fuzzy_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ast import literal_eval\n",
    "import numpy as np\n",
    "import math\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from difflib import SequenceMatcher\n",
    "from collections import Counter\n",
    "from itertools import groupby\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import precision_recall_fscore_support"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import bo attr lookup data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>parent_org_name</th>\n",
       "      <th>children_org_name_list</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>children_org_name</th>\n",
       "      <th>parent_trigram</th>\n",
       "      <th>child_trigram</th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>jaro_func_score_trigram_parent</th>\n",
       "      <th>jaro_func_score_trigram_child</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"NEXARK INC\"</td>\n",
       "      <td>[NEWKIRK PRODUCTS]</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NEWKIRK PRODUCTS</td>\n",
       "      <td>[\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...</td>\n",
       "      <td>[NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+DESCONOCIDO</td>\n",
       "      <td>[DEGENKOLB]</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>LOW</td>\n",
       "      <td>DEGENKOLB</td>\n",
       "      <td>[+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...</td>\n",
       "      <td>[DEG, EGE, GEN, ENK, NKO, KOL, OLB]</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+EMBOL LTDA</td>\n",
       "      <td>[EMPLOYBRIDGE]</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EMPLOYBRIDGE</td>\n",
       "      <td>[+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]</td>\n",
       "      <td>[EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>[0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>[0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+MARTINS</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>SAN MARINA</td>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS]</td>\n",
       "      <td>[SAN, AN , N M,  MA, MAR, ARI, RIN, INA]</td>\n",
       "      <td>0.78</td>\n",
       "      <td>[0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]</td>\n",
       "      <td>[0.84, 0.62, 0.62, 0.48, 0.48, 0.48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+RED COMUNICACAO &amp; MARKETING LTDA</td>\n",
       "      <td>[RED ENVELOPE ENTERTAINMENT]</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>RED ENVELOPE ENTERTAINMENT</td>\n",
       "      <td>[+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...</td>\n",
       "      <td>[RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>[0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...</td>\n",
       "      <td>[0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     parent_org_name        children_org_name_list  \\\n",
       "0                       \"NEXARK INC\"            [NEWKIRK PRODUCTS]   \n",
       "1                       +DESCONOCIDO                   [DEGENKOLB]   \n",
       "2                        +EMBOL LTDA                [EMPLOYBRIDGE]   \n",
       "3                           +MARTINS                  [SAN MARINA]   \n",
       "4  +RED COMUNICACAO & MARKETING LTDA  [RED ENVELOPE ENTERTAINMENT]   \n",
       "\n",
       "   match_confidence_score match_confidence           children_org_name  \\\n",
       "0                0.533333              LOW            NEWKIRK PRODUCTS   \n",
       "1                0.666667              LOW                   DEGENKOLB   \n",
       "2                0.600000              LOW                EMPLOYBRIDGE   \n",
       "3                0.750000           MEDIUM                  SAN MARINA   \n",
       "4                0.625000              LOW  RED ENVELOPE ENTERTAINMENT   \n",
       "\n",
       "                                      parent_trigram  \\\n",
       "0  [\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...   \n",
       "1  [+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...   \n",
       "2      [+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]   \n",
       "3                     [+MA, MAR, ART, RTI, TIN, INS]   \n",
       "4  [+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...   \n",
       "\n",
       "                                       child_trigram  jaro_func_score  \\\n",
       "0  [NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...             0.58   \n",
       "1                [DEG, EGE, GEN, ENK, NKO, KOL, OLB]             0.51   \n",
       "2  [EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...             0.63   \n",
       "3           [SAN, AN , N M,  MA, MAR, ARI, RIN, INA]             0.78   \n",
       "4  [RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...             0.57   \n",
       "\n",
       "                      jaro_func_score_trigram_parent  \\\n",
       "0  [0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1             [0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]   \n",
       "2  [0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....   \n",
       "3     [0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]   \n",
       "4  [0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...   \n",
       "\n",
       "                       jaro_func_score_trigram_child  \n",
       "0  [0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...  \n",
       "1  [0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....  \n",
       "2  [0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...  \n",
       "3               [0.84, 0.62, 0.62, 0.48, 0.48, 0.48]  \n",
       "4  [0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read in data\n",
    "# df = pd.read_csv('bo_attr_lookup_data_new.csv', encoding='latin1')\n",
    "df = pd.read_csv('bo_attr_lookup_new.csv', encoding='latin1')\n",
    "\n",
    "#create spare child col\n",
    "df['children_org_name'] = df['children_org_name_list'].agg(list)\n",
    "\n",
    "#conver child col to list\n",
    "df['children_org_name_list'] = df['children_org_name_list'].agg(list)\n",
    "\n",
    "#fill missing values\n",
    "df = df.fillna('missingmissingmissingmissingmissing')\n",
    "\n",
    "#trigram function\n",
    "def split_str(s, N):\n",
    "    rmax = np.clip(len(s)-N, a_min=0, a_max=None)\n",
    "    return [s[0+i:N+i] for i in range(0, rmax+1)]\n",
    "\n",
    "#trigrams\n",
    "df['parent_trigram'] = df['parent_org_name'].apply(lambda x: split_str(x, 3))\n",
    "df['child_trigram'] = df['children_org_name_list'].apply(lambda x: split_str(x, 3))\n",
    "\n",
    "#convert to list\n",
    "df['children_org_name_list'] = df.children_org_name_list.map(lambda x: [x])\n",
    "\n",
    "#jaro score\n",
    "df['jaro_func_score'] = df[['parent_org_name', 'children_org_name_list']].agg(lambda x: get_top_matches(*x), axis=1)\n",
    "df['jaro_func_score'] = df.jaro_func_score.apply(lambda x: [val[1] for val in x])\n",
    "df['jaro_func_score'] = df.jaro_func_score.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "\n",
    "#parent to child\n",
    "df['jaro_func_score_trigram_parent'] = df[['parent_org_name', 'child_trigram']].agg(lambda x: get_top_matches(*x), axis=1)\n",
    "df['jaro_func_score_trigram_parent'] = df.jaro_func_score_trigram_parent.apply(lambda x: [val[1] for val in x])\n",
    "\n",
    "#child to parent\n",
    "df['jaro_func_score_trigram_child'] = df[['children_org_name', 'parent_trigram']].agg(lambda x: get_top_matches(*x), axis=1)\n",
    "df['jaro_func_score_trigram_child'] = df.jaro_func_score_trigram_child.apply(lambda x: [val[1] for val in x])\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Jaro Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jaro version\n",
    "def sort_token_alphabetically(word):\n",
    "    token = re.split('[,. ]', word)\n",
    "    sorted_token = sorted(token)\n",
    "    return ' '.join(sorted_token)\n",
    "\n",
    "def get_jaro_distance(first, second, winkler=True, winkler_ajustment=True,\n",
    "                      scaling=0.1, sort_tokens=True):\n",
    "    \"\"\"\n",
    "    :param first: word to calculate distance for\n",
    "    :param second: word to calculate distance with\n",
    "    :param winkler: same as winkler_ajustment\n",