['Unnamed: 13'], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['fda_desc_cleaned']=''\n",
    "data['hc_desc_cleaned']=''\n",
    "\n",
    "# Pre-processing FDA Descriptions\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "pattern1 = re.compile(r'\\b(' + r'|'.join(stopwords.words('english')) + r')\\b\\s*')\n",
    "pattern2 = '[0-9]'\n",
    "for i in range(0,len(data)):\n",
    "        sent_ref = data.iloc[i]['FDA_Description']\n",
    "#         sent_ref = sent_ref.lower() # convert to lower case\n",
    "        word_list = nltk.word_tokenize(sent_ref)\n",
    "        sent_ref = ' '.join([lemmatizer.lemmatize(w) for w in word_list]) # lemmatize\n",
    "        sent_ref = re.sub(r'\\([^)]*\\)', '', sent_ref) # remove characters which are within () and []\n",
    "        sent_ref = pattern1.sub('', sent_ref) # remove stopwords\n",
    "        sent_ref = re.sub(r'[^\\w\\s]','',sent_ref) # remove spaces (new line, tabs etc)\n",
    "        sent_ref = re.sub(pattern2, '', sent_ref) # remove numbers\n",
    "        sent_ref = \" \".join(sent_ref.split()) # remove whitespaces, if any\n",
    "        data['fda_desc_cleaned'][i] = sent_ref\n",
    "        \n",
    "# Pre-processing Health Canada Descriptions\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "pattern1 = re.compile(r'\\b(' + r'|'.join(stopwords.words('english')) + r')\\b\\s*')\n",
    "pattern2 = '[0-9]'\n",
    "for i in range(0,len(data)):\n",
    "        sent_ref = data.iloc[i]['HC_Description']\n",
    "#         sent_ref = sent_ref.lower() # convert to lower case\n",
    "        word_list = nltk.word_tokenize(sent_ref)\n",
    "        sent_ref = ' '.join([lemmatizer.lemmatize(w) for w in word_list]) # lemmatize\n",
    "        sent_ref = re.sub(r'\\([^)]*\\)', '', sent_ref) # remove characters which are within parenthesis\n",
    "        sent_ref = pattern1.sub('', sent_ref) # remove stopwords\n",
    "        sent_ref = re.sub(r'[^\\w\\s]','',sent_ref) # remove spaces (new line, tabs etc)\n",
    "        sent_ref = re.sub(pattern2, '', sent_ref) # remove numbers\n",
    "        sent_ref = \" \".join(sent_ref.split()) # remove whitespaces, if any\n",
    "        data['hc_desc_cleaned'][i] = sent_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def angular_distance(src, tgt):\n",
    "    cos_sim = cosine_similarity(src, tgt)\n",
    "    np.fill_diagonal(cos_sim, 1)\n",
    "    distance = 1 - (np.arccos(cos_sim) / math.pi)\n",
    "    return distance\n",
    "\n",
    "def cosine_sim(src, tgt):\n",
    "    similarity = cosine_similarity(src, tgt)\n",
    "    return similarity\n",
    "\n",
    "\n",
    "def manhattan_dist(src, tgt):\n",
    "    distance = manhattan_distances(src, tgt)\n",
    "    return distance\n",
    "\n",
    "\n",
    "def euclidean_dist(src, tgt):\n",
    "    distance = euclidean_distances(src, tgt)\n",
    "    return distance\n",
    "\n",
    "def vector_summation(sentences):\n",
    "    sent_len = sentences.shape[1]\n",
    "    summed_sentence = sentences.sum(axis=1) / sent_len\n",
    "    return summed_sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def theta(src, tgt):\n",
    "    similarity_ = cosine_sim(src, tgt)\n",
    "    np.fill_diagonal(similarity_, 1)\n",
    "    theta_ = np.arccos(similarity_) + math.radians(10)\n",
    "    return theta_\n",
    "\n",
    "\n",
    "def magnitude_difference(src, tgt):\n",
    "    src_len = len(src)\n",
    "    src_norm = norm(src, axis=1).repeat(src_len).reshape(src_len, -1)\n",
    "    tgt_norm = norm(tgt, axis=1)\n",
    "    difference_ = np.abs(src_norm - tgt_norm)\n",
    "    return difference_\n",
    "\n",
    "\n",
    "def triangle_area_similarity(src, tgt, theta_):\n",
    "    src_len = len(src)\n",
    "    src_norm = norm(src, axis=1).repeat(src_len).reshape(src_len, -1)\n",
    "    tgt_norm = norm(tgt, axis=1)\n",
    "    triangle_similarity_ = (src_norm * tgt_norm * np.sin(theta_)) / 2\n",
    "    return triangle_similarity_\n",
    "\n",
    "\n",
    "def sector_area_similarity(src, tgt, theta_):\n",
    "    distance_ = euclidean_dist(src, tgt)\n",
    "    difference_ = magnitude_difference(src, tgt)\n",
    "    sector_similarity_ = math.pi * ((distance_ + difference_) ** 2)\n",
    "    sector_similarity_ *= theta_ / 360\n",
    "    return sector_similarity_\n",
    "\n",
    "\n",
    "def triangle_sector_similarity(src, tgt):\n",
    "    theta_ = theta(src, tgt)\n",
    "    triangle_similarity_ = triangle_area_similarity(src, tgt, theta_)\n",
    "    sector_similarity_ = sector_area_similarity(src, tgt, theta_)\n",
    "    ts_ss_ = triangle_similarity_ * sector_similarity_\n",
    "    return ts_ss_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "options_file = \"https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json\"\n",
    "weight_file = \"https://allennlp.s3.amazonaws.com/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = English()\n",
    "tokenizer = Tokenizer(nlp.vocab)\n",
    "elmo = Elmo(options_file, weight_file, 1, dropout=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['angular_distance'] = ''\n",
    "data['cosine']=''\n",
    "data['manhattan_distance']=''\n",
    "data['euclidean_distance']=''\n",
    "data['tss']=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FDA_Chapter</th>\n",
       "      <th>FDA_Section</th>\n",
       "      <th>FDA_Code</th>\n",
       "      <th>FDA_Subpart</th>\n",
       "      <th>FDA_Description</th>\n",
       "      <th>matched</th>\n",
       "      <th>score</th>\n",
       "      <th>HC_Code</th>\n",
       "      <th>HC_Chapter</th>\n",
       "      <th>HC_Section</th>\n",
       "      <th>HC_Subpart</th>\n",
       "      <th>HC_Description</th>\n",
       "      <th>truth</th>\n",
       "      <th>fda_desc_cleaned</th>\n",
       "      <th>hc_desc_cleaned</th>\n",
       "      <th>angular_distance</th>\n",
       "      <th>cosine</th>\n",
       "      <th>manhattan_distance</th>\n",
       "      <th>euclidean_distance</th>\n",
       "      <th>tss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Buildings and Facilities</td>\n",
       "      <td>\"211.56\"</td>\n",
       "      <td>Sanitation.</td>\n",
       "      <td>(a) Any building used in the manufacture, proc...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.773877</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "      <td>Any building used manufacture processing packi...</td>\n",
       "      <td>The premise lot batch drug fabricated packaged...</td>\n",
       "      <td>0.778003</td>\n",
       "      <td>0.7665</td>\n",
       "      <td>206.003</td>\n",
       "      <td>8.37137</td>\n",
       "      <td>18.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR TYPE A...</td>\n",
       "      <td>Construction and Maintenance of Facilities and...</td>\n",
       "      <td>\"226.20\"</td>\n",
       "      <td>Buildings.</td>\n",
       "      <td>Buildings in which Type A medicated article(s)...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.724563</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "      <td>Buildings Type A medicated article manufacture...</td>\n",
       "      <td>The premise lot batch drug fabricated packaged...</td>\n",
       "      <td>0.826815</td>\n",
       "      <td>0.855605</td>\n",
       "      <td>200.205</td>\n",
       "      <td>8.23025</td>\n",
       "      <td>9.94901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Buildings and Facilities</td>\n",
       "      <td>\"211.58\"</td>\n",
       "      <td>Maintenance.</td>\n",
       "      <td>Any building used in the manufacture, processi...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.717186</td>\n",
       "      <td>C.02.004</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Premises</td>\n",
       "      <td>The premises in which a lot or batch of a drug...</td>\n",
       "      <td>Y</td>\n",
       "      <td>Any building used manufacture processing packi...</td>\n",
       "      <td>The premise lot batch drug fabricated packaged...</td>\n",
       "      <td>0.775353</td>\n",
       "      <td>0.761125</td>\n",
       "      <td>162.36</td>\n",
       "      <td>6.66179</td>\n",
       "      <td>14.0367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>\"211.63\"</td>\n",
       "      <td>Equipment design, size, and location.</td>\n",
       "      <td>Equipment used in the manufacture, processing,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.742327</td>\n",
       "      <td>C.02.005</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>The equipment with which a lot or batch of a d...</td>\n",
       "      <td>Y</td>\n",
       "      <td>Equipment used manufacture processing packing ...</td>\n",
       "      <td>The equipment lot batch drug fabricated packag...</td>\n",
       "      <td>0.82588</td>\n",
       "      <td>0.854081</td>\n",
       "      <td>121.597</td>\n",
       "      <td>4.91787</td>\n",
       "      <td>6.97141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...</td>\n",
       "      <td>Records and Reports</td>\n",
       "      <td>\"211.182\"</td>\n",
       "      <td>Equipment cleaning and use log.</td>\n",
       "      <td>A written record of major equipment cleaning, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.628404</td>\n",
       "      <td>C.02.007</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Good Manufacturing Practices</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>(1) Every person who fabricates or packages/la...</td>\n",
       "      <td>Y</td>\n",
       "      <td>A written record major equipment cleaning main...</td>\n",
       "      <td>Every person fabricates packageslabels drug sh...</td>\n",
       "      <td>0.772023</td>\n",
       "      <td>0.7543</td>\n",
       "      <td>157.146</td>\n",
       "      <td>6.33179</td>\n",
       "      <td>15.2658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         FDA_Chapter  \\\n",
       "0  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "1  CURRENT GOOD MANUFACTURING PRACTICE FOR TYPE A...   \n",
       "2  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "3  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "4  CURRENT GOOD MANUFACTURING PRACTICE FOR FINISH...   \n",
       "\n",
       "                                         FDA_Section   FDA_Code  \\\n",
       "0                           Buildings and Facilities   \"211.56\"   \n",
       "1  Construction and Maintenance of Facilities and...   \"226.20\"   \n",
       "2                           Buildings and Facilities   \"211.58\"   \n",
       "3                                          Equipment   \"211.63\"   \n",
       "4                                Records and Reports  \"211.182\"   \n",
       "\n",
       "                             FDA_Subpart  \\\n",
       "0                            Sanitation.   \n",
       "1                             Buildings.   \n",
       "2                           Maintenance.   \n",
       "3  Equipment design, size, and location.   \n",
       "4        Equipment cleaning and use log.   \n",
       "\n",
       "                                     FDA_Description  matched     score  \\\n",
       "0  (a) Any building used in the manufacture, proc...        1  0.773877   \n",
       "1  Buildings in which Type A medicated article(s)...        1  0.724563   \n",
       "2  Any building used in the manufacture, processi...        1  0.717186   \n",
       "3  Equipment used in the manufacture, processing,...        1  0.742327   \n",
       "4  A written record of major equipment cleaning, ...        1  0.628404   \n",
       "\n",
       "    HC_Code HC_Chapter                    HC_Section  HC_Subpart  \\\n",
       "0  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "1  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "2  C.02.004      Drugs  Good Manufacturing Practices    Premises   \n",
       "3  C.02.005      Drugs  Good Manufacturing Practices   Equipment   \n",
       "4  C.02.007      Drugs  Good Manufacturing Practices  Sanitation   \n",
       "\n",
       "                                      HC_Description truth  \\\n",
       "0  The premises in which a lot or batch of a drug...     Y   \n",
       "1  The premises in which a lot or batch of a drug...     Y   \n",
       "2  The premises in which a lot or batch of a drug...     Y   \n",
       "3  The equipment with which a lot or batch of a d...     Y   \n",
       "4  (1) Every person who fabricates or packages/la...     Y   \n",
       "\n",
       "                                    fda_desc_cleaned  \\\n",
       "0  Any building used manufacture processing packi...   \n",
       "1  Buildings Type A medicated article manufacture...   \n",
       "2  Any building used manufacture processing packi...   \n",
       "3  Equipment used manufacture processing packing ...   \n",
       "4  A written record major equipment cleaning main...   \n",
       "\n",
       "                                     hc_desc_cleaned angular_distance  \\\n",
       "0  The premise lot batch drug fabricated packaged...         0.778003   \n",
       "1  The premise lot batch drug fabricated packaged...         0.826815   \n",
       "2  The premise lot batch drug fabricated packaged...         0.775353   \n",
       "3  The equipment lot batch drug fabricated packag...          0.82588   \n",
       "4  Every person fabricates packageslabels drug sh...         0.772023   \n",
       "\n",
       "     cosine manhattan_distance euclidean_distance      tss  \n",
       "0    0.7665            206.003            8.37137   18.446  \n",
       "1  0.855605            200.205            8.23025  9.94901  \n",
       "2  0.761125             162.36            6.66179  14.0367  \n",
       "3  0.854081            121.597            4.91787  6.97141  \n",
       "4    0.7543            157.146            6.33179  15.2658  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,len(data)):\n",
    "    sent1 = data['fda_desc_cleaned'][i]\n",
    "    sent2 = data['hc_desc_cleaned'][i]\n",
    "    sentences = [sent1,sent2]\n",
    "    sentences = [[tok.text for tok in tokenizer(sentence)] for sentence in sentences]\n",
    "    char_ids = batch_to_ids(sentences)\n",
    "    embedding_tensor = elmo(char_ids)[\"elmo_representations\"][0]\n",
    "    embedding_array = embedding_tensor.detach().numpy()\n",
    "    enc_sentences = vector_summation(embedding_array)\n",
    "    \n",
    "    data['angular_distance'][i] = angular_distance(enc_sentences[0:1],enc_sentences)[0,1]\n",
    "    data['cosine'][i] = cosine_sim(enc_sentences[0:1],enc_sentences)[0,1]\n",
    "    data['manhattan_distance'][i] = manhattan_dist(enc_sentences[0:1],enc_sentences)[0,1]\n",
    "    data['euclidean_distance'][i] = euclidean_dist(enc_sentences[0:1],enc_sentences)[0,1]\n",
    "    data['tss'][i] = triangle_sector_similarity(enc_sentences[0:1],enc_sentences)[0,1]\n",
    "    \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### angular distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.87950516\n",
      "0.7218361\n"
     ]
    }
   ],
   "source": [
    "print(max(data.angular_distance))\n",
    "print(min(data.angular_distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gkp9P0+yvGBr/ejknZx3Hj89EKSPh4f8hKOghq4ZKS5HF/EKIs4xK8a+kJF5PSeEmLy++7tiRprIBd4NnMoGlt/m0VQgbBQxRSk2puh4DRCulHr7gMR7AWiAC8ADGKKXWXe516+qaMKWMGAz5NQ5KZ7tPRmPRNb6jhr29d42D1NnLOp3HNR1WLCs7xdGjD5Cb+xNeXjfSrt0iXF3bXmPtdYss5heiccurrGTc4cP8lJvL9ObNmde6tWzAXc8YDJCXBzk5kJtr/vPs14XX/3rflCnwzjuWrc1WIWw0MPgvIayXUuqRCx4zCugLPAG0wtwJi1JKFfzltaYCUwFatGjR/eTJkxap2RZMpgoqK3MvepjQaCy6TNDywXzE13qUUuj1S0hMfAyTqYzQ0H8TEvK41euwJFnML0Tjcri4mLsPHCC5rIx327RhavPmti6pUVMKCgquPkzl51/6NXU68PUFPz/z14WX+/WDu+6y7Geqy4cj1wH/UUr9WXX9N2CWUmr7pV63rnbCGpPy8nSOHp1OTs5aPDx6ERGxGDe3jrYuq9ZdajF/l8Auti4Nezt7hkcMx93R3dalCFFvfZ+dzbjDh3Gp2oD7RtmA26IMBti/H2Jj4fjxSwcto/HSr+HldfEw9dfrF1729ARbnldmqxBmj3lh/q1AGuaF+eOUUgcveMwHgF4p9aKmaQHAbsydsOxLva6EsLpBKUVm5hccO/YwRmMhoaFzCAl5Gju7hjdD52KL+euCR3s9yvzb59u6DCHqHaUUr506xXMnTtC1agPuENmAu9adPm0OXGe/du6EkhLzfS4uVw5Pf73u4wP29rb9TNfCliMqhgLzMI+oWKyUekXTtGkASqmFmqY1Bz4DmgEa5q7Yssu9poSwuqWiIpNjxx4hK+tL3N270q7dYjw8bN8pspSC8gJyS3NtXQYvbHqBVQdWceyRY7TwamHrcoSoN4qNRiYmJPBVVhbjmjblk3btcJENuK9bWRnEx1cPXadOme9zcIBu3aB37/NfLVvatjtlTTKsVVhcVta3HD36IAZDDi1azKZly2exs5P1U5ZyKv8UbRa0YXzn8Xx818e2LkeIeiG5tJThBw6wr7iY/4aHMzMkpF7PP7QVpeDkyeqBKz4eKqqGS7VsWT1wdekCjbnRKCFMWEVlZQ6JiY+j1y/F1bUjERGf4unZ09ZlNViP/vgo7+94n8MPHaaNXxtblyNEnbYpL4/Rhw5RaTKxqkMHhsgG3DVWVGQ+lHhh6NLrzfe5uEDPnucDV3Q0yLkN1UkIE1aVk7OOI0ceoKLiNCEhMwkNfRGdzsXWZTU4GUUZhM8PZ0T7ESy/Z7mtyxGiTlJK8X56OjOOHaONqyvfdepEW9mA+5JMJjh2zBy0tm0z/7l/v/l2gDZtzGHrhhvMf3bqZD7cKC5NQpiwOoMhn+PHZ3L69Ce4uLQjImIxXl59bF1WgzN7w2z+u+W/7J22V7ZfEuIvyk0mHj52jE9On+ZOPz+Wt2+PZ31c2W1BeXmwffv5DldcnPk2MJ9VGB1dvcslDcSrJyFM2Exu7i8cOfJ/lJefIijoUcLDX0Gnc7N1WQ1GbmkuYfPDuDn0ZtaMXWPrcoSoMzLKyxl58CBbCwr4V9UG3HaNfP2X0QgHD57vcMXGQkKC+T5Ng44dq3e5IiIsP02+MbhcCJNfCYRF+foOpGfPAyQlzSItbT45Of+Pdu0+wcfnZluX1iD4uvgy84aZzNk0h+1p2+kVdOl9T4VoLHYUFDDiwAHyDAa+7NCB0U2b2rokm8jMrL6Oa/t2KC4239ekiTlo3Xef+c+ePc2dL2Fd0gkTVnPmzB8kJEyirOw4zZtPIzz8deztZXPc61VYXkj4O+F0DezK+pj1ti5HCJtalpHBlCNHCHR05LvISKLcG8dA44oK2Lu3+lquEyfM99nbQ1TU+UOKN9wArVo1nhERtiadMFEneHv3o2fPfZw48TypqW+Tk/MD7dp9hK/vYFuXVq95OHkwq+8sZv4yk9+Tf6d/aH9blySE1RlMJmYlJfFmaioDvL35skMH/BvoBtxKQWpq9S7Xrl1QXm6+v3lzc9B68EFz8OrWDeRchLpJOmHCJvLzYzlyZCIlJQkEBk6iVas3cXCQLUOuVWllKa0XtCbMO4w/J/4ps49Eo5JXWcnYQ4dYn5fHw0FBvNWqFQ4NaDFTaak5ZF3Y5UpPN9/n5ATdu1dfyxUcbNt6RXXSCRN1jpdXb7p3j+fkyZc5dep1cnN/pG3bD2nSZJitS6uXXBxceL7f80xfN52fEn/i9ja327okIaziUNUG3CfLyvi4bVum1PMhVUqZ91W8sMu1d69530WA8HAYMOD8GYtRUdBAG36NgnTChM0VFu4iIWESxcX7aNp0HG3avIODg5wHfbUqjBVEvBuBt7M3O6fuxE5rOJ0AIS7mu+xs7jt8GDc7O77t1Ik+Xl62LumqFRTAjh3VQ1d21e7Jbm7Qq9f5Dld0NDTScwzqNemEiTrNw6M73bvv4NSp1zh5ci55eRto0+Y9mjYdZevS6hVHnSMvDniRCWsm8O3hbxnVQb5/omEyKcUrJ08yJzmZHh4erO7YkeB6sC+OyWQeCXHhiIiDB83dL4D27WHYsPNdro4dQba1bNikEybqlKKifSQkTKKoaBf+/qNo0+ZdHB0DbF1WvWE0GYn8wDy0df/0/ejs5F9w0bAUGQzcn5DAN9nZ3BcQwEdt29bpDbhNJli9Gj76yBy6CgrMt/v4nD9TsXdvc8fLW5bFNkiX64TJ8QpRp7i7d6Zbt1jCwl4jO3st27d3QK9fTn37ZcFWdHY6Xr75ZQ5nH2b5ftnKSDQsJ0pL6RMfz+rsbN5s1YolERF1NoApBWvXms9MHDXKvM5r3Dj47DNzNyw7G378EebMgUGDJIA1VtIJE3VWcfFhjhyZREFBLH5+w2jT5j2cnUNsXVadZ1ImenzUgzNlZ0h4OAFHnazaFfXfb3l5jD54EBPwRYcODPL1tXVJF6UU/PyzOVzt2AGtW8MLL8A//ymHFhsr6YSJesnNrT1du26mVau3yMvbQGxsKHv3DkGvX4HRWGLr8uosO82OubfM5cSZEyyOX2zrcoS4LkopFqSmMmjvXgIdHdnRrVudDWC//QY33gi3326eVr9oERw+bJ5KLwFMXIx0wkS9UFqazOnTn6DXL6O8/CQ6nTv+/qMICIjB23sAmpwJWI1Sips+vYkTZ06Q+EgiLg4uti5JiKtWbjIx/ehRPs3I4G4/P5a2b49HHdyAe/NmeP552LQJgoLguedg0iQZHSHMpBMm6j0Xl1DCw+fSu3cSXbpswt9/DFlZ37B3763ExoaSlDSb4uLDti6zztA0jVdueYX0wnTe3/G+rcsR4qqdLi9nwJ49fJqRwZyWLfm2U6c6F8C2b4fBg+Gmm8wdr/nzITERpk2TACZqRjphot4yGkvJyVlLRsYScnN/Box4ePQgICCGpk3H4ugoA3UGLR1EfEY8SY8m4eEk+3SK+mF71Qbc+QYDn7dvz0h/f1uXVE18vHnN1/ffmzfCfuYZ8xZBsjWQuBjphIkGSadzoWnTMXTuvI4+fdJo1eptlDKRmDiDbduC2L9/GJmZX2E0ltm6VJuZe8tcskuymRc7z9alCFEjn2dk0C8+Hkc7O7Z261anAtj+/XDPPeYzHrdsgVdegaQkmDlTApi4NtIJEw1OcfFBMjKWotcvo6IiDZ3Oi6ZNRxMQMB4vr76Nbv3Y8FXD2Zi8kRMzTuDrUjcXNAthMJl4KimJeamp3Fy1AXeTOnJMLyEBXnoJvvgCPDzg8cfNX/VwQL+wgct1wiSEiQZLKSN5eRvR65eSlfUNJlMxzs6hBATEEBAQg6trG1uXaBX79fuJWhjFM32f4bXbXrN1OUL8TU5lJWMOHuTXM2d4NCiIN+rIBtzHj5vD1/Ll4OICM2bAk09CHT05U9RREsJEo2c0FpOVtRq9fgl5eb8CJjw9e1etHxvT4PeqvPfbe1mTsIbjjx4n0D3Q1uUIcc6BoiLuPnCA1PJyFrZty8RmzWxdEidPwr//bR6s6ugIDz0ETz8NdejIqKhHJIQJcYHy8jT0+hXo9UsoLj6Apjng53cHAQHj8fMbip2dk61LrHWJuYlEvBvBQz0fYv7t821djhAArM7KIubwYTzs7VndsSO9bXx8Ly3NvM7rk09A08xnOc6aBXUgF4p6TEKYEBehlKKoaC96/VL0+uVUVuqxt/ehadOxBATE4OnZG03TbF1mrfm/tf/Hkn1LOPbIMVp4tbB1OaIRMynFv0+e5MXkZHp5ePBtp04EOdnul5+MDPjPf2DhQvNej5Mnw7PPQnCwzUoSDYiEMCGuwGQykJe3Ab1+CdnZazCZSnFxaV21fuw+XFzCbV3idTuVf4o2C9oQ0zmGT+76xNbliEaq0GBgQkICq7OzmRAQwMK2bXG20Tj57Gx4/XV4912oqIAJE8xDV0NDbVKOaKAkhAlxFQyGArKyvkGvX8qZMxsB8PK6kYCA8fj7j8bBof7utDvjxxm8t+M9Dj90mDZ+jePEBFF3HC8t5e79+zlcUsKbrVoxIzjYJt3mvDx4803zcNWSErj3XvPcr9atrV6KaAQkhAlxjcrKTqHXL0evX0JJSQKa5oS39014evbG07M3Hh7RODo2sXWZNaYv0hP+Tjh3t7ubFSNX2Loc0YhsyM3lH4cOAfBlhw7cZoNTDAsKYN48eOstyM+HMWPMm2u3b2/1UkQjIiFMiOuklKKwcBd6/TLy83+nqGgfYALAxaV1VSi7AU/P3ri5RWJn52Dbgi9j9obZ/HfLf9k7bS+RAZG2Lkc0cEop5qem8uTx47R3deW7yEhauVh3L9OiIvMhx//9D3JzYcQI8+iJSPnxF1YgIUyIWmYwFFFUtIuCglgKCmLJz99GZaUeADs7Fzw8epzrlnl69sbJqbmNKz4vtzSX8PnhDAgdwJqxa2xdjmjAyoxGph09yud6PSOaNOHziAir7v9YWgoffGBedJ+VBXfcAS+/bJ54L4S1XC6E1a3dUIWoJ+zt3fH27o+3d3/A/Nt+WdnJc6GsoCCW1NR5KFUJgJNTi2qhzN29Kzqds01q93XxZWafmTy/8Xm2p22nV1Avm9QhGra08nLuOXCA7YWFvBgayvMtW2JnpfVf5eXw0Ufw6qvmMx8HDjSHr969rfL2QtSYdMKEsBCjsYyioj3Vgll5+UkANM0Bd/eu1YKZs3Oo1RYpF5YXEv5OOF0Cu/BLzC9WeU/ROGRVVPB+ejoLUlMpM5lY2r49I6w05bSiAj79FObOhdRU6N/fPHT1ppus8vb1RmVlJampqZSVNd59dS3B2dmZ4OBgHByqL0eRTpgQNqDTOePl1Rsvr/O/fpeXp1NQEHculJ0+/TFpae8A4OAQUC2UeXj0wN7e3SK1eTh5MPvG2Ty5/kk2JW9iQOgAi7yPaDyOlJTwdkoKn+v1lJlM3OHry39btaKjm5vF39tggKVLzd2u5GS44QbztPtbbjEPXRXVpaam4uHhQWio9X7xa+iUUuTk5JCamkpYWFiNnyedMCFsyGSqpLj4QLVuWWnp0ap77XBzi6wWzFxd29baBuSllaW0XtCaUO9QNk/cLP8Yi6umlOLP/HzeTElhbU4OTppGTGAgTwQH094K4ctohFWrzIvsjx2DHj3Mna/BgyV8Xc7hw4eJiIiQv/O1TClFQkIC7f9yuq10woSoo+zsHPDw6IqHR1eCgqYDUFmZU61blpm5ktOnPwTA3t4HT8/oqn0vJ+DiEnrN7+3i4MLz/Z5n+rrp/Jj4I0PbDK2NjyQaAYPJxDfZ2byRksLOwkL87O15vmVLHgoKIsDR0eLvbzLBN9/Aiy/CoUMQFQXffQfDhkn4qikJYLXvWr6n0gkToo5TykRJSUK1bpl5z0sdgYGTadnyWZydQ67ptSuMFbR/rz1eTl7snLoTu1rqsomGqdBgYNHp08xLTeVkeTltXFx4IjiY8YGBuFph6r1S5rD1wguwbx906GDugt1zD9jJj26NHT58+G/dGltYvXo199xzz7nOXENwse/t5Tph8mMrRB2naXa4uXWgWbNJtGv3ET177uOGG1Jo1mwqGRmLiYtrzbFjj0CYdtoAACAASURBVFJefvqqX9tR58iL/V8kPiOebw9/a4HqRUOQWlbGM8ePE7JtG48fP06IszNrOnUioVcvpgUFWTyAKQU//AA9e5pnfJWWwvLl5iA2apQEsPpq5cqV3Hjjjaxatcpi72E0Gi322rVBfnSFqIecnIJo2/Y9oqOPERg4nrS094mLCycxcSYVFVlX9VrjIsfRwb8DczbOwWiq2/9gCevaW1TE+MOHCYuL442UFAb5+hLbrRt/du3K3U2aWHzkhFKwYQP07Wue8ZWbaz778dAhGDcObLTlpKgFRUVFbNmyhUWLFp0LYUajkZkzZxIZGUnnzp1ZsGABADt27KBPnz5ERUXRq1cvCgsL+eyzz3j44YfPvd6dd97Jpk2bAHB3d2fOnDlER0ezbds2Xn75ZXr27EmnTp2YOnUqZ48AJiYmcttttxEVFUW3bt04fvw4MTExfPfdd+de995772Xt2rUW+z7ImjAh6jFn55a0a/cxLVrMIjn5ZVJT3yY9fSHBwY8SEjITB4crbw2js9Px8oCXGfXVKJbvX874qPFWqFzUVUopfs7N5c3UVDbk5eFmZ8eDzZvzWHAwYVacdP/HH+bNtP/4A0JC4MMPYeJEcKi7m1HUS489Bnv21O5rduli3h7qctasWcOQIUNo27Ytvr6+7N69m7i4OE6cOEF8fDz29vbk5uZSUVHBmDFj+OKLL+jZsycFBQW4XOHnsLi4mE6dOvHyyy8D0KFDB+bMmQNATEwM33//PcOGDePee+9l1qxZjBgxgrKyMkwmE1OmTOHtt9/m7rvvJj8/n61bt/L555/XyvflYqQTJkQD4OLSivbtP6dnz4M0aTKMU6f+Q2xsKCdOvIjBkH/F59/T/h66NevGC5teoMJYYYWKRV1TbjLx6enTdN65k9v37+dgcTGvhYWRcsMNzG/TxmoBLDbWPFy1f3/zGY8LFpj/nDpVAlhDsnLlSsaOHQvA2LFjWblyJRs2bGDatGnYV+2q4Ovry5EjR2jWrBk9e/YEwNPT89z9l6LT6Rg5cuS56xs3biQ6OprIyEh+++03Dh48SGFhIWlpaYwYMQIwz/hydXWlf//+JCYmkpmZycqVKxk5cuQV3+96SCdMiAbEzS2CDh1W0qLFsyQnv8DJky+RljafkJCZBAU9ir29x0Wfp2kac2+ey9AVQ1m0exHTe063cuXCVnIrK1mYns6CtDQyKiqIdHPjs4gI/tm0KY5WXGy1axfMmWNe++XvD2++CdOng5W3mWx0rtSxsoScnBx+++03Dhw4gKZpGI1GNE2je/fufzvDUCl10bMO7e3tMZlM565fOHjW2dkZXdWx6rKyMh588EF27txJSEgIL774ImVlZVzupMSYmBiWL1/OqlWrWLx48fV+3Muy6N8wTdOGaJp2RNO0RE3TZl3iMQM0TdujadpBTdN+t2Q9QjQW7u6d6NTpG7p3342X102cOPEccXHhnDr1P4zGkos+Z0jrIfQN6cvcP+dSWllq5YqFtR0vLeWRY8cI2baNZ0+coLObGz937szeHj2YEBhotQC2bx8MH26e8RUba97nMSkJnnhCAlhD9fXXXzN+/HhOnjxJcnIyKSkphIWF0a1bNxYuXIjBYAAgNzeXiIgI0tPT2bFjBwCFhYUYDAZCQ0PZs2cPJpOJlJQUtm/fftH3OhvOmjRpQlFREV9//TVg7qgFBwezZo15/9zy8nJKSsz/Nt5///3Mq0qnHTt2tNw3AguGME3TdMB7wO1AB+CfmqZ1+MtjvIH3gbuUUh2B0ZaqR4jGyMOjK5GRa+nWLQ539+4kJT1NbGw4qanzMRqrb1miaRqv3voq6YXpvL/jfRtVLCxtW34+ow4coE1cHB+mpzPa35+9PXrwc1QUg3x9rTY/6tAh+Mc/zDO+Nm0yT7s/cQKeeQbcLbNRhKgjVq5cee4w4FkjR44kPT2dFi1a0LlzZ6KiolixYgWOjo588cUXPPLII0RFRTFw4EDKysro27cvYWFhREZGMnPmTLpdYld2b29v/u///o/IyEiGDx9+7rAmwNKlS3nnnXfo3Lkzffr0ISMjA4CAgADat2/PxIkTLfdNqGKxOWGapt0AvKiUGlx1fTaAUuq1Cx7zINBcKfVcTV9X5oQJce3OnNlMcvIczpzZiKNjEC1bPkuzZpOxszs/YHPwssHsPr2bpEeT8HC6+OFLUb8YleK77GzeTElha0EB3vb2TGvenEeCgmju5GTVWo4dM8/2WrEC3NzMC8OfeAJ8fKxaRqNWV+aE1VUlJSVERkaye/duvLy8ruq5tT4nTNM0naZp/7uqKsyCgJQLrqdW3XahtoCPpmmbNE3bpWmanJYlhAV5e99Ily6/ERX1G87OoRw79iBxcW05fXoRJlMlAHNvnkt2STbzYm2wWETUqmKjkffS0mgXF8fIgwdJr6hgfuvWpPTuzWvh4VYNYCdOwKRJ0L49rF4NTz1lvu3f/5YAJuqODRs2EBERwSOPPHLVAexaXHFhvlLKqGlad03TNHV1bbOL9bT/+nx7oDtwK+ACbNM0LVYpdfTCB2maNhWYCtCiRYurKEEIcTE+Pjfj7f0neXnrOXHieY4cmcLJk68RGvoCPZqPY3jEcN7Y9gYP9XoIX5crj7kQdUtGeTnvpqXxQXo6uQYDvTw8eC08nBFNmmBv5cmmKSnwyiuwaJF5rtcjj8CsWRAQYNUyhKiR2267jVOnTlnt/Wr6tzEe+E7TtBhN0+45+3WF56QCF+6lEgykX+QxPymlipVS2cAfQNRfX0gp9ZFSqodSqoe/v38NSxZCXI6mafj6DqZbtzg6dVqLvb0HCQnj2b69I89160tReQGvb3nd1mWKGjIqxYbcXMYfPkzL2FhePXWKft7e/NmlC7HdujG6aVOrBrDTp+HRR6F1a1i82Dxi4vhxePttCWBCnFXTERW+QA5wywW3KeBy+5zsANpomhYGpAFjgXF/ecx3wLuaptkDjkA08HYNaxJC1AJN02jSZBh+fneQnb2GEyfmUJj2FF/19eL9Y29zOnoGzTya2bpMcQl7i4pYptezQq8nvaICT52Oyc2a8XhwMG1cXa1Wh1Jw4ACsW2f+2rrVvJn2xInw3HPQsqXVShGi3qhRCFNKXfUpAkopg6ZpDwM/AzpgsVLqoKZp06ruX6iUOqxp2k/APsAEfKKUOnC17yWEuH6aZoe//z00aTKczMwvOZb0LM+1TyJuRxQ3dl6Mn98dVjtzTlxealkZKzIzWabXs7+4GHtN43ZfX+YFBHCnnx8uVtrPp6QEfv3VPNtr3TrzoUcwT0yfPdscwFq1skopQtRLNTo7UtO0YGAB0BdzB2wzMEMplWrZ8v5Ozo4UwjpMJgP//fk2WvI7zV3AwyOasLCX8fEZKGHMBgoMBr7JymKZXs/GM2dQQG9PT+4LCGCMvz9NHB2v+Bq14cSJ892ujRuhvNx8luPAgeb9HW+/HYL+egqWqFPk7EjLudqzI2t6OPJTYAXn53jdV3XbwGusUwhRx9nZ2XNfn6W0e7cVc7pF09/pFPv2DcbL60ZCQ/+Nj88AW5fY4FWaTPycm8syvZ7vcnIoM5lo5ezMnJYtuS8ggNZWONxYWQmbN58PXgkJ5ttbt4Zp08zBq18/sPKkC1HP6XQ6IiMjMRgMhIWFsXTpUry9vWvt9UNDQ9m5cydNmjTB3d2doqKiWnvt2lTTEOavlPr0guufaZr2mCUKEkLUHSFeIUzt/iDPbX+XQ9P34lbxOydPvsLevTfj7X0LoaEv4eXVVzpjtUgpxfbCQpbp9azKzCS7shI/e3smBQYSExBAtKenxb/fej38+KM5dK1fDwUF5n0b+/eHBx4wB682bSxagmjgXFxc2FO1c/iECRN47733ePbZZ21clfXVNIRla5p2H7Cy6vo/MS/UF0I0cLNvnM3Huz/mxT9eYcXIFQQGTuL06Q85efI19uy5CSenFvj5DcXX9w58fG5Bp7PeYvCGJKm0lGV6Pcv0eo6VluKkadzVpAn3BQQwxNfXotsImUzmvRvPdrvOrvho3tw81X7oULjtNvCQ2b3CAm644Qb27dsHwPHjx3nooYfIysrC1dWVjz/+mIiICPR6PdOmTSMpKQmADz74gD59+jB8+HBSUlIoKytjxowZTJ061ZYf5arVNIRNAt7FfOaiArZW3SaEaOAC3AOYET2D1za/xqwbZ9E5oDPBwTNo1mwKev1KcnK+JyNjKenpC7Gzc8bb+2Z8fYfi53cHLi5hti6/TsuprOTLqgX2WwsKABjg7c2sFi0Y6e+Pl31N/4m+evn55i7XunXmrldmpvlsxuho8wDVO+4wL7CXJmfD9thPj7EnY0+tvmaXwC7MG1KzYc9Go5Fff/2VyZMnAzB16lQWLlxImzZtiIuL48EHH+S3337j0UcfpX///qxevRqj0Xju8OLixYvx9fWltLSUnj17MnLkSPz8/Gr181jSFf+GV+0B+apS6i4r1COEqIOe6vMU7+94n+c3Ps93Y78DQKdzo3nzKTRvPgWTqZwzZ/4gJ2cdubnrSEx8hMTER3B1bY+f3x34+t6Bl1df7OwcbPxJbK/MaGRdbi5LMzL4ITeXSqXo4OrKa2FhjAsIoIWzs0XeVyk4fPj8mYybN4PBAN7eMGSIOXQNGQJNmljk7YWoprS0lC5dupCcnEz37t0ZOHAgRUVFbN26ldGjz28jXV5eDsBvv/3GkiVLAPN6srPT7N955x1Wr14NQEpKCseOHWtYIaxqYr6/pmmOSqkKaxQlhKhbfFx8eKrPUzy38TniUuOIDo6udr+dnRO+vgPx9R0IzKOk5Cg5OT+Qm7uO1NT5pKS8gU7nia/vIHx978DP73YcHRvPxE6TUmzOz2epXs9XmZnkG40EOjrySFAQMQEBRLm7W2SdV2mpeXPss4cZk5PNt0dGwpNPmoPXDTeABRtuoo6raceqtp1dE5afn8+dd97Je++9x/3334+3t/e5tWJXsmnTJjZs2MC2bdtwdXVlwIABlJWVWbjy2lXTv3rJwBZN09YCxWdvVEq9ZYmihBB1z4zeM5gfN5/nNj7HLzG/XPaxrq5tcXVtS0jIYxgMheTlbajqkv1AVtbXAHh49DzXJfPw6IamWXc7HWs4XFzMMr2e5Xo9J8vLcbOz4x5/f2ICArjFxwddLQcvpSApyXyY8YcfzDO8SkvBxQVuvRWeeca8vkt2fxN1hZeXF++88w53330306dPJywsjK+++orRo0ejlGLfvn1ERUVx66238sEHH/DYY49hNBopLi4mPz8fHx8fXF1dSUhIIDY21tYf56rVNISlV33ZAbI0U4hGyN3Rndk3zuaJ9U+wKXkTA0IH1Oh59vYe+PuPwN9/BEopior2nDtsmZz8EsnJL+LgEICf3+34+t6Br+9A7O0tv3GupWSUl7MqM5Olej27i4qwAwb5+vJKeDjDmzTBrRYHqRYWwo4dEBt7/isry3xfWBhMnmzudvXvbw5iQtRFXbt2JSoqilWrVrF8+XKmT5/O3LlzqaysZOzYsURFRTF//nymTp3KokWL0Ol0fPDBBwwZMoSFCxfSuXNn2rVrR+/evW39Ua7aFYe1Vq0J+1wpdZ91Sro8GdYqhO2UGcpo/U5rWnq3ZPPEzdd9CK2iIovc3J/Izf2B3NyfMBjOoGn2eHndVNUlG4qra0SdH4GhlGJdTg7vpaezPjcXE9Dd3Z2YwEDG+PsTWAtDtEwmOHKkeuA6cMB8O0C7dtC7t/mrf3+IiJBF9eLiZFir5dT6sFZZEyaEOMvZ3pnn+z3PtHXT+DHxR4a2GXpdr+fo6E9gYAyBgTGYTAYKCrad65IdPz6T48dn4uwcdu6wpbf3AHQ6yyxcv1bb8vN5OimJzfn5tHByYlaLFtwXEEB7N7fret3cXNi+3Ry2tm2DuDjzGY0AXl7msxhHjDCHrl69wNe3Fj6MEMKqarpt0YdAN8Dma8KkEyaEbVUaK4l4LwJPJ092Td2FnYXWcpWVnTq3uD8v71dMplLs7Fzw8bm1anH/HTg7h1jkvWviSEkJs5OSWJ2dTYCDAy+GhjK5WTMcrmGel8Fg7mpd2OU6csR8n50ddOp0vsvVu7e562XBsWGigZNOmOVYatsiWRMmhADAQefASwNeImZ1DN8c+obRHUdf+UnXwNm5BUFB0wgKmobRWMqZM7+Tm7uOnJx15OR8z7Fj4OYWiZ/fHfj5DcPT8warHLY8XV7Oi8nJLDp9GhedjpdDQ3k8OBj3qzjFUK+vHrh27IDiql9v/f3NQWvCBPOfPXrIkFQhGqoadcIu+kRNs1dKGWq5niuSTpgQtmc0Gem8sDMmZSJuShyeTp5We2+lFCUlCefOtszP/xOlDDg7hxEQcB8BATG4utb+njoFBgOvnzrF26mpVCrFtObNea5lS5peYePsigrYs8d8SPFs6Do7KsLeHrp2rd7lCguTtVzCsqQTZjlX2wm7bAjTNG2zUurGqstLlVIxF9y3WynVrXbKrjkJYULUDWuPrOXuVXfjYu/C6I6jmdx1Mje1uMnqi+gNhnyys9ei1y8lL+9XwISnZ28CAsbTtOkYHByub7FUucnEwvR05p48SXZlJWObNmVuWBitLnK6oVKQklK9y7V7N1TNmyQkpHrg6tpVzloU1ichzHJqO4TFK6W6/vXyxa5bi4QwIeqOHWk7+GT3J6w8sJLCikJa+7ZmUpdJjI8aT5BnkNXrKS9PQ69fgV6/hOLiA2iaA35+dxIQMB4/v6HY2V2+a3Uhk1KsyszkuRMnOFFWxi3e3vw3PJwentW7fiYT/P47LFkCP/8Mp0+bb3d2Nh9KvDB0BVn/WyLE30gIs5zaDmHnul1/7XxJJ0wIcVZJZQnfHPqGRfGL+P3k79hpdgxpPYTJXSdzZ9s7cdTVPPzUBvM8sr3o9UvR65dTWanH3t6Xpk3HEhAQg6dn9GU7dhtyc3kmKYndRUVEubnx31atGOTjU+05hw/D0qWwbJm5++XhAXfeCX37mgNX587gILs0iTqoLoQwnU5HZGQkBoOB9u3b8/nnn+Pq6npdrzlnzhz69evHbbfddtH7Fy5ciKurK+PHj7+u97mc2g5hScCTmBfk/w+YefYu4HWlVKvaKPpqSAgTom5LzE3k0/hP+WzvZ6QXpuPv6k9M5xgmdZ1Ex6YdrV6PyWQgL28Dev0SsrNXYzKV4eLShoCAGAIC7qu2yXh8YSHPJCXxS14eLZ2cmFu1n6NdVfjKyoKVK83ha+dO0Olg8GCIiYG77oLr/H+IEFZRF0KYu7v7uU247733Xrp3784TTzxx7n6j0YiuFgcbW8vVhrArneT8O3AXcGfV5WFVX3cCf1x3tUKIBqe1b2teufUVTj12inXj1tGvZT8WbF9Apw860fuT3ny06yMKygusVo+dnT1+fkPo0GEFffroadduMU5OQSQnzyEuLpz4+H7sO/k+9x+Io9uuXewqLOStVq04Eh3NfYGBVJRrfPUVDBsGzZvDjBlgNMLbb0NamnlPxrFjJYAJca1uuukmEhMT2bRpEzfffDPjxo0jMjISo9HIU089Rc+ePencuTMffvjhuee8/vrrREZGEhUVxaxZswC4//77+fpr87Zos2bNokOHDnTu3JmZM839oxdffJE33ngDgD179tC7d286d+7MiBEjyMvLA2DAgAE888wz9OrVi7Zt2/Lnn39a9LNf6Zzqj4BYda2nUAohGi2dnY6hbYYytM1QsoqzWLZvGYviF/HA9w/w2E+P2WQxv729J82aTaRZs4mUlZ0kKX0Jx9M/wyP/IcbhyBDX2+gTOpUgv2i2brFj6VL48kvzkNTmzeGJJ8xdr06drFKuEJb32GPm03drU5cuMK9mG4MbDAZ+/PFHhgwZAsD27ds5cOAAYWFhfPTRR3h5ebFjxw7Ky8vp27cvgwYNIiEhgTVr1hAXF4erqyu5ubnVXjM3N5fVq1eTkJCApmmcOXPmb+87fvx4FixYQP/+/ZkzZw4vvfQS86pqNhgMbN++nR9++IGXXnqJDRs2XOc35NKu1AmbAOzSNG2Vpmn3a5oWaLFKhBANlr+bP4/f8Dj7p+8nbkoc46PGsyZhDf0/60/bd9vy2p+vkVaQZrV6io1G3tTDDWkDGG74hB/9vsI/cArNy7aTdGg469YF8eWXM4iL28Vddyl++QVOnYL//lcCmBC1obS0lC5dutCjRw9atGjB5MmTAejVqxdhYeYlAuvXr2fJkiV06dKF6OhocnJyOHbsGBs2bGDixInn1pD5/mW7CE9PT5ydnZkyZQrffvvt39aa5efnc+bMGfr37w/AhAkT+OOP8wf37rnnHgC6d+9O8tl5MhZy2U6YUmoagKZpEcDtwGeapnkBG4GfgC1KKaNFKxRCNBiaptErqBe9gnrx1uC3zi3m/9dv/+K5jc9ZfDG/wWTi04wMXkhO5nRFBXf7+fG0Xzh7v3PjoaWj2LHjbaKjf+Lee5dyzz0LGTnyHVxd2xMQMJ7KynvR6Ww3oV8Ii6hhx6q2ubi4sOciHTi3C7b7UkqxYMECBg8eXO0xP/3002W75/b29mzfvp1ff/2VVatW8e677/Lbb7/VuDanqr1edTodBoNlx6HWaOMLpVSCUuptpdQQ4BZgMzAaiLNkcUKIhsvVwZWYqBg23b+JY48cY/aNs9mbsZeRX44k+K1gnvz5SQ5mHqyV91JKsSYri8idO5l69CihTs68UtgFuxciGRDqxoMPQmEhvPqqI198cRfTp3/FjTdm0Lbth9jb+3LixGxiY1uyZ8+tZGR8jsFQWCt1CSEubfDgwXzwwQdUVlYCcPToUYqLixk0aBCLFy+mpKQE4G+HI4uKisjPz2fo0KHMmzfvb2HPy8sLHx+fc+u9li5deq4rZm012mdD07RWQKpSqhyIBloDzyul/n6gVQghrlJr39bMvWUuLw14ifXH17MofhELti/grdi3iA6KZlLXSYztNPaaJvNvyc/n6ePH2VpQQEvNlSFbOhL3VhO25WoEBMDDD8P48RAVVX1SvYODD82bT6V586mUlh5Hr19GRsZSEhLux85uOk2a3ENgYAw+PrehafXvLC4h6ropU6aQnJxMt27dUErh7+/PmjVrGDJkCHv27KFHjx44OjoydOhQXn311XPPKyws5O6776asrAylFG+//fbfXvvzzz9n2rRplJSUEB4ezqeffmrNj3ZOTTfw3gP0AEKBnzFv5N1OKTXUotVdhIyoEKJxuHAx/8Gsg+cm80/qMol+LftdcTH/oeJiZiclsTYnB49yR1y+CCXz80CcHe0YMcK8wH7gQPPWQTWllKKgYBt6/VIyM1dhMJzB0bEZTZuOIzBwPO7una/zUwtheXVhREVDVatzwi54gd1KqW6apj0FlCmlFsjEfCGENSil2JG+g8Xxi1l5YCUF5QW09m3NxC4TmRA14W+T+dPKy5l99ATLsjOwK9dhXNYCvglmQG8d48fDyJHgWQtbXZpM5eTkrCMjYwm5uetQyoCLS1u8vPrg6dkbT8/euLp2xM7uKlKeEFYgIcxyLBXC4oB5wLPAMKXUCU3TDiilrH6ekIQwIRqvs5P5F+9ZzKbkTdUm80eHDOHxrRl8q6ViRMGaIFrFtmDSSEfuvRdatrRcXRUV2WRlfUFu7noKCrZRWZkFgJ2dG56ePc+FMk/P3jg6BliuECFqQEKY5VgqhHUApgHblFIrNU0LA8Yopf5TCzVfFQlhQggwT+ZfvPszPt61nGy/HtBiPDh64BDnxD9zu/DwKBd69Ki+zssalFKUlZ2goCD23FdRUTxKmc+ycnYOqxbK3N27XNWelkJcLwlhlnO1IaxGfXKl1CFN054BWlRdPwFYPYAJIURWFvz0E3z7awt+cppC2eDbwbcSp8xTGFKeprIsgYQ2vYi3m0zb8jF4OXtZtT5N03BxCcfFJZyAgHEAGI2lFBXFV4WybeTn/0lm5sqqxzvh4dHtgmB2A05OwVYbYCuEsJ2adsKGAW8AjkqpME3TugAvK6XusnSBfyWdMCEaF6UgPt68PdAPP0BsWgmMTIUhGeBkoluFLy91COHOYB+yirNYvn85i+IXcSDzwFUv5remsrJUCgvjznXLCgt3YjKVAeDo2Lxat8zDozs6neyLJGqHdMIsx1KHI3dhng+26exifE3T9iulIq+/5KsjIUyIhq+wEDZsOB+8Tp9W0Dkf76kp5HfIwV7TGB8YyBMhwXS4YLjjWUopdqbvZFH8onOL+Vv5tGJS10kXXcxfF5hMlRQX76OgIJb8/G0UFMRSVna86l4d7u5R1bplLi6t6lSoFPWHhDDLsdjCfKVU9IVnRGqatk8pZfXzsSWECdEwHT1qDl3r1sEff0BlJXh4m+g4LZvMASkkORXia2/PQ0FBPBQURIBjzdZRlVSW8O3hb1kUv6jaYv5JXSYxrN0wi0zmry0VFVkUFFzYLduO0WgeFGtv71etW+bp2RN7e+seehX1U10IYa+88gorVqxAp9NhZ2fHhx9+SHR0dK289tChQ1mxYgXe3t688847fPDBB3Tr1o0xY8Zw6NChcxt+X0yfPn3YunUrycnJbN26lXHjxl3Ve1sqhC0CfgVmASOBRwGHs9saWZOEMCEahvJy+P13c6dr3TpITDTf3qEDDLzLgHFwBt87pZJcXkZrFxeeCA5mQmAgrrprH4x6PPc4n+75lM/2fEZaYRpNXJsQ0zmGyV0n07Fpx1r6ZJajlJHi4sMXLPrfRknJoap7NVxdO+Dp2Rtv7/74+g7B0dHfpvWKusnWIWzbtm088cQTbNq0CScnJ7Kzs6moqKB58+a1/l4RERH8+OOP5/ajrKlNmzbxxhtv8P3331/V8642hNVo2yLgEaAjUA6sAPKBGVdVmRCi0UtLg48/huHDwc8PBg+GDz+Etm3hvfdg27Fyhq09zud3xPIuiQQ7O7G69y2X+QAAIABJREFUY0cSevVielDQdQUwgFa+rZh7y1xOPnaSH8b9wIDQAby7/V06fdCJ6E+i+XDnh+SX5dfSp619mqbD3b0TzZtPISLiE3r1Okjfvnl07rye0NCXcHZuSXb2ahISxrN1awC7d99AcvK/KSzcTU1+4RbCGv5/e/cdHlWVPnD8ezJJSEgvFJMQCAgCQkLoVrAA9oaIWMG2uD/Fsu7aEEEXxbVhWwUFAQVdxIIrugIaERWkBUKvCRBqem8zc35/3CQkkISQZHJnJu/nee6TO3fuvXnnEG7enHvue44cOUJ4eHjlHI3h4eFERETQqVMnnnzySQYOHMjAgQPZU/6XWVpaGiNHjmTAgAEMGDCA33//HTCmJxo3bhy9e/cmNjaWL7/8EoBOnTqRnp7O+PHj2bdvH9dddx1vvvkmc+bM4aGHHgLg2LFj3HjjjcTFxREXF8cff/wBgL+/PwBPPfUUK1eupE+fPrz55ptcdNFF1aY/uuCCC0hKSmp0W9S3iuDVWutnMeqEAaCUGgV80egIhBBuy2aDP/88cZtx0yZje3S0MVXQ1VfDJZfAHns+rx88yKOHjmPTmpFt2vC3Dh0Y1BRVVWtg8bBwZdcrubLrldUG849fMp7HfnyMm3vezL3x9zrdYP6aeHkFExo6jNDQYQBobScvbwOZmUvIyFhCSsokUlIm4e19FqGhVxEWdjUhIZfj6RlgcuTCGTy6ezcb8/Ob9Jx9/P2Z3rVrre8PHz6cF154gW7dunH55ZczevToyrkbAwMDWbNmDfPmzePRRx/lu+++45FHHuGxxx7jwgsv5MCBA4wYMYLt27fz4osvEhQUxObNmwHIysqq9n0++OAD/ve//5GQkEB4eDhz5sypfG/ChAkMGTKEr7/+GpvNRv5JbTBt2rRqPWGhoaHMmTOH6dOns2vXLkpKSoiNbfyIrPomYU9zasJV0zYhRAuXmWmUkFiyxPiamQkWC1xwAbzyipF49ewJoFmalcUNuw+yLCsLPw8PHoyI4JGoKDr7+jZbvG382vDo4Ed5ZNAjrDu8jtmJs1mwZQGfJH3i9IP5a6KUB4GB/QkM7E+nTs9TWnqMzMz/kZGxhLS0Lzh6dBZKeREUdDFhYVcTFnY1rVt3Mzts0YL4+/uzfv16Vq5cSUJCAqNHj2baNKPq1ZgxYyq/PvbYYwAsX76cbdu2VR6fm5tLXl4ey5cv5/PPP6/cHhISUu8Yfv75Z+bNmweAxWIhKKju8ZSjRo3ixRdf5NVXX2X27NmMHTu23t+rLnUmYUqpK4GrgEil1NtV3goErE0SgRDCpWkNmzef6O1atQrsdmjTBq65xki6hg2Diutjid3O3KPHeCM1lc0FBZzl7c3LMTH8JSKCEC8v0z6HUooBkQMYEDmA10e8XjmY/9mfn+W5hOdcZjD/yby929G+/d20b383dnsZOTm/k5n5PRkZS9i793H27n0cX9+zCQ01ErLg4Ivx8GhldtiimdTVY+VIFouFoUOHMnToUHr37s3cuXMBqvU8V6zb7XZWrVqF70l/nGmtm62nunXr1gwbNozFixezcOFCmmps+unGhB0G1gHFwPoqy7fAiCaJQAjhkrZtgwcfNG4txsXBM89AcTFMnAirV8PRozB3Ltxyi5GAZZaV8fL+/cSsXs24nTvRwJzu3UkZPJinOnY0NQE7WWuv1twRewcJdyew5+E9PH3h02w6uombv7iZyDciefzHx9lyfIvZYZ4xDw8vQkKG0qXLvxg4cCuDBu2ja9d38fXtypEjM0hKGs5vv4WxefMNHD78ISUlh8wOWbihnTt3snv37srXGzdupGP5vGL/+c9/Kr+ed955gHH78t133622f03bT74dWZfLLruM999/HwCbzUZubm619wMCAsjLy6u27b777mPChAkMGDCA0NDQen+vutSZhGmtN2mt5wJna63nVlm+0lrX/9MKIdzGrl1wxx3QqxfMmwcDB8KsWXD4MKxbB1OmwKBB4FF+ddlXVMSE3bvpsGoVzyQn08vPjx9jY0nq35+727fH26O+zweZo+pg/h9u/6FyMH/v93u7xGD+uvj6xhAZ+X/Exn7PBRdk0KvXf2nf/i7y8xPZtesBVq2KYt26ePbtm0hOzh9obTM7ZOEG8vPzufvuu+nZsyexsbFs27aNyZMnA1BSUsKgQYN46623ePPNNwF4++23WbduHbGxsfTs2ZMPPvgAgIkTJ5KVlUWvXr2Ii4sjISGh3jG89dZbJCQk0Lt3b/r168fWrVurvR8bG4unpydxcXGVcfTr14/AwEDGjRvXBK1gqG+Jiq7Ay0BPwKdiu9a6c5NFUk9SokIIcyQnwwsvwCefQKtW8PDD8MQTEB5e8/6rc3J4PTWVr9LSsCjFbW3b8niHDsSWP33kytIL0/k06dNqlflv7nkz98Tfw5COQ5x+MP/paK0pKNhaObg/J+cPwIanZxihoVcQFnY1oaEj8PJqmt4A0bzMLlFRm06dOrFu3TrCa7uomOzw4cMMHTqUHTt24FHLH48OmTsS+Bh4HngTuAQYB7j2VUYIUS8HD8I//wmzZxsD7CdMgCefhHbtTt3XpjXfpqfz+sGD/J6bS7CnJ/+IjubhyEgiWrnPOKPw1uF1DuYf12ccY/uMdZnB/CdTSuHv3wt//15ERz9JWVkWWVlLychYQmbmDxw/Ph/wIDDwvMrB/X5+vV0++RSiNvPmzePZZ5/ljTfeqDUBa4h6T1ukte5XdaoipdRKrfVFTRZJPUlPmBDN48gReOklmDnTGHz/wAPGuK+a6ikW2mzMOXqUN1NT2VNURCcfHx6LiuKe9u3x96zv33quraIy/+zE2SSkJOChPBjRZQT3xt/rcoP566K1jdzctZW9ZPn5iQC0atWhSgmMS7FYTp1OSjgHZ+0JcweOqpj/O3ARsAj4GTgETNNan3Oa464A3gIswEda62m17DcAWA2M1lovquuckoQJ4VjHjxulJP79b7BaYdw4Y7B9dPSp+x4rLeXdQ4f496FDZFqtDAwI4IkOHbgxPBxPJx/r5Uh7M/cyZ+McPt74cWVl/jt638G9fe+lV9teZofXpEpKDpOZ+QMZGUvIylqGzZaPUq0IDh5KRMQDhIffKD1kTkaSMMdxVBI2ANgOBAMvAkHAK1rrP+s4xgLsAoYBqcBaYIzWelsN+y3DeAJztiRhQpgjIwNeew3eeQeKiuDOO2HSJOhcy8jPxenpjN66lVKtuT48nL9FRXFBUJD8wq3CZrexbN8yZiXOYvGOxZTZyxgQMYB74+/l1l63EuTjXnM92u0lZGevJDPze9LTv6G4OBl//3g6dZpCWNg18rPhJCQJcxyHJGGnHKSUJ0av1fw69jkPmKy1HlH++mkArfXLJ+33KFAGDAC+kyRMiOaVnQ1vvmks+flw663w/PNwTh393L9kZXFFUhJx/v580qMH3Vq3br6AXZS7D+Y/md1u5fjxBaSkTKG4eB8BAQOJiXmBkJDhbvdZXY0kYY7TpHNHKqUClVJPK6XeVUoNV4aHgD3ALaeJJRI4WOV1avm2quePBG4EPjjNuYQQTSwvD6ZOhZgY46nH4cMhKQkWLKg7AVufl8d1W7bQxdeX72NjJQGrp4rB/Enjk1hz3xrujrubxTsXc8ncS+j6Tlem/jqV1NxUs8NsMh4enrRvfxcDB+6gW7cPKS09SlLSFSQmXkRWVv1LCQjhzk43aOMT4BxgM3AfsBQYBdygtb7+NMfW9KfOyd1u04En9WmKzyilHlBKrVNKrUtLSzvNtxVC1KWgAF591Ui+Jk6Eiy6CxERYtMio/VWXnYWFXJGURKinJ0vj4ghzogKrrqKiMv/717zPkb8d4ZMbP6FDUAcmJkyk4/SOXDX/KhZtW0SprdTsUJuEh4cXERH3MWjQLrp2fY/i4mQ2bbqUjRsvJSfnd7PDE80sIyODPn360KdPH9q3b09kZGTl69LS6j/z06dPp7Cw8LTnHDp0aI0V7IcOHco555xTef6bb765yT5HUzndY0udqzwN+RGQDkRrrfPqPgwwer46VHkdhVGBv6r+wOflXdPhwFVKKavW+puqO2mtZwIzwbgdWY/vLYQ4SXExfPABTJsGx47BFVcYhVUHDqzf8QeLixm2aRMKWBYXR6QblZwwS0Vl/jti72Bv5l4+3vgxczbOYdQXo9xuML+HRysiI/9K+/bjOHx4BgcOvExi4oWEhIwgJuYFAgPr+YMoXFpYWFhlxfvJkyfj7+/PE088UeO+06dP54477qB1I3rb58+fT//+Nd4JBMBqteJZ5Qnuk1/X97iGOt0ZyipWtNY2pVRyPRMwMAbid1VKxWA8TXkrcFvVHbTWMRXrSqk5GGPCqiVgQojGKSkxKtpPnWpUtb/0UvjyS2NC7fpKLy1leFISOVYrv/TpQ1e5BdnkKirzTxk6haV7lzJ742zeW/se0/+c7laD+S0WXzp0eJSIiPs5dOjfHDjwChs2DCIs7Fo6dXqBgIA+ZocomtlPP/3EE088gdVqZcCAAbz//vvMmDGDw4cPc8kllxAeHk5CQgIPPvgga9eupaioiJtvvpkpU6Y06PuNHTuW0NBQEhMT6du3LxkZGdVe33nnnYwfP57CwkK6dOnC7NmzCQkJYejQoZx//vn8/vvvXHfddfztb39r9Gc/XRIWp5SqmFBJAb7lrxWgtdaBtR2otbaWjx/7EaNExWyt9Val1Pjy92UcmBAOVFZmzN344otw4ABceCHMnw9Dh57ZefKsVq7avJmU4mJ+jI0lPiDAIfEKg8XDwpVdr+TKrleSVpDG/M3zmZU4i/FLxvPYj48xsudI7o2/l4s7XoyHct0yIBaLH9HRfyciYjyHDr3NwYOvsX59POHhI4mJmYKf37lmh9gi7N79KPn5G5v0nP7+fejadXq99i0uLmbs2LH89NNPdOvWjbvuuov333+fRx99lDfeeIOEhITKCvpTp04lNDQUm83GZZddRlJSErGxsXWe//bbb6+c+HvYsGG8+uqrAOzatYvly5djsVgYO3ZstdexsbG88847DBkyhEmTJjFlyhSmTzc+T3Z2NitWrGho05zidHNHWrTWgeVLgNbas8p6rQlYleO/11p301p30VpPLd/2QU0JmNZ67OmejBRCnJ7NZszp2KMH3H8/tG8PP/4Iv/565glYid3OjVu2sCEvj4U9e3JxcLBDYhY1a+PX5pTB/N/u/NatBvN7egbQseOzDBqUTMeOk8jKWsratb3Ztu02Cgt3mh2ecDCbzUZMTAzdunUD4O677+bXX3+tcd+FCxfSt29f4uPj2bp1K9u2batxv6rmz5/Pxo0b2bhxY2UCBjBq1CgsFsspr3NycsjOzmbIkCE1xjN69OgGfc7atIxS1kK0AHY7LFwIkyfDzp0QHw///S9cfTU0pCKATWtu27aNn7Kzmdu9O9c66XxuLUHFYP4BkQN4fcTrfLX9K2YlzmJiwkQm/TKJEV1GcE/8PVx3znUuW5nfyyuYmJgpREVN4ODB10hNfZvjx/9Du3Z30KnTJHx9u5gdoluqb4+Vo/j51W9mheTkZF577TXWrl1LSEgIY8eOpbi4uMm+b33jqO9+9eW6fdlCCMCYUuirryAuDsaMAS8vY8zXunVwzTUNS8C01vxl506+Sk/nzS5duKt9+6YPXDRIxWD+hLsT2PPwHp6+8GmSjiUx6otRRL4RyWP/e4zNxzabHWaDeXmF0bnzywwenExU1KOkpS1kzZru7Nx5P8XF+80OTzSx4uJiUlJS2LNnDwCffPJJZS9UQEAAeXnGMPTc3Fz8/PwICgri2LFj/PDDDw6JJygoiJCQEFauXHlKPI4gPWFCuCitYckSo6p9YqJR2+uzz+CWW6CxMwY9vW8fs44eZWLHjjzaocPpDxCmON1g/nvi72FMrzEuOZjf27stZ5/9Oh06/I0DB17m8OGZHD06l7POup+OHZ+hVSvXnBxdVOfj48PHH3/MqFGjKgfmjx8/HoAHHniAK6+8krPOOouEhATi4+M599xz6dy5MxfU88miqmPCwsPDWb58+WmPmTt3buXA/M6dO/Pxxx83/AOeRoMq5ptJKuaLlq6gABIS4J//hD//NKYVev55uO02aIq5sl89cIB/7NvH+IgI/t21q1Q3dzEnV+b38fQxKvP3uYchnYa47GD+4uKD7N//T44enQ1YiIx8kOjop/D2bmd2aC5HKuY7TrNMW2QmScJES6I17N4Nq1efWJKSjMH30dHw3HNw993GLcimMOvIEe7buZPRbdowv2dPLJKAuSytNesOr2N24mwWbFlAbkkunUM6M67POMb2GUtUYJTZITZIUVEy+/e/yNGj88prjz1Ehw5/x9tbxizWlyRhjiNJmBAuLCcH1qwxkq1Vq4yersxM472AABg0CAYPNpbLL4emrJf6VVoao7ZuZVhICN/27o13Y+9pCqdRWFZYOZj/l5Rf8FAeDO8ynHvj7+XabtfSytP1Cu8WFu4mJWUKx48vwGLxIzLyETp0+BteXiFmh+b0JAlzHEnChHARNhts21a9l2v7dqP3Syno2fNEwjV4sFFyosoT1U3q56wsrkxKom9AAMvj4vBz1DcSpqtamf9Q3iHCfMO4M/ZO7om/h97tepsd3hkrKNhGSspk0tK+wGIJokOHx4mKehRPz9NWUWqxJAlzHEnChHBSaWlGz1ZFL9eaNZCfb7wXFlY94RowAIKaaSz12txcLt20iU4+Pqzo04dQmQ+yRbDZbSzbt4xZibNYvGMxZfYy+kf0r6zMH+zjWjXh8vM3kZz8PBkZi/H0DKVDh78TFfUwFkvTlhRwB9u3b6d79+4y3rOJaa3ZsWOHJGFCmK201Bi7VbWXa+9e4z2LxSgnUTXpOvvshpWSaKwdBQVcmJhIgKcnv8fHEyHzQbZI7jSYPzd3HSkpk8jM/AEvrzZERz9FRMSDWCy+ZofmNJKTkwkICCAsLEwSsSaitSYjI4O8vDxiYmKqvSdJmBAOlppaPeFav96YMBuMivXnnWcsgwdDv37gDFMvHigu5oLERMrsdn6Lj+dsZwhKmEprzfoj65m1YZbLD+bPyVlFSsoksrKW4+19FtHRzxARcT8eHvKHRllZGampqY0qdipO5ePjQ1RUFF4n3U2QJEyIJlRUBBs2VE+6UstnjvH2NpKsqr1cHTqY08tVl7TSUi5KTORoaSkr4uOJ8/c3OyThZCoG889OnE1CSoLLDubPzl5BcvJz5OSspFWrDnTsOJH27cfh4SG33UXzkCRMiEbKzIT//MeYAPvPP8FqNbZ36mQkWhW9XHFxTfvEoiPkWq1cunEjWwsLWRoby0UyH6Q4jb2Ze5mzcQ5zNs0hNTeVMN8w7oi9g3vj73WJwfxaa7KyfiIl5Tlyc1fj4xNDx46TaNfuDjw8pGa5cCxJwoRogNJS+P57YzLs776DsjI491xjKqDzzjPKRbjabD7FNhtXbd7Mr9nZLO7dm6vDwswOSbiQisH8sxNn882Ob1xuML/WmszMH0hOfo78/A34+najU6fnadt2NErJE8HCMSQJE6KetDZ6uj75BD7/3OgBa9vWqEZ/113Qp4/z3VqsL6vdzqht2/gmPZ1Pe/Tg9nZSaVw0XHphOvOT5jMrcRabj2/Gx9OHkT1Gcm/8vU4/mF9rTXr6YlJSJlFQsJnWrXvSqdMU2rS5CeXEcQvXJEmYEKeRnAyffmokX7t3g48P3HAD3HknDB/eNNMBmUlrzb07d/Lx0aO8dfbZTIhynQHWwrlVDOafnTibBZsXkFOSUzmY/68D/kqob6jZIdZKaztpaYtISXmewsId+PnFERMzhbCw6+SpQdFkJAkTogY5OfDFF8btxpUrjW1Dhhg9XiNHNl+dLkfTWvOPfft47eBBJnXsyJSTHp8WoqkUlhXy9favmZU4i4SUBKKDovli1BcMjBxodmh10trGsWOfsX//FIqK9hAQ0J9OnV4gNPQKScZEo0kSJkS5sjL48Uejx2vxYigpgXPOMXq8br/dGGjvbl45cICn9u3j/yIieEcm5BbNZM2hNdzyxS0czjvM9Cum82D/B53+Z89ut3Ls2DxSUl6gpGQ/gYHnExPzAsHBlzp97MJ5SRImWjStjZIS8+bBZ58ZlevDwmDMGCP5GjDAdcd5nc6Hhw/zwK5djGnblk979MDDXT+ocEoZhRnc+fWd/LDnB27rfRszrpmBv7fzl0Ox20s5cmQ2+/f/k9LSQwQFDSEm5kWCgy8yOzThgiQJEy3SwYNGSYl584w5Gb294dprjduNV1xhvHZni44fZ/S2bYwIDeWbXr1kQm5hCru289LKl5iUMIkebXqwaNQierRxjXkLbbZijhyZyf79L1FWdoyQkGHExLxIYOAgs0MTLkSSMNFi5OXBV18ZiVdCgtELdsEFRo/XLbdASIjZETaP5ZmZXL15M/0DAlgWF0drmZBbmGz5vuXc9uVtFJYVMuu6WYzuNdrskOrNZivk8OH3OXBgGmVl6YSGXk1MzAsEBPQ1OzThAiQJE27NZoPly41xXl99ZVS079zZ6PG64w7o0sXsCJvXmtxcLt24kc6+vqzo04cQmZBbOInU3FRGLxrNHwf/YMLACbw6/FW8La7TJW215nPo0DscPPgqVmsW4eE30qnTFPz9nb9grTCPJGHCLSUlGT1eCxbAkSMQHAyjRxvJ13nnue84r7psKyjgosREgj09+S0+nrOcvXy/aHHKbGX8Y9k/mP7ndAZHDWbhzQvpENTB7LDOiNWaQ2rqdA4efAObLY82bW6hU6fJ+Pl1Nzs04YQkCRNu48gRI+maN89Iwjw94eqrjduN11zj/FMGOdL+4mIu2LABG/B7fDydfX3NDkmIWn2x9Qvu+fYefDx9WHDTAoZ1GWZ2SGesrCyTgwdfJzX1Lez2Itq1u52OHSfRuvXZZocmnEhdSZiM1BUu4YcfjMH0UVHwxBNGMdV33zWSsm++Mep6teQE7HhpKcM2bSLfZuPH2FhJwITTG3XuKNbdv452fu0Y8ekIXljxAnZtNzusM+LlFUrnzlMZPDiZDh0eJy1tEWvWdGfHjvsoLt5vdnjCBUhPmHBq6enwyCNG71d0tNHjdeedRm0vYci1Whm6cSM7CgtZFhfHBe5SZVa0CAWlBYxfMp5Pkz7lirOv4NMbPyWstWvOaVpScpQDB17m8OEPAM1ZZ91Hx47P0qpVpNmhCRPJ7UjhkhYtgv/7P8jKgokT4amn3L+sxJkqstm4MimJ33NzWdyrF1fJhNzCBWmtmbF+Bo/87xHa+7d3iSr7dSkuTuXAgakcOTIL8CAiYjzR0U/RqlV7s0MTJpAkTLiUY8eM5OvLL6FfP/j4Y+gtDx+dwmq3M3LrVv6bkcGnPXpwm0zILVzcusPruHnhzS5VZb8uRUUp7N//IkePzkUphb9/HwIDB1cuPj6dXfrzifqRJEy4BK2N244TJkBBAUyebIz/cvXJsx3BrjX37NjB3GPHeOfss3lIJuQWbiKzKJM7v76T73d/71JV9utSWLiHo0dnk5u7mtzcNdjtBQB4eYWXJ2TnERg4mICAAXh6BpgcrWhqkoQJp3foEIwfD999B4MHw+zZ0MM1imo3u+OlpbyQksJ7hw8zuVMnnnfHCS9Fi2bXdl5e+TKTfplE9/DuLlVl/3TsdiuFhdvIzV1VnpStprBwR/m7Cj+/XtV6y1q37o5S8gydK5MkTDgtrY3bjY8/DqWl8NJL8PDDIAXeTyiy2fgtJ4elWVksy8xkU4HxV/SEyEimn3223M4QbuunfT8x5ssxLlll/0yUlWWRl7emMinLzV2N1ZoNgMUSRGDgwMressDAQXh5hZocsTgTkoQJp7R/P9x/PyxbBkOGwEcfwdlSXge71iTl57MsK4ulWVmszM6mRGu8lOKCoCCGh4QwPDSUvv7+koAJt3co9xC3LLrFZavsN4TWdoqKdpOTc6K3rKBgM2CU8PD17Vatt8zPrzceHjJuw1lJEiacit0OM2bAP/5h9IT961/GrciWPL/0oZISlmVmsjQri+VZWaSVlQFwbuvWDA8NZVhICBcHB+MnXYSiBXKHKvuNZbXmk5e3rkpv2SrKyo4D4OHRmoCA/lV6ywbLk5hORJIw4TT27oX77oNffoFhw2DmTGiJQ5ryrVZW5OSwNDOTZVlZbC8sBKCdlxeXl/d0XR4SQkRLrkArxEncocp+U9FaU1y8v1pSlp+fiNbGH3CtWnWs1lsWEBCPh4dcT8wgSZgwnc0G77wDzzwDXl7wxhtwzz0tZ35Hm9asz8szbjFmZrIqN5cyrfHx8ODioKDK3q7efn5yi1GIOuxM38nIhSPZlraNyUMnM/HiiXjIwHUAbLZi8vMTq40tKyk5AIBS3vj7x1cmZUFB59GqVbRcb5qBJGHCVDt2GAnXqlXGPI8zZkBkCyggnVxUVJl0/ZydTZbVCkC8vz/Dynu7LggMxEduMQpxRgpKC3hwyYN8kvQJI7qM4NObPiW8dbjZYTmlkpLD5Ob+WZmU5eWtxW4vAsDbu/1JvWX9sVj8TI7Y/UgSJkxhtcLrr8Pzz0Pr1vD223D77e7b+5VdVkZCdnblU4x7i4sBiGrVyki6QkK4LCSENlL2X4hG01ozc/1MJvxvgltU2W8udnsZBQWbq/WWFRXtLn/Xgr9/72pjy3x9u0pvWSNJEiaa3ebNMG4crF8PN90E770H7d1snGiZ3c6fublG0pWVxZrcXOyAv8XC0OBghoeEMCwkhHNat5aLmBAO4m5V9s1QWppepUTGKnJz/8RmywPA0zOUwMBBVXrLBuLlFWxyxK5FkjDRbEpLYdo0+Oc/ITjYSL5uvtl9er9Sior4LiODpVlZ/JKdTZ7NhgcwICCAYaGhDA8JYVBgIN4t+VFPIZpZZlEmd319F0t2L3GbKvtm0tpGYeGOar1lBQVbAQ3FJ3wdAAAdYklEQVQoWrfucVKJjJ4oJcMqaiNJmGgWGzYYvV9JSTBmjHH7MdxNhmkcLilhSkoKs44cwQZ09vGpHNd1SXAwIV5eZocoRItm13am/TaN5xKec7sq+87Aas0lL28tubmrK+uXWa0ZAFgs/gQEDKyWmHl7tzE5YuchSZhwqOJiePFFeOUVaNsW3n8frr/e7KiaRo7VyqsHDvBGaipWrRkfEcEjUVF08fU1OzQhRA1aSpV9s2mtKSraW623LD9/I2ADwMenS7WkzN8/Fg+Pljke1rQkTCl1BfAWYAE+0lpPO+n924Eny1/mAw9qrTfVdU5JwpzL6tXGk4/bt8PYsUbpiZAQs6NqvBK7nQ8OH+bFlBQyrFZubduWf8bESPIlhAuoWmX/4YEP89rw19y+yr4zsNkKycvbUGVezFWUlh4BwMPDB3//ftVuYXp5heHpGYqHh3vfSTAlCVPGDeJdwDAgFVgLjNFab6uyz/nAdq11llLqSmCy1npQXeeVJMw5FBbCpEnw5ptGuYmZM+GKK8yOqvHsWvP58eNMTE4mubiYS4ODeaVzZ/oHBpodmhDiDJTZynhy+ZO8ufrNFltl32xaa0pKUqv1luXlrUfrkmr7WSwB5QlZGF5eofVYD8PTM8hlJjY3Kwk7DyOpGlH++mkArfXLtewfAmzRWtdZQUqSMPOtXGn0fu3ZA3/5izHtkDvkKMsyM3ly3z4S8/OJ8/PjlS5dGB4SIk9aCeHCFm1bxD2L76GVZ6sWX2XfGdjtpeTnb6SoaB9WawZlZZmUlWXUuG61ZmE8DFATDzw9Q/DyMhK0iuTsdOsWS/MXxK4rCXPkjJ+RwMEqr1OBunq57gV+cGA8TsNqhawsyMyEjIwTS9XXmZmQn288YRgWBqGhxteKperrwMDmmXcxPx+efhrefRdiYuCnn+DSSx3/fR1tQ14eT+3bx7KsLDq2asUn3btzW7t2eDTXf1Stja7FnBzIzTWWivXathUUQJcu0L+/sZxzDkjRVyFOcXPPm4ltF8vIhSMZ8ekIqbJvMg8PbwIDBxIYePqablrbsFqzT0rOKhK06uulpYcpKNiC1ZqBzZZf6zmV8q68DerlFUabNiOJiprQlB/xjDgyCavpN1iNKa1S6hKMJOzCWt5/AHgAIDo6uqniazStjd+JJydPtSVWFa+zs2s/p8VyIsHy84Ndu4zjTndMSEjNCVpdyZuvb/1LRyxfDvffD/v3w4QJMHUq+Lv4E+DJRUVMTE5mwfHjhHp68kaXLvw1MpJW9c1otYaSkvolTXVty8015nU6HT8/I+MODDT+8VasMOaCqngvPh769TuRmHXr1rJnRReiXLewbqy+dzUPLnmQ5395nj8O/iFV9l2AUpbyXqwwoGu9j7PbSygry6olacvAas2sXLfbS05/Qgcy/XakUioW+Bq4Umu963TndfTtyLyjBeydv5o02nDMFs6hknDScrxrTLQyM41erdoEBZ1ZUhQaWnuvls1m9J6dLtE7+b3yeaFr1KpV3bFUrC9ZAh9+aPxOnzULLqwxVTaRzWY8ollSYnytul7DtrSyMqb6+fHvoCA8tebRQ4d4cu9eggoKTnssJSWQl3cikSorO318Pj7GP2xQ0IkkqmK9pm01vR8QAJ4n/c1ks8HOnUZF3HXrjCUxEYqMKUnw94e+fasnZmefLYmZaLG01ny44UMe/uFh2vm1Y9Eti6TKvnA4s8aEeWIMzL8MOIQxMP82rfXWKvtEAz8Dd2mt/6jPeR2dhO1YsIHut/erti2bILIt4eS0akOBbxuKA8IpC2qDLawNHm3C8TyrDd6RbWgdHU5glzYER/kTGqZO+Z1phuLi6knj6XrpKtarJpceHvC3v8GUKUYHjENpDUePwr59kJxsfK1YT0urOUGqTy8SUODjw/SRI3llzBgKfHy454cfmDx3LpHp6UZ3oo+PkZn6+NS+3qqVkdzUN4kKDITmnKbIajUm66yamG3caLQTGPFUTcz69TNua0piJlqQqlX23xzxJn8d8FcZ+ykcxswSFVcB0zFKVMzWWk9VSo0H0Fp/oJT6CBgJ7C8/xFpboBUcnYQVHMtn52cbCLWnEVSShn9xOl7ZaUYCkJ5ufK1YSktrPkmrVkaV0jZtjOV066GhTjWeR2tj/FdFQhYcbPyebjL5+acmWBXrKSknenIqREZC585GEbK6EqRa1q2tWjHby4vJNhtHtOZ6X19eatuWnoGBJ/ZzhozZUaxW2LbtRGK2fr2RmJWUd8MHBRnJWNXErHNn95nmQIgaVK2yP6bXGGZeO1Oq7AuHkGKtjlCRqZycnJ2cqFV9nZtb87mUMhKxk5Oz8HBjsFdwsLGcvB4U5FTJWyWrFVJTa0+00tKq7x8QYPzSr1hiYk6sd+xoJEoNoLXmm/R0nt63j51FRZwfGMgrnTtzYbDMe0ZZGWzdWj0x27TpxB8WISGnJmadOkliBmC3G38oFBcbX6suNht4eRmLt/eJ9dq2SXuaqmqV/XPCzuHLW76UKvuiyUkS5ixKS08kZbUlblXXMzJOf6stIKDuRK2udX//hv0S0NoYoFZTgpWcbIzer3o/02KB6OjaE63Q0Cb/ZfRbdjb/2LePVbm5dG/dmpdjYrg+PFxuOdSltBS2bKmemCUlnRj3Fhp6IiGr+Bod3XT/dlqfutjt9Xtts9WeGJ28rbH71NYD3hAWS+0J25lur7pNKaNNKha73ZzXFovRy3xyrA3d1pDjvL2Na11AwInxlYGBxnvlqlbZ/+i6j7i1161N928sWjxJwlxVRW9bdraR9GRnn9l6Xl7d57dYTiRmdSVtcGqilZNT/Vzh4dUTq6qJVocOzXa7b1tBAU/v28e3GRmc5e3NlE6dGNe+PZ4y5qlhSkpg8+bqY8y2bDmRZAcFGb/kziRhqm1bc/PyMgY5+voava0V67Vtq88+FouRtFYspaXVX9e1/Uz2Pd05wBjnZ7GcWMx4bbMZPysnx3nytvrsU3VbU/DxqZaYlfi1Yl3eLvbbs4iOOpfzeg7HEhxyavJW8bVivaaHZoSoQpKwlspqNW6BViRnZ5rIVQzmBmPcVExMzYlWTIzp1VpTi4uZnJLCx0eP4m+x8GR0NI9EReHnjLdrXV1xsdFDtn69cUvTZjN+4Sp1YjnT1w05pupri+XMkyn52XBNFT2f9U3gSkuNP2Yrnmqu+Fp1vfyrzssl/Wgy9pxsgso88Cm11y8mX9+aE7SqX2NijDo/8nPX4phVrFWYzdPTuIUUGtqw44uLjWTMbof27Z3yCbrssjJeOXiQ6amp2LRmQlQUz0ZHE96cTyS2ND4+MHCgsQjR3JQyrm2enk3+uLYC2nCiyr4vXnw2YiaXhvWvVxJX7b2DB6tvKymBvXvh1VebNGbh2iQJE7Xz8TGSLydUYrfz3qFDTN2/n0yrldvbtuXFmBhiZIJtIUQjVa2yf/l/R/H8kOd5bshzjauy/9BD8Npr0Ls33HVX0wUrXJrcjhQuxa41848d47nkZPaXlDA8JIRpnTsTHxBgdmhCCDdTUFrAg0se5JOkTxjRZUTjquyXlcEVV8BvvxmzXQwe3LTBCqdV1+1I57u/JEQNtNb8LyODvuvWcdeOHYR5ebEsNpYf4+IkARNCOISftx9zb5jLjGtmkJCSQN8Zffkz9c+GnczLCxYuhKgouPFGo4yPaPEkCRNOL8dq5cYtW7hy82ZybTYW9OjB2n79uLyhY92EEKKelFI80O8B/rjnDyweFi76+CLeXfMuDbqLFBYG335rPChwww2nFqYWLY4kYcKp7SosZND69SzJzORfnTuzY+BAxrRrh4fU+xJCNKN+Ef1Y/8B6hncZzsM/PMxtX91Gfmn+mZ/o3HNhwQLYsAHuvdec8izCaUgSJpzWDxkZDFy/ngyrleVxcfw9OhpvJ3xCUwjRMoT6hvLtmG+ZeulUFm5dyMAPB7I9bfuZn+jaa2HqVPjsM5g2rekDFS5DfqMJp6O15l8HDnD15s3E+Pqyrl8/hshUQ0IIJ+ChPHjmomdYesdS0gvTGfDhAD7b/NmZn+ipp2DMGHj2WeMWpWiRJAkTTqXQZuP27dt5ct8+RrVpw2/x8XRs4NyRQgjhKJd1vozEvyTSp30fbvvqNh7+/mFKbWcwpZVSMGsW9O0Lt99uFD4WLY4kYcJpHCgu5qLERD4/fpyXYmL4vGdPqXgvhHBakYGRJNydwOODH+fdte9y8ccXcyDnQP1P4OsL33xjzG153XXGfMGiRZEkTDiFldnZDFi/nj1FRfy3d2+e7thRJtsWQjg9L4sXr494nUWjFrEtbRt9Z/Tlxz0/1v8EUVHw9ddw6BCMGtV0c2MKlyBJmDDdjMOHuXTTJoI9Pfmzb1+uDgszOyQhhDgjI3uOZN0D64gIiODK+Vcy+ZfJ2Oy2+h08eDDMnAkJCfDYY44NVDgVScKEaUrtdh7ctYvxu3YxLCSEP/v2pbufn9lhCSFEg3QL68bq+1ZzZ9ydTFkxhasWXEV6YXr9Dr7rLnjiCXjvPZgxw7GBCqchSZgwxfHSUi7btIkPDh/mqeho/tu7N8FeXmaHJYQQjdLaqzVzrp/DjGtm8EvKL2dWZX/aNGNqo4cegl9/dWygwilIEiaa3Ya8PPqvX8/6vDwW9OjBy507Y5HxX0IIN1FTlf13/nzn9FX2LRajdliXLjByJKSkNEu8wjyShIlm9dmxY1yYmAjAb/HxjGnXzuSIhBDCMapW2Z/wvwn1q7IfHGzUDbNa4frrjSmOhNuSJEw0C5vWPLV3L7dt307/gADW9etHX5l4Wwjh5iqq7L906Uss3LqQAR8OYFvatroP6tYN/vMf2LLFGCtmtzdPsKLZSRImHC67rIxrN2/mlYMHeTAiguVxcbT19jY7LCGEaBYeyoOnL3qaZXcuI7Mok4EfDjx9lf3hw+H1143yFVOmNE+gotlJEiYcakdBAYM2bGBZVhYfdOvGv7t1k/kfhRAt0qUxl7LhgQ2VVfYf+v4hSqwltR/wyCMwbhy88AJ88UXzBSqajfw2FA7zXXo6gzZsIMtq5ee4OP4SEWF2SEIIYaqqVfbfW/seF8+po8q+UvD++3D++XD33VA+nla4D0nCRJPTWvPS/v1ct2ULZ5dPwH2RTMAthBBA9Sr729O2111lv1Ur+OorCAszBuofO9a8wQqHkiRMNKkCm41bt23j2eRkxrRty8r4eKJlAm4hhDhFvavst2sHixdDejrcdBOU1HELU7gUScJEk9lfXMyFiYl8kZbGvzp35tMePWgtE3ALIUSt6l1lv29fmDMH/vgD/vpXOF3NMeESJAkTTWJFdjb9168nuaiIJb178/foaJmAWwgh6qGiyv7Ma2ayImVF7VX2b7kFJk6E2bPh7bebP1DR5CQJE42itebfhw5x+aZNhHt5saZfP66UCbiFEOKMKKW4v9/9/H7P73VX2Z8yBW64AR5/HJYuNSdY0WQkCRMNVmq385ddu/i/3bsZERLC6r596da6tdlhCSGEy+oX0Y8ND2xgxNkjaq6y7+EBn3wC554Lo0fD7t3mBSsaTZIw0SBHS0q4ZONGPjxyhGeio1ncuzdBnp5mhyWEEC4vxDeExbcurr3Kvr+/MVDfYoHrroOcHPOCFY0iSZg4Y+tyc+m/fj0b8/P5T8+eTJUJuIUQoklVVNlffufymqvsx8TAl1/Cnj1w221gq+GpSuH0JAkTZ+TTo0e5aONGPJXi9/h4bmnb1uyQhBDCbV0Sc0ntVfaHDIF334Xvv4ennzY3UNEgkoSJerFpzd/37uXOHTsYFBDA2n796CMTcAshhMPVWWX/L38xSla8+qoxVky4FEnCxGlllZVxVVISrx08yEORkSyLi6ONTMAthBDNpqLK/pe3fHlqlf3p02HoULj/fvizhtIWwmmpUx5/dXL9+/fX69atMzsMhyq121mVm8v6vDxsJv/7aODDI0fYX1zMv7t25T6Z/1EIIUy1O2M3IxeOZMvxLTx38XNMGjIJS2YWDBwIxcWwdi1ERpodpiinlFqvte5f43uShJlPa832wkKWZmayLCuLFdnZFNjtZodVKcLbmy/OPZfzg4LMDkUIIQRQWFbIX5f8lbmb5jK8y3Dm3zSf8H1H4bzzoHt3+PVX8PU1O0yBJGFO6XhpKcuzsioTr8OlpQB08/VlWEgIw0JDuTAoCB8P8+8Y+3h4yNOPQgjhZLTWfLThIx7+4WHa+rXli1FfMGjDMaOY65gx8OmnINdu00kS5gSKbDZ+y8lhaVYWyzIz2VRQAECopyeXh4RUJl4dZbJrIYQQZ2DDkQ2MXDiSQ7mHeH346zy0PBc1cSJMmwZPPml2eC2eJGEmsGtNUn4+y7KyWJqVxcrsbEq0xkspLgwKMpKukBDiAwKkl0kIIUSjZBVlcdc3d/Hdru+49dzRzPvChtcXX8K338I115gdXosmSVgzOVRSwrLMTJZmZbE8K4u0sjIAevn5VSZdFwcH42exmBypEEIId2PXdl757RUmJkwkNuBs/pjrhe++A7BqlTHNkTCFJGEOkm+1siInp3Jc1/bCQgDaeXkxLDSUYSEhXB4SQkSrViZHKoQQoqVISE7g1i9vJSgtj02zvPENCoM1ayAszOzQWiTTkjCl1BXAW4AF+EhrPe2k91X5+1cBhcBYrfWGus5pZhJm05r1eXnGLcbMTFbl5lKmNT4eHlwcFMTw8sSrt58fSm4xCiGEMMnhvMOMXjQa6++/8etcDywXXYzHj0vBy8vs0FqcupIwh824rJSyAO8Bw4BUYK1S6lutdZVZSLkS6Fq+DALeL//qNJKLiiqTrp+zs8myWgGI9/fnsagohoeGckFgID5yi1EIIYSTiAiI4Oe7fubpyKe5L+N15n7zC3kPPUDAjI/NDk1U4bAkDBgI7NFa7wNQSn0OXA9UTcKuB+ZpoztutVIqWCl1ltb6iAPjqlO+1cqyrKzKxGtvcTEAUa1acUN4OMNDQrgsJEQqxgshhHBqXhYvXhv+Gl91OJ930sfw8Mw5rFdZWONkfFiFkO7xdLvkZtO+vyOTsEjgYJXXqZzay1XTPpFAtSRMKfUA8ABAdHR0kwda1Y7CQm7auhV/i4WhwcFMiIpieEgI57RuLbcYhRBCuJybetzE7i83sfLSAVw0YzGw2OyQnMavV53rtklYTRnLyQPQ6rMPWuuZwEwwxoQ1PrTaxQcEsKJPHwYHBuLtBIVShRBCiMbq2rY7HTccZ8eKxWi7zexwnEaXyM6mfn9HJmGpQIcqr6OAww3Yp1lZlOLi4GAzQxBCCCGanLe3L92H3Wp2GKIKR3b1rAW6KqVilFLewK3Atyft8y1wlzIMBnLMHA8mhBBCCNFcHNYTprW2KqUeAn7EKFExW2u9VSk1vvz9D4DvMcpT7MEoUTHOUfEIIYQQQjgTR96ORGv9PUaiVXXbB1XWNfB/joxBCCGEEMIZychzIYQQQggTSBImhBBCCGECScKEEEIIIUwgSZgQQgghhAkkCRNCCCGEMIEkYUIIIYQQJpAkTAghhBDCBMoo1eU6lFJpwP4mOl04kN5E53IH0h7VSXucIG1RnbRHddIeJ0hbVCftAR211m1qesPlkrCmpJRap7Xub3YczkLaozppjxOkLaqT9qhO2uMEaYvqpD3qJrcjhRBCCCFMIEmYEEIIIYQJWnoSNtPsAJyMtEd10h4nSFtUJ+1RnbTHCdIW1Ul71KFFjwkTQgghhDBLS+8JE0IIIYQwhdsmYUqpK5RSO5VSe5RST9Xw/t+VUhvLly1KKZtSKlQp1UEplaCU2q6U2qqUesSM+JtSQ9uiyvsWpVSiUuq75o3cMRrTHkqpYKXUIqXUjvKfkfOa/xM0rUa2x2Pl/0+2KKU+U0r5NP8naDr1aIsgpdR/lVKbyj/3uPoe64oa2h7ueB2Fxv18lL/vNtfSRv5fcbvraINprd1uASzAXqAz4A1sAnrWsf+1wM/l62cBfcvXA4BddR3r7Etj2qLKtseBBcB3Zn8es9sDmAvcV77uDQSb/ZnMag8gEkgGfMtfLwTGmv2ZHNkWwDPAK+XrbYDM8n3PqB1dYWlke7jVdbSx7VHlfbe4lja2LdztOtqYxV17wgYCe7TW+7TWpcDnwPV17D8G+AxAa31Ea72hfD0P2I7xy8ZVNbgtAJRSUcDVwEcOjbL5NLg9lFKBwMXALACtdanWOtvB8Tpao34+AE/AVynlCbQGDjssUserT1toIEAppQB/jF8s1noe62oa3B5ueB2Fxv18uNu1tMFt4abX0QZz1yQsEjhY5XUqtVwAlFKtgSuAL2t4rxMQD/zZ5BE2n8a2xXTgH4DdUQE2s8a0R2cgDfi4/JbCR0opP0cG2wwa3B5a60PAa8AB4AiQo7Ve6tBoHas+bfEu0AMj2dwMPKK1ttfzWFfTmPao5CbXUWh8e7jTtbQxbeGO19EGc9ckTNWwrbbHQK8FftdaZ1Y7gVL+GL9sHtVa5zZxfM2pwW2hlLoGOK61Xu+o4EzQmJ8NT6Av8L7WOh4oAFx97E9jfj5CMP76jQEiAD+l1B0OibJ51KctRgAbMT5vH+Dd8r/sz6QdXUVj2sM4gftcR6ER7eGG19LG/Gy443W0wdw1CUsFOlR5HUXtt0lupfrtFZRSXhgXjvla668cEmHzaUxbXABcp5RKwehuvlQp9akjgmxGjWmPVCBVa13xF/0ijIuJK2tMe1wOJGut07TWZcBXwPkOibJ51KctxgFfacMejDFx3et5rKtpTHu423UUGtce7nYtbez/FXe7jjac2YPSHLFgZNr7MP5Crxg0eG4N+wVh3Kf2q7JNAfOA6WZ/DrPb4qT3h+Lig0mboj2AlcA55euTgVfN/kxmtQcwCNiKMRZMYQy2fdjsz+TItgDeByaXr7cDDmFMUFyvdnSlpZHt4VbX0ca2x0n7uPy1tLFt4W7X0cYsnrghrbVVKfUQ8CPGUxyztdZblVLjy9//oHzXG4GlWuuCKodfANwJbFZKbSzf9ozW+vtmCr9JNbIt3E4TtMfDwHyllDfGRWgcLqwx7aG1/lMptQjYgDH4OBEXro5dz7Z4EZijlNqMkWg8qbVOB6jpWDM+R1NpTHsopS7Eja6j0PifD3fSBG3hVtfRxpCK+UIIIYQQJnDXMWFCCCGEEE5NkjAhhBBCCBNIEiaEEEIIYQJJwoQQQgghTCBJmBBCCCGECSQJE0I4PaVUmFJqY/lyVCl1qHw9Wym1zQHfb7JS6okzPCa/lu1zlFI3N01kQgh3IkmYEMLpaa0ztNZ9tNZ9gA+AN8vX+1CPufjKJxgXQginIkmYEMLVWZRSHyqltiqlliqlfAGUUr8opV5SSq0AHlFK9VNKrVBKrVdK/aiUOqt8vwlKqW1KqSSl1OdVztuz/Bz7lFITKjYqpR5XSm0pXx49ORhleLf8nEuAtg7+/EIIFyV/HQohXF1XYIzW+n6l1EJgJFAxL1+w1npI+TyGK4DrtdZpSqnRwFTgHozJg2O01iVKqeAq5+0OXAIEADuVUu8DsRjVvQdhVAH/Uym1QmudWOW4G4FzgN4Y07VsA2Y75JMLIVyaJGFCCFeXrLWumBpnPdCpynv/Kf96DtALWKaUAmOqlSPl7yVhTKHyDfBNlWOXaK1LgBKl1HGMhOpC4OuK6ZuUUl8BF2FM2VThYuAzrbUNOKyU+rlJPqUQwu1IEiaEcHUlVdZtgG+V1xVzXSpgq9b6vBqOvxojcboOeE4pdW4t5/UsP099yHxwQojTkjFhQoiWYCfQRil1HoBSykspda5SygPooLVOAP4BBAP+dZznV+AGpVRrpZQfxq3HlTXsc6tSylI+7uySpv4wQgj3ID1hQgi3p7UuLS8T8bZSKgjj2jcd2AV8Wr5NYTx1mV1+y7Km82xQSs0B1pRv+uik8WAAXwOXApvLz7+iqT+PEMI9KK2l11wIIYQQornJ7UghhBBCCBNIEiaEEEIIYQJJwoQQQgghTCBJmBBCCCGECSQJE0IIIYQwgSRhQgghhBAmkCRMCCGEEMIEkoQJIYQQQpjg/wHBpYzPJNp3NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(data.angular_distance)\n",
    "min_value = min(data.angular_distance)\n",
    "\n",
    "threshold = np.arange(min_value,max_value,0.01)\n",
    "\n",
    "precision=[]\n",
    "accuracy=[]\n",
    "recall=[]\n",
    "specificity=[]\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    data['y_pred'] = ''\n",
    "    data['cm'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['angular_distance'][j] >= threshold[i]):\n",
    "            data['y_pred'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_pred'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'N'):\n",
    "            data['cm'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm.value_counts().TP if 'TP' in data.cm.value_counts() is not None else 0\n",
    "    tn = data.cm.value_counts().TN if 'TN' in data.cm.value_counts() is not None else 0\n",
    "    fp = data.cm.value_counts().FP if 'FP' in data.cm.value_counts() is not None else 0\n",
    "    fn = data.cm.value_counts().FN if 'FN' in data.cm.value_counts() is not None else 0\n",
    "\n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    precision.append(prec)\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    recall.append(rec)\n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    specificity.append(spec)\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    accuracy.append(acc)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(threshold, accuracy, color = 'b', label = 'Accuracy')\n",
    "plt.plot(threshold, recall, color = 'g', label = 'Recall')\n",
    "plt.plot(threshold, precision, color = 'r', label = 'Precision')\n",
    "plt.plot(threshold, specificity, color = 'c', label = 'Specificity')\n",
    "plt.plot(threshold, [1-x for x in accuracy], color = 'y', label = 'Total Error')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- ELMo: Angular Distance ----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.74</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.894737</td>\n",
       "      <td>0.178947</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.75</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>0.123457</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.183908</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.76</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.77</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.78</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>0.37037</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.163934</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.79</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold  TP  TN  FP  FN specificity    recall precision accuracy\n",
       "0       0.74  17   3  78   2    0.037037  0.894737  0.178947      0.2\n",
       "1       0.75  16  10  71   3    0.123457  0.842105  0.183908     0.26\n",
       "2       0.76  15  18  63   4    0.222222  0.789474  0.192308     0.33\n",
       "3       0.77  15  21  60   4    0.259259  0.789474       0.2     0.36\n",
       "4       0.78  10  30  51   9     0.37037  0.526316  0.163934      0.4\n",
       "5       0.79   9  36  45  10    0.444444  0.473684  0.166667     0.45"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.arange(0.74,0.79,0.01)\n",
    "results = pd.DataFrame(threshold,columns=['threshold'])\n",
    "results['TP'] = ''\n",
    "results['TN'] = ''\n",
    "results['FP'] = ''\n",
    "results['FN'] = ''\n",
    "\n",
    "results['specificity'] = ''\n",
    "results['recall']=''\n",
    "results['precision']=''\n",
    "results['accuracy']=''\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    threshold = results['threshold'][i]\n",
    "    data['y_angular'] = ''\n",
    "    data['cm_angular'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['angular_distance'][j] >= threshold):\n",
    "            data['y_angular'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_angular'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_angular'][k] == 'Y'):\n",
    "            data['cm_angular'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_angular'][k] == 'Y'):\n",
    "            data['cm_angular'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_angular'][k] == 'N'):\n",
    "            data['cm_angular'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm_angular'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm_angular.value_counts().TP if 'TP' in data.cm_angular.value_counts() is not None else 0\n",
    "    results['TP'][i] = tp\n",
    "    \n",
    "    tn = data.cm_angular.value_counts().TN if 'TN' in data.cm_angular.value_counts() is not None else 0\n",
    "    results['TN'][i] = tn\n",
    "    \n",
    "    fp = data.cm_angular.value_counts().FP if 'FP' in data.cm_angular.value_counts() is not None else 0\n",
    "    results['FP'][i] = fp\n",
    "    \n",
    "    fn = data.cm_angular.value_counts().FN if 'FN' in data.cm_angular.value_counts() is not None else 0\n",
    "    results['FN'][i] = fn\n",
    "    \n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    results['precision'][i] = prec\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    results['recall'][i] = rec\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    results['accuracy'][i] = acc \n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    results['specificity'][i] = spec\n",
    "\n",
    "print('---------- ELMo: Angular Distance ----------')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_list = results.TP.to_list()\n",
    "tn_list = results.TN.to_list()\n",
    "fp_list = results.FP.to_list()\n",
    "fn_list = results.FN.to_list()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "threshold = np.arange(0.74,0.79,0.01)\n",
    "\n",
    "plt.plot(threshold,tp_list, color = 'b', label = 'TP')\n",
    "plt.plot(threshold,tn_list, color = 'g', label = 'TN')\n",
    "plt.plot(threshold,fp_list, color = 'r', label = 'FP')\n",
    "plt.plot(threshold,fn_list, color = 'y', label = 'FN')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true positives: 15\n",
      "true negatives: 21\n",
      "false positives: 60\n",
      "false negatives: 4\n",
      "precision: 0.2\n",
      "recall: 0.7894736842105263\n",
      "accuracy: 0.36\n"
     ]
    }
   ],
   "source": [
    "data['y_angular']=''\n",
    "for i in range(0,len(data)):\n",
    "    if data['angular_distance'][i] >= 0.77:\n",
    "        data['y_angular'][i] = 'Y'\n",
    "    else:\n",
    "        data['y_angular'][i] = 'N'\n",
    "        \n",
    "data['cm_angular']=''\n",
    "for k in range(0,len(data)):\n",
    "    if (data['truth'][k] == 'Y') and (data['y_angular'][k] == 'Y'):\n",
    "        data['cm_angular'][k] = 'TP'\n",
    "    elif (data['truth'][k] == 'N') and (data['y_angular'][k] == 'Y'):\n",
    "        data['cm_angular'][k] = 'FP'\n",
    "    elif (data['truth'][k] == 'Y') and (data['y_angular'][k] == 'N'):\n",
    "        data['cm_angular'][k] = 'FN'\n",
    "    else:\n",
    "        data['cm_angular'][k] = 'TN'\n",
    "        \n",
    "tp = data.cm_angular.value_counts().TP if 'TP' in data.cm_angular.value_counts() is not None else 0\n",
    "print('true positives:',tp)\n",
    "tn = data.cm_angular.value_counts().TN if 'TN' in data.cm_angular.value_counts() is not None else 0\n",
    "print('true negatives:',tn)\n",
    "fp = data.cm_angular.value_counts().FP if 'FP' in data.cm_angular.value_counts() is not None else 0\n",
    "print('false positives:',fp)\n",
    "fn = data.cm_angular.value_counts().FN if 'FN' in data.cm_angular.value_counts() is not None else 0\n",
    "print('false negatives:',fn)\n",
    "\n",
    "precision = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "print('precision:',precision)\n",
    "    \n",
    "recall = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "print('recall:',recall)   \n",
    "\n",
    "acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "print('accuracy:',acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### manhattan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231.2047672501194\n",
      "103.49958937894553\n"
     ]
    }
   ],
   "source": [
    "print(max(data.manhattan_distance))\n",
    "print(min(data.manhattan_distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(data.manhattan_distance)\n",
    "min_value = min(data.manhattan_distance)\n",
    "threshold = np.arange(min_value,max_value,5)\n",
    "\n",
    "precision=[]\n",
    "accuracy=[]\n",
    "recall=[]\n",
    "specificity=[]\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    data['y_pred'] = ''\n",
    "    data['cm'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['manhattan_distance'][j] >= threshold[i]):\n",
    "            data['y_pred'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_pred'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'N'):\n",
    "            data['cm'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm.value_counts().TP if 'TP' in data.cm.value_counts() is not None else 0\n",
    "    tn = data.cm.value_counts().TN if 'TN' in data.cm.value_counts() is not None else 0\n",
    "    fp = data.cm.value_counts().FP if 'FP' in data.cm.value_counts() is not None else 0\n",
    "    fn = data.cm.value_counts().FN if 'FN' in data.cm.value_counts() is not None else 0\n",
    "\n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    precision.append(prec)\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    recall.append(rec)\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    accuracy.append(acc)\n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    specificity.append(spec)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(threshold, accuracy, color = 'b', label = 'Accuracy')\n",
    "plt.plot(threshold, recall, color = 'g', label = 'Recall')\n",
    "plt.plot(threshold, precision, color = 'r', label = 'Precision')\n",
    "plt.plot(threshold, specificity, color = 'c', label = 'Specificity')\n",
    "plt.plot(threshold, [1-x for x in accuracy], color = 'y', label = 'Total Error')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- ELMo: Manhattan Distance ----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>216</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.0526316</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold TP  TN FP  FN specificity     recall precision accuracy\n",
       "0        213  2  75  6  17    0.925926   0.105263      0.25     0.77\n",
       "1        214  2  75  6  17    0.925926   0.105263      0.25     0.77\n",
       "2        215  2  76  5  17    0.938272   0.105263  0.285714     0.78\n",
       "3        216  2  76  5  17    0.938272   0.105263  0.285714     0.78\n",
       "4        217  1  76  5  18    0.938272  0.0526316  0.166667     0.77"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.arange(213,218,1)\n",
    "results2 = pd.DataFrame(threshold,columns=['threshold'])\n",
    "results2['TP'] = ''\n",
    "results2['TN'] = ''\n",
    "results2['FP'] = ''\n",
    "results2['FN'] = ''\n",
    "\n",
    "results2['specificity'] = ''\n",
    "results2['recall']=''\n",
    "results2['precision']=''\n",
    "results2['accuracy']=''\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    threshold = results2['threshold'][i]\n",
    "    data['y_manhattan'] = ''\n",
    "    data['cm_manhattan'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['manhattan_distance'][j] >= threshold):\n",
    "            data['y_manhattan'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_manhattan'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_manhattan'][k] == 'Y'):\n",
    "            data['cm_manhattan'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_manhattan'][k] == 'Y'):\n",
    "            data['cm_manhattan'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_manhattan'][k] == 'N'):\n",
    "            data['cm_manhattan'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm_manhattan'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm_manhattan.value_counts().TP if 'TP' in data.cm_manhattan.value_counts() is not None else 0\n",
    "    results2['TP'][i] = tp\n",
    "    \n",
    "    tn = data.cm_manhattan.value_counts().TN if 'TN' in data.cm_manhattan.value_counts() is not None else 0\n",
    "    results2['TN'][i] = tn\n",
    "    \n",
    "    fp = data.cm_manhattan.value_counts().FP if 'FP' in data.cm_manhattan.value_counts() is not None else 0\n",
    "    results2['FP'][i] = fp\n",
    "    \n",
    "    fn = data.cm_manhattan.value_counts().FN if 'FN' in data.cm_manhattan.value_counts() is not None else 0\n",
    "    results2['FN'][i] = fn\n",
    "    \n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    results2['precision'][i] = prec\n",
    "\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    results2['recall'][i] = rec\n",
    "\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    results2['accuracy'][i] = acc \n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    results2['specificity'][i] = spec\n",
    "\n",
    "print('---------- ELMo: Manhattan Distance ----------')    \n",
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_list = results2.TP.to_list()\n",
    "tn_list = results2.TN.to_list()\n",
    "fp_list = results2.FP.to_list()\n",
    "fn_list = results2.FN.to_list()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "threshold = np.arange(213,218,1)\n",
    "\n",
    "plt.plot(threshold,tp_list, color = 'b', label = 'TP')\n",
    "plt.plot(threshold,tn_list, color = 'g', label = 'TN')\n",
    "plt.plot(threshold,fp_list, color = 'r', label = 'FP')\n",
    "plt.plot(threshold,fn_list, color = 'y', label = 'FN')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true positives: 2\n",
      "true negatives: 76\n",
      "false positives: 5\n",
      "false negatives: 17\n",
      "precision: 0.2857142857142857\n",
      "specificity: 0.9382716049382716\n",
      "recall: 0.10526315789473684\n",
      "accuracy: 0.78\n"
     ]
    }
   ],
   "source": [
    "data['y_manhattan']=''\n",
    "for i in range(0,len(data)):\n",
    "    if data['manhattan_distance'][i] >= 216:\n",
    "        data['y_manhattan'][i] = 'Y'\n",
    "    else:\n",
    "        data['y_manhattan'][i] = 'N'\n",
    "        \n",
    "data['cm_manhattan']=''\n",
    "for k in range(0,len(data)):\n",
    "    if (data['truth'][k] == 'Y') and (data['y_manhattan'][k] == 'Y'):\n",
    "        data['cm_manhattan'][k] = 'TP'\n",
    "    elif (data['truth'][k] == 'N') and (data['y_manhattan'][k] == 'Y'):\n",
    "        data['cm_manhattan'][k] = 'FP'\n",
    "    elif (data['truth'][k] == 'Y') and (data['y_manhattan'][k] == 'N'):\n",
    "        data['cm_manhattan'][k] = 'FN'\n",
    "    else:\n",
    "        data['cm_manhattan'][k] = 'TN'\n",
    "        \n",
    "tp = data.cm_manhattan.value_counts().TP if 'TP' in data.cm_manhattan.value_counts() is not None else 0\n",
    "print('true positives:',tp)\n",
    "tn = data.cm_manhattan.value_counts().TN if 'TN' in data.cm_manhattan.value_counts() is not None else 0\n",
    "print('true negatives:',tn)\n",
    "fp = data.cm_manhattan.value_counts().FP if 'FP' in data.cm_manhattan.value_counts() is not None else 0\n",
    "print('false positives:',fp)\n",
    "fn = data.cm_manhattan.value_counts().FN if 'FN' in data.cm_manhattan.value_counts() is not None else 0\n",
    "print('false negatives:',fn)\n",
    "\n",
    "precision = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "print('precision:',precision)\n",
    "\n",
    "specificity = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "print('specificity:',specificity)\n",
    "    \n",
    "recall = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "print('recall:',recall)   \n",
    "\n",
    "acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "print('accuracy:',acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### euclidean distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.661367\n",
      "4.109021\n"
     ]
    }
   ],
   "source": [
    "print(max(data.euclidean_distance))\n",
    "print(min(data.euclidean_distance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(data.euclidean_distance)\n",
    "min_value = min(data.euclidean_distance)\n",
    "threshold = np.arange(min_value,max_value,0.5)\n",
    "\n",
    "precision=[]\n",
    "accuracy=[]\n",
    "recall=[]\n",
    "specificity=[]\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    data['y_pred'] = ''\n",
    "    data['cm'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['euclidean_distance'][j] >= threshold[i]):\n",
    "            data['y_pred'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_pred'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'N'):\n",
    "            data['cm'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm.value_counts().TP if 'TP' in data.cm.value_counts() is not None else 0\n",
    "    tn = data.cm.value_counts().TN if 'TN' in data.cm.value_counts() is not None else 0\n",
    "    fp = data.cm.value_counts().FP if 'FP' in data.cm.value_counts() is not None else 0\n",
    "    fn = data.cm.value_counts().FN if 'FN' in data.cm.value_counts() is not None else 0\n",
    "\n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    precision.append(prec)\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    recall.append(rec)\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    accuracy.append(acc)\n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    specificity.append(spec)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(threshold, accuracy, color = 'b', label = 'Accuracy')\n",
    "plt.plot(threshold, recall, color = 'g', label = 'Recall')\n",
    "plt.plot(threshold, precision, color = 'r', label = 'Precision')\n",
    "plt.plot(threshold, specificity, color = 'c', label = 'Specificity')\n",
    "plt.plot(threshold, [1-x for x in accuracy], color = 'y', label = 'Total Error')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- ELMo: Euclidean Distance ----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.9</td>\n",
       "      <td>7</td>\n",
       "      <td>67</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0.82716</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0.864198</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.2</td>\n",
       "      <td>7</td>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.3</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold TP  TN  FP  FN specificity    recall precision accuracy\n",
       "0        7.9  7  67  14  12     0.82716  0.368421  0.333333     0.74\n",
       "1        8.0  7  69  12  12    0.851852  0.368421  0.368421     0.76\n",
       "2        8.1  7  70  11  12    0.864198  0.368421  0.388889     0.77\n",
       "3        8.2  7  72   9  12    0.888889  0.368421    0.4375     0.79\n",
       "4        8.3  5  72   9  14    0.888889  0.263158  0.357143     0.77"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.arange(7.9,8.4,0.1)\n",
    "results3 = pd.DataFrame(threshold,columns=['threshold'])\n",
    "results3['TP'] = ''\n",
    "results3['TN'] = ''\n",
    "results3['FP'] = ''\n",
    "results3['FN'] = ''\n",
    "\n",
    "results3['specificity'] = ''\n",
    "results3['recall']=''\n",
    "results3['precision']=''\n",
    "results3['accuracy']=''\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    threshold = results3['threshold'][i]\n",
    "    data['y_euclid'] = ''\n",
    "    data['cm_euclid'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['euclidean_distance'][j] >= threshold):\n",
    "            data['y_euclid'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_euclid'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_euclid'][k] == 'Y'):\n",
    "            data['cm_euclid'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_euclid'][k] == 'Y'):\n",
    "            data['cm_euclid'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_euclid'][k] == 'N'):\n",
    "            data['cm_euclid'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm_euclid'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm_euclid.value_counts().TP if 'TP' in data.cm_euclid.value_counts() is not None else 0\n",
    "    results3['TP'][i] = tp\n",
    "    \n",
    "    tn = data.cm_euclid.value_counts().TN if 'TN' in data.cm_euclid.value_counts() is not None else 0\n",
    "    results3['TN'][i] = tn\n",
    "    \n",
    "    fp = data.cm_euclid.value_counts().FP if 'FP' in data.cm_euclid.value_counts() is not None else 0\n",
    "    results3['FP'][i] = fp\n",
    "    \n",
    "    fn = data.cm_euclid.value_counts().FN if 'FN' in data.cm_euclid.value_counts() is not None else 0\n",
    "    results3['FN'][i] = fn\n",
    "    \n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    results3['precision'][i] = prec\n",
    "\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    results3['recall'][i] = rec\n",
    "\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    results3['accuracy'][i] = acc \n",
    "\n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    results3['specificity'][i] = spec\n",
    "\n",
    "print('---------- ELMo: Euclidean Distance ----------') \n",
    "results3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_list = results3.TP.to_list()\n",
    "tn_list = results3.TN.to_list()\n",
    "fp_list = results3.FP.to_list()\n",
    "fn_list = results3.FN.to_list()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "threshold = np.arange(7.9,8.4,0.1)\n",
    "\n",
    "plt.plot(threshold,tp_list, color = 'b', label = 'TP')\n",
    "plt.plot(threshold,tn_list, color = 'g', label = 'TN')\n",
    "plt.plot(threshold,fp_list, color = 'r', label = 'FP')\n",
    "plt.plot(threshold,fn_list, color = 'y', label = 'FN')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true positives: 7\n",
      "true negatives: 72\n",
      "false positives: 9\n",
      "false negatives: 12\n",
      "precision: 0.4375\n",
      "specificity: 0.8888888888888888\n",
      "recall: 0.3684210526315789\n",
      "accuracy: 0.79\n"
     ]
    }
   ],
   "source": [
    "data['y_euclid']=''\n",
    "for i in range(0,len(data)):\n",
    "    if data['euclidean_distance'][i] >= 8.2:\n",
    "        data['y_euclid'][i] = 'Y'\n",
    "    else:\n",
    "        data['y_euclid'][i] = 'N'\n",
    "        \n",
    "data['cm_euclid']=''\n",
    "for k in range(0,len(data)):\n",
    "    if (data['truth'][k] == 'Y') and (data['y_euclid'][k] == 'Y'):\n",
    "        data['cm_euclid'][k] = 'TP'\n",
    "    elif (data['truth'][k] == 'N') and (data['y_euclid'][k] == 'Y'):\n",
    "        data['cm_euclid'][k] = 'FP'\n",
    "    elif (data['truth'][k] == 'Y') and (data['y_euclid'][k] == 'N'):\n",
    "        data['cm_euclid'][k] = 'FN'\n",
    "    else:\n",
    "        data['cm_euclid'][k] = 'TN'\n",
    "        \n",
    "tp = data.cm_euclid.value_counts().TP if 'TP' in data.cm_euclid.value_counts() is not None else 0\n",
    "print('true positives:',tp)\n",
    "tn = data.cm_euclid.value_counts().TN if 'TN' in data.cm_euclid.value_counts() is not None else 0\n",
    "print('true negatives:',tn)\n",
    "fp = data.cm_euclid.value_counts().FP if 'FP' in data.cm_euclid.value_counts() is not None else 0\n",
    "print('false positives:',fp)\n",
    "fn = data.cm_euclid.value_counts().FN if 'FN' in data.cm_euclid.value_counts() is not None else 0\n",
    "print('false negatives:',fn)\n",
    "\n",
    "precision = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "print('precision:',precision)\n",
    "\n",
    "specificity = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "print('specificity:',specificity)\n",
    "    \n",
    "recall = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "print('recall:',recall)   \n",
    "\n",
    "acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "print('accuracy:',acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ts-ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(max(data.tss))\n",
    "print(min(data.tss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(data.tss)\n",
    "min_value = min(data.tss)\n",
    "threshold = np.arange(min_value,max_value,2)\n",
    "\n",
    "precision=[]\n",
    "accuracy=[]\n",
    "recall=[]\n",
    "specificity=[]\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    data['y_pred'] = ''\n",
    "    data['cm'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['tss'][j] >= threshold[i]):\n",
    "            data['y_pred'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_pred'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'N'):\n",
    "            data['cm'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm.value_counts().TP if 'TP' in data.cm.value_counts() is not None else 0\n",
    "    tn = data.cm.value_counts().TN if 'TN' in data.cm.value_counts() is not None else 0\n",
    "    fp = data.cm.value_counts().FP if 'FP' in data.cm.value_counts() is not None else 0\n",
    "    fn = data.cm.value_counts().FN if 'FN' in data.cm.value_counts() is not None else 0\n",
    "\n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    precision.append(prec)\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    recall.append(rec)\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    accuracy.append(acc)\n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    specificity.append(spec)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(threshold, accuracy, color = 'b', label = 'Accuracy')\n",
    "plt.plot(threshold, recall, color = 'g', label = 'Recall')\n",
    "plt.plot(threshold, precision, color = 'r', label = 'Precision')\n",
    "plt.plot(threshold, specificity, color = 'c', label = 'Specificity')\n",
    "plt.plot(threshold, [1-x for x in accuracy], color = 'y', label = 'Total Error')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- ELMo: TS-SS ----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>0.901235</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.0526316</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.0526316</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold TP  TN FP  FN specificity     recall precision accuracy\n",
       "0         21  2  73  8  17    0.901235   0.105263       0.2     0.75\n",
       "1         22  2  73  8  17    0.901235   0.105263       0.2     0.75\n",
       "2         23  2  75  6  17    0.925926   0.105263      0.25     0.77\n",
       "3         24  1  75  6  18    0.925926  0.0526316  0.142857     0.76\n",
       "4         25  1  76  5  18    0.938272  0.0526316  0.166667     0.77"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.arange(21,26,1)\n",
    "results4 = pd.DataFrame(threshold,columns=['threshold'])\n",
    "results4['TP'] = ''\n",
    "results4['TN'] = ''\n",
    "results4['FP'] = ''\n",
    "results4['FN'] = ''\n",
    "\n",
    "results4['specificity'] = ''\n",
    "results4['recall']=''\n",
    "results4['precision']=''\n",
    "results4['accuracy']=''\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    threshold = results4['threshold'][i]\n",
    "    data['y_tss'] = ''\n",
    "    data['cm_tss'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['tss'][j] >= threshold):\n",
    "            data['y_tss'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_tss'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_tss'][k] == 'Y'):\n",
    "            data['cm_tss'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_tss'][k] == 'Y'):\n",
    "            data['cm_tss'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_tss'][k] == 'N'):\n",
    "            data['cm_tss'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm_tss'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm_tss.value_counts().TP if 'TP' in data.cm_tss.value_counts() is not None else 0\n",
    "    results4['TP'][i] = tp\n",
    "    \n",
    "    tn = data.cm_tss.value_counts().TN if 'TN' in data.cm_tss.value_counts() is not None else 0\n",
    "    results4['TN'][i] = tn\n",
    "    \n",
    "    fp = data.cm_tss.value_counts().FP if 'FP' in data.cm_tss.value_counts() is not None else 0\n",
    "    results4['FP'][i] = fp\n",
    "    \n",
    "    fn = data.cm_tss.value_counts().FN if 'FN' in data.cm_tss.value_counts() is not None else 0\n",
    "    results4['FN'][i] = fn\n",
    "    \n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    results4['precision'][i] = prec\n",
    "\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    results4['recall'][i] = rec\n",
    "\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    results4['accuracy'][i] = acc \n",
    "\n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    results4['specificity'][i] = spec\n",
    "\n",
    "print('---------- ELMo: TS-SS ----------') \n",
    "results4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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APffcwwUXXMCcOXO4/vrrednLXta0Yzb6q8Y3Ah8GuoFVEdEH/J/MXNe0SiRJ0uxUZ2aqasPXeJWh0Wu8bgReAfwEIDMHqP2yUZIkSQ1qNHgdycyfnrCumc9vlCRJanuNXlz/QET8N6AjIlYD7wD+tbyyJEmSynXgwIGT1q1Zs4Y1a9aUdsxGZ7x+D3gZMEjtxqk/Bd5ZVlGSJEntqNEZr1/LzBuAG4ZXRMSVwN+WUpUkSVIbanTG6w8aXCdJkqQxjDvjFRGvB94AnBkRHx3xUQ9wpMzCJEmS2k29U41PANuBdcD9I9bvB64rqyhJkqR2NG7wyswdwI6I+ExmHq6oJkmSpLbU6DVeKyPi7yLioYj43vCr1MokSZJK1NHRQV9f37HXzp072bZtGxHBF77whWPfu+yyy9i2bVtTjtnorxr/CvjfwM3Aa6g9NDuaUoEkSVILjPasxp07d7JixQpuuukm3vjGNzb9mI3OeM3PzK1AZOYPMvNG4LVNr0aSJKnFzj//fBYvXszdd9/d9H03OuP1QkTMAR6NiLcDPwaWNb0aSZI06zz66EYOHBio/8UJWLiwj9Wrx3/49vPPP09fXx8Aq1atYvPmzcc+e9/73sf73vc+LrnkkqbW1Wjw2gicQu1RQX9IbbbrrU2tRJIkqUKjnWocdvHFFwNwzz33NPWYDQWvzLyvaB4Aro6ITuA3gXubWo0kSZp16s1MtcoNN9zATTfdRGdno/NU9Y17jVdE9ETEH0TEn0XE66Lm7cBjwFV1tp0XEf8eETsi4sGI+ECxfklE3B0RjxbvpzWtN5IkSU3yute9jmeeeYYdO3Y0bZ/1Lq7/a+Ac4DvAfwfuAq4ErsjMy+tsOwi8NjPPB/qASyPil4Hrga2ZuRrYWixLkiRNOzfccAO7du1q2v7qzZ29JDN/CSAiPg48DZydmfvr7Tgzk9qpSYCu4pXA5cCaYv0mYBvw3okWLkmSNBUHDhw4ad2aNWtYs2bNseV169ZRizTNUW/G69jd6jPzKPD9RkLXsIjoiIgBYA9wd2beCyzPzN3FPnfjryMlSdIsUW/G6/yIeLZoBzC/WA5qk1o9421chLW+iDgV2BwRv9hoYRGxAdgAcPbZZze6mSRJ0rQ17oxXZnZkZk/xWpSZnSPa44auE/bzE2qnFC8FnoqIMwCK9z1jbHNrZvZnZn9vb2/DHZIkSTNDM0/htcJk6m/0zvUTFhG9xUwXETEf+C/Ad4EtwPria+uBO8uqQZIkTU/z5s1j3759MzZ8ZSb79u1j3rx5E9queTemONkZwKaI6KAW8G7PzC9GxL8Bt0fENcAPqf1KUpIkzSIrVqxg165d7N27t9WlTNq8efNYsWLFhLYpLXhl5reBC0ZZvw9YW9ZxJUnS9NfV1cWqVataXUblSjvVKEmSpOMZvCRJkipi8JIkSaqIwUuSJKkiBi9JkqSKGLwkSZIqYvCSJEmqiMFLkiSpIgYvSZKkihi8JEmSKmLwkiRJqojBS5IkqSIGL0mSpIoYvCRJkipi8JIkSaqIwUuSJKkiBi9JkqSKGLwkSZIqYvCSJEmqiMFLkiSpIgYvSZKkihi8JEmSKmLwkiRJqojBS5IkqSKlBa+IOCsivhoRD0fEgxHxzmL9koi4OyIeLd5PK6sGSZKk6aTMGa8jwLsz81zgl4G3RcR5wPXA1sxcDWwtliVJktpeacErM3dn5jeL9n7gYeBM4HJgU/G1TcAVZdUgSZI0nVRyjVdErAQuAO4FlmfmbqiFM2BZFTVIkiS1WunBKyIWAp8HNmbmsxPYbkNEbI+I7Xv37i2vQEmSpIqUGrwioota6Pp0Zt5RrH4qIs4oPj8D2DPatpl5a2b2Z2Z/b29vmWVKkiRVosxfNQZwG/BwZv7JiI+2AOuL9nrgzrJqkCRJmk46S9z3q4HfAb4TEQPFuv8JfAi4PSKuAX4IXFliDZIkSdNGacErM78OxBgfry3ruJIkSdNVmTNekiSVLjPJPMzQ0CCZhxgaGjyhfYjMwePamdnqstUiPT3/iblzz2zZ8Q1ekqSGZA6NEmom327GPobbUqPOO+9zLFt2VcuOb/CSpGmkNntzpAgUtRma8toTCzhwtKl9jehmzpxuIuYyZ87cMdtdXQtHWd/NnDlzJ932UcWz17x5P9fS4xu8qP1fXOZQq8uQVKlawGn01FT57RfDEDTzNNicInB0F8Fl9HZHx0I6O5eM+53G2vWDVK3dTe3H79LsYvACdu36KI8/fl2ry5A0g0V01Zlp6R4xezPZUDOxdu3dv+al6cR/I4HFi1/FypV/2OoyJFUsonPKp6yGA46zN5IaYfACenpeSU/PK1tdhiRJanNeXShJklQRg5ckSVJFDF6SJEkVMXhJkiRVxOAlSZJUEYOXJElSRQxekiRJFTF4SZIkVcTgJUmSVBGDlyRJUkUMXpIkSRUxeEmSJFXE4CVJklQRg5ckSVJFDF6SJEkVMXhJkiRVxOAlSZJUkdKCV0R8IiL2RMQDI9YtiYi7I+LR4v20so4vSZI03ZQ54/VJ4NIT1l0PbM3M1cDWYlmSJGlWKC14ZeY/A//vhNWXA5uK9ibgirKOL0mSNN1UfY3X8szcDVC8L6v4+JIkSS0zbS+uj4gNEbE9Irbv3bu31eVIkiRNWdXB66mIOAOgeN8z1hcz89bM7M/M/t7e3soKlCRJKkvVwWsLsL5orwfurPj4kiRJLVPm7ST+Bvg34JyI2BUR1wAfAi6JiEeBS4plSZKkWaGzrB1n5lvG+GhtWceUJEmazkoLXjPKQw/B9u2trkKt0tkJixbVXj09x7fnz4eIVlcoSWoTBi+Au+6C665rdRWajubMOTmQjRbQRmufuLxwYW1/kqRZy+AFcPXVsG5dq6tQqxw+DPv3w7PP1t5PbI/22e7dx68/erSxYy1cOPkgd+I2nf7rK0kzjX9zAyxeXHtJk5EJL7wwfngbL8jt3Hn8+kOHGjvu/PkTn3Ubqz13bqn/iCRJNQYvaaoiaiFo/nxY1oSHMRw61Nis22jt3bvhkUdeXH7uucaO2dU1udOno7W9Lk6SxmTwkqab7m5YurT2mqojR+DAgckFuX374Pvff3H9gQO12b16OjqOD2qTvSaupwcWLPC6OEltxeAltbPOTjj11NprqoaG4ODBiZ9KHW4/8cTx6ydyXVwzronzujhJ04B/C0lqzPAvPBctmvq+hq+La+T06WihbngmbviziV4XNxzKvLZt9ursrI1/d3ftfax2vc8nug9ncGc9g5ek6o28Lm758qnvb3Dw+HDWaJBrNLCpvWTWTsO/8AL89Ke1Pz+HDtXeT2wfPtzcY3d0TD28NSMAntju6vLazIoYvCTNfMP/ATn99FZXonaT+WIQO/F9Ku16n//0p/W/MzTU3L5OhwA4WrvNZgkNXpIkjSXixQAw3Rw5Um0YHH49++z4+2j2TPJos4RTCX1vehOcd15za5wAg5ckSTNRZ2ftdcopra7keMOzhFWFwXqzhCPXDQ3VQpfBS5IktYWRs4TN+DFOMzX6a+oSGbwkSdLs0NHR6gporyvWJEmSpjGDlyRJUkUMXpIkSRUxeEmSJFXE4CVJklQRg5ckSVJFvJ0EsHUr3H57q6uQVLWOjvKeetLp366SRuFfDcDjj8OWLa2uQlLVRj5xZXCwufueM2d6Pvauu9tnIUutFJnZ6hrq6u/vz+3bt7e6DEltLLMWxKp87F2j+zhypLl97epqfgBsxv6cJVS7iIj7M7N/tM/8Yy5J1GaBurpqr+lmaKg1AfDgwfrPS26mkbOEZQfAaXADc7XIRRfBmWe27vgtCV4RcSnwp0AH8PHM/FAr6pCkmWDOHJg3r/aaTiYyS9jMGcCDB+GZZ8b/brNnCdU+Pvc5uOqq1h2/8uAVER3AnwOXALuA+yJiS2Y+VHUtkqTJm4mzhMPvQ0OtrlCtsmpVa4/fihmvVwCPZeb3ACLis8DlgMFLktQU03WWUGrFfbzOBH40YnlXsU6SJKmttSJ4jfZD5pN+WhkRGyJie0Rs37t3bwVlSZIklasVwWsXcNaI5RXAEyd+KTNvzcz+zOzv7e2trDhJkqSytCJ43QesjohVEdENvBnw9qWSJKntVX5xfWYeiYi3A1+hdjuJT2Tmg1XXIUmSVLWW3McrM/8R+MdWHFuSJKlVWnGqUZIkaVYyeEmSJFXE4CVJklQRg5ckSVJFIvOke5dOOxGxF/hByYc5HXi65GNMZ7O5/7O57zC7+2/fZ6/Z3P/Z3Heopv8/l5mj3oR0RgSvKkTE9szsb3UdrTKb+z+b+w6zu//2fXb2HWZ3/2dz36H1/fdUoyRJUkUMXpIkSRUxeL3o1lYX0GKzuf+zue8wu/tv32ev2dz/2dx3aHH/vcZLkiSpIs54SZIkVaTtg1dEnBURX42IhyPiwYh4Z7H+ymJ5KCLG/HVDRFwaEY9ExGMRcX11lU9dE/q+MyK+ExEDEbG9usqbY5z+/3FEfDcivh0RmyPi1DG2b8exb7Tv7Tr2f1j0fSAi7oqInx1j+3Yc+0b7PmPHfqy+j/j89yMiI+L0MbafseMOTel/2419RNwYET8u+jQQEW8YY/vqxj4z2/oFnAFcWLQXAf8BnAecC5wDbAP6x9i2A3gceAnQDewAzmt1n6roe7HNTuD0VvejhP6/Dugs1v8R8EezaOzr9r3Nx75nxHfeAXxsFo193b7P9LEfq+/F8lnAV6jdE/Kk/s30cZ9q/9t17IEbgd+vs22lY9/2M16ZuTszv1m09wMPA2dm5sOZ+UidzV8BPJaZ38vMQ8BngcvLrbh5ptj3GW+c/t+VmUeKr30DWDHK5u069o30fcYbp//PjvjaAmC0i1zbdewb6fuMNlbfi49vBt7D2P2e0eMOU+7/jFan7/VUOvZtH7xGioiVwAXAvQ1ucibwoxHLu2h8IKeVSfQdav+C3hUR90fEhjLqqso4/f9d4EujbDIbxn6svkMbj31E3BQRPwJ+C/hfo2zStmPfQN+hTcZ+ZN8jYh3w48zcMc4mbTPuMKn+QxuOfbHq7cVp9k9ExGmjbFLp2M+a4BURC4HPAxtP+D+/cTcbZd2M+7+FSfYd4NWZeSHweuBtEfGrpRRYsrH6HxE3AEeAT4+22Sjr2mbs6/Qd2njsM/OGzDyLWt/fPtpmo6xri7FvoO/QBmM/su/U/pzfwNhB89hmo6ybceMOk+4/tNnYF3/u/wL4eaAP2A18ZLTNRllX2tjPiuAVEV3UBuLTmXnHBDbdRe28+LAVwBPNrK1sU+g7mflE8b4H2ExtOnZGGav/EbEeuAz4rSxO8p+gbce+gb639diP8BngN0ZZ37ZjP8JYfZ/xYz9K338eWAXsiIid1MbzmxHxMydsOuPHHabU/3YcezLzqcw8mplDwF8yep8qHfu2D14REcBtwMOZ+ScT3Pw+YHVErIqIbuDNwJZm11iWqfQ9IhZExKLhNrWLsh9ofpXlGav/EXEp8F5gXWY+N8bmbTn2jfS9zcd+9YivrQO+O8rm7Tr2dfs+08d+tL5n5ncyc1lmrszMldT+I3thZj55wuYzetxhav1vx7Ev1p8x4mu/zuh9qnbsm3ml/nR8Ab9Cbcrw28BA8XpDMQC7gEHgKeArxfd/FvjHEdu/gdqvIx4Hbmh1f6rqO7Vfd+woXg/OtL7X6f9j1M7nD6/72Cwa+7p9b/Ox/zy1v3i/DXyB2kXns2Xs6/Z9po/9WH0/4Ts7KX65107jPtX+t+vYA38NfKdYvwU4o9Vj753rJUmSKtL2pxolSZKmC4OXJElSRQxekiRJFTF4SZIkVcTgJUmSVBGDl6RpKSKWRsRA8XoyIn5ctH8SEQ+VcLwbI+L3J7jNgTHWfzIi3tScyiS1E4OXpGkpM/dlZl9m9gEfA24u2n3AUL3tI6Kz7BolaaIMXpJmoo6I+MuIeDAi7oqI+QARsS0iPhgRXwPeGREXRcTXiof+fmX4LtYR8Y6IeKh4cO5nR+z3vGIf34uIdwyvjIh3RcQDxWvjicVEzZ8V+/wHYFnJ/Zc0Q/l/hJJmotXAWzLzf0TE7dSeO/ip4rNTM/M/F89t+xpweWbujYjfBG4Cfhe4HliVmYMRceqI/f4C8BpgEdMt8RAAAAFwSURBVPBIRPwF8HLgauCV1B6me29EfC0zvzViu18HzgF+CVgOPAR8opSeS5rRDF6SZqLvZ+ZA0b4fWDnis88V7+cAvwjcXXuMGx3A7uKzbwOfjoi/B/5+xLb/kJmDwGBE7KEWon4F2JyZBwEi4g7gYmBk8PpV4G8y8yjwRET8U1N6KantGLwkzUSDI9pHgfkjlg8W7wE8mJmvGmX7X6MWltYB74+Il42x385iP43w+WuS6vIaL0nt6hGgNyJeBRARXRHxsoiYA5yVmV8F3gOcCiwcZz//DFwREadExAJqpxXvGeU7b46IjuI6stc0uzOS2oMzXpLaUmYeKm7p8NGIWEzt77tbgP8APlWsC2q/lvxJcTpytP18MyI+Cfx7serjJ1zfBbAZeC3wnWL/X2t2fyS1h8h0dlySJKkKnmqUJEmqiMFLkiSpIgYvSZKkihi8JEmSKmLwkiRJqojBS5IkqSIGL0mSpIoYvCRJkiry/wGcjd3PM67yfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_list = results4.TP.to_list()\n",
    "tn_list = results4.TN.to_list()\n",
    "fp_list = results4.FP.to_list()\n",
    "fn_list = results4.FN.to_list()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "threshold = np.arange(21,26,1)\n",
    "\n",
    "plt.plot(threshold,tp_list, color = 'b', label = 'TP')\n",
    "plt.plot(threshold,tn_list, color = 'g', label = 'TN')\n",
    "plt.plot(threshold,fp_list, color = 'r', label = 'FP')\n",
    "plt.plot(threshold,fn_list, color = 'y', label = 'FN')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['y_tss']=''\n",
    "for i in range(0,len(data)):\n",
    "    if data['tss'][i] >= 23:\n",
    "        data['y_tss'][i] = 'Y'\n",
    "    else:\n",
    "        data['y_tss'][i] = 'N'\n",
    "        \n",
    "data['cm_tss']=''\n",
    "for k in range(0,len(data)):\n",
    "    if (data['truth'][k] == 'Y') and (data['y_tss'][k] == 'Y'):\n",
    "        data['cm_tss'][k] = 'TP'\n",
    "    elif (data['truth'][k] == 'N') and (data['y_tss'][k] == 'Y'):\n",
    "        data['cm_tss'][k] = 'FP'\n",
    "    elif (data['truth'][k] == 'Y') and (data['y_tss'][k] == 'N'):\n",
    "        data['cm_tss'][k] = 'FN'\n",
    "    else:\n",
    "        data['cm_tss'][k] = 'TN'\n",
    "        \n",
    "tp = data.cm_tss.value_counts().TP if 'TP' in data.cm_tss.value_counts() is not None else 0\n",
    "print('true positives:',tp)\n",
    "tn = data.cm_tss.value_counts().TN if 'TN' in data.cm_tss.value_counts() is not None else 0\n",
    "print('true negatives:',tn)\n",
    "fp = data.cm_tss.value_counts().FP if 'FP' in data.cm_tss.value_counts() is not None else 0\n",
    "print('false positives:',fp)\n",
    "fn = data.cm_tss.value_counts().FN if 'FN' in data.cm_tss.value_counts() is not None else 0\n",
    "print('false negatives:',fn)\n",
    "\n",
    "precision = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "print('precision:',precision)\n",
    "\n",
    "specificity = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "print('specificity:',specificity)\n",
    "    \n",
    "recall = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "print('recall:',recall)   \n",
    "\n",
    "acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "print('accuracy:',acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(max(data.cosine))\n",
    "print(min(data.cosine))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_value = max(data.cosine)\n",
    "min_value = min(data.cosine)\n",
    "threshold = np.arange(min_value,max_value,0.1)\n",
    "\n",
    "precision=[]\n",
    "accuracy=[]\n",
    "recall=[]\n",
    "specificity=[]\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    data['y_pred'] = ''\n",
    "    data['cm'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['cosine'][j] >= threshold[i]):\n",
    "            data['y_pred'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_pred'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_pred'][k] == 'Y'):\n",
    "            data['cm'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_pred'][k] == 'N'):\n",
    "            data['cm'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm.value_counts().TP if 'TP' in data.cm.value_counts() is not None else 0\n",
    "    tn = data.cm.value_counts().TN if 'TN' in data.cm.value_counts() is not None else 0\n",
    "    fp = data.cm.value_counts().FP if 'FP' in data.cm.value_counts() is not None else 0\n",
    "    fn = data.cm.value_counts().FN if 'FN' in data.cm.value_counts() is not None else 0\n",
    "\n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    precision.append(prec)\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    recall.append(rec)\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    accuracy.append(acc)\n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    specificity.append(spec)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.plot(threshold, accuracy, color = 'b', label = 'Accuracy')\n",
    "plt.plot(threshold, recall, color = 'g', label = 'Recall')\n",
    "plt.plot(threshold, precision, color = 'r', label = 'Precision')\n",
    "plt.plot(threshold, specificity, color = 'c', label = 'Specificity')\n",
    "plt.plot(threshold, [1-x for x in accuracy], color = 'y', label = 'Total Error')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- ELMo: Cosine Similiarity ----------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.73</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.74</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.76</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>0.308642</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.188406</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.77</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>0.37037</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.163934</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.78</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.175439</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold  TP  TN  FP FN specificity    recall precision accuracy\n",
       "0       0.73  15  18  63  4    0.222222  0.789474  0.192308     0.33\n",
       "1       0.74  15  18  63  4    0.222222  0.789474  0.192308     0.33\n",
       "2       0.75  15  21  60  4    0.259259  0.789474       0.2     0.36\n",
       "3       0.76  13  25  56  6    0.308642  0.684211  0.188406     0.38\n",
       "4       0.77  10  30  51  9     0.37037  0.526316  0.163934      0.4\n",
       "5       0.78  10  34  47  9    0.419753  0.526316  0.175439     0.44"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.arange(0.73,0.78,0.01)\n",
    "results5 = pd.DataFrame(threshold,columns=['threshold'])\n",
    "results5['TP'] = ''\n",
    "results5['TN'] = ''\n",
    "results5['FP'] = ''\n",
    "results5['FN'] = ''\n",
    "\n",
    "results5['specificity'] = ''\n",
    "results5['recall']=''\n",
    "results5['precision']=''\n",
    "results5['accuracy']=''\n",
    "\n",
    "for i in range(0,len(threshold)):\n",
    "    threshold = results5['threshold'][i]\n",
    "    data['y_cosine'] = ''\n",
    "    data['cm_cosine'] = ''\n",
    "    \n",
    "    for j in range(0,len(data)): # generate predicted values\n",
    "        if (data['cosine'][j] >= threshold):\n",
    "            data['y_cosine'][j] = 'Y'\n",
    "        else:\n",
    "            data['y_cosine'][j] = 'N'\n",
    "            \n",
    "    \n",
    "    for k in range(0,len(data)): # determine confusion matrix\n",
    "        if (data['truth'][k] == 'Y') and (data['y_cosine'][k] == 'Y'):\n",
    "            data['cm_cosine'][k] = 'TP'\n",
    "        elif (data['truth'][k] == 'N') and (data['y_cosine'][k] == 'Y'):\n",
    "            data['cm_cosine'][k] = 'FP'\n",
    "        elif (data['truth'][k] == 'Y') and (data['y_cosine'][k] == 'N'):\n",
    "            data['cm_cosine'][k] = 'FN'\n",
    "        else:\n",
    "            data['cm_cosine'][k] = 'TN'\n",
    "    \n",
    "    \n",
    "    tp = data.cm_cosine.value_counts().TP if 'TP' in data.cm_cosine.value_counts() is not None else 0\n",
    "    results5['TP'][i] = tp\n",
    "    \n",
    "    tn = data.cm_cosine.value_counts().TN if 'TN' in data.cm_cosine.value_counts() is not None else 0\n",
    "    results5['TN'][i] = tn\n",
    "    \n",
    "    fp = data.cm_cosine.value_counts().FP if 'FP' in data.cm_cosine.value_counts() is not None else 0\n",
    "    results5['FP'][i] = fp\n",
    "    \n",
    "    fn = data.cm_cosine.value_counts().FN if 'FN' in data.cm_cosine.value_counts() is not None else 0\n",
    "    results5['FN'][i] = fn\n",
    "    \n",
    "    prec = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "    results5['precision'][i] = prec\n",
    "\n",
    "    \n",
    "    rec = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "    results5['recall'][i] = rec\n",
    "\n",
    "    \n",
    "    acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "    results5['accuracy'][i] = acc \n",
    "    \n",
    "    spec = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "    results5['specificity'][i] = spec\n",
    "\n",
    "print('---------- ELMo: Cosine Similiarity ----------')    \n",
    "results5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp_list = results5.TP.to_list()\n",
    "tn_list = results5.TN.to_list()\n",
    "fp_list = results5.FP.to_list()\n",
    "fn_list = results5.FN.to_list()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "threshold = np.arange(0.73,0.78,0.01)\n",
    "\n",
    "plt.plot(threshold,tp_list, color = 'b', label = 'TP')\n",
    "plt.plot(threshold,tn_list, color = 'g', label = 'TN')\n",
    "plt.plot(threshold,fp_list, color = 'r', label = 'FP')\n",
    "plt.plot(threshold,fn_list, color = 'y', label = 'FN')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Rates/Error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true positives: 15\n",
      "true negatives: 21\n",
      "false positives: 60\n",
      "false negatives: 4\n",
      "precision: 0.2\n",
      "specificity: 0.25925925925925924\n",
      "recall: 0.7894736842105263\n",
      "accuracy: 0.36\n"
     ]
    }
   ],
   "source": [
    "data['y_cosine']=''\n",
    "for i in range(0,len(data)):\n",
    "    if data['cosine'][i] >= 0.75:\n",
    "        data['y_cosine'][i] = 'Y'\n",
    "    else:\n",
    "        data['y_cosine'][i] = 'N'\n",
    "        \n",
    "data['cm_cosine']=''\n",
    "for k in range(0,len(data)):\n",
    "    if (data['truth'][k] == 'Y') and (data['y_cosine'][k] == 'Y'):\n",
    "        data['cm_cosine'][k] = 'TP'\n",
    "    elif (data['truth'][k] == 'N') and (data['y_cosine'][k] == 'Y'):\n",
    "        data['cm_cosine'][k] = 'FP'\n",
    "    elif (data['truth'][k] == 'Y') and (data['y_cosine'][k] == 'N'):\n",
    "        data['cm_cosine'][k] = 'FN'\n",
    "    else:\n",
    "        data['cm_cosine'][k] = 'TN'\n",
    "\n",
    "tp = data.cm_cosine.value_counts().TP if 'TP' in data.cm_cosine.value_counts() is not None else 0\n",
    "print('true positives:',tp)\n",
    "tn = data.cm_cosine.value_counts().TN if 'TN' in data.cm_cosine.value_counts() is not None else 0\n",
    "print('true negatives:',tn)\n",
    "fp = data.cm_cosine.value_counts().FP if 'FP' in data.cm_cosine.value_counts() is not None else 0\n",
    "print('false positives:',fp)\n",
    "fn = data.cm_cosine.value_counts().FN if 'FN' in data.cm_cosine.value_counts() is not None else 0\n",
    "print('false negatives:',fn)\n",
    "\n",
    "precision = (tp/(tp+fp)) if (tp+fp) is not 0 else 0\n",
    "print('precision:',precision)\n",
    "\n",
    "specificity = (tn / (tn + fp)) if (tn + fp) is not 0 else 0\n",
    "print('specificity:',specificity)\n",
    "    \n",
    "recall = (tp/(tp+fn)) if (tp+fn) is not 0 else 0\n",
    "print('recall:',recall)   \n",
    "\n",
    "acc = ((tp+tn)/(tp+tn+fp+fn)) if (tp+tn+fp+fn) is not 0 else 0\n",
    "print('accuracy:',acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['y_pred', 'cm'], axis=1, inplace=True)\n",
    "data.to_excel('elmo.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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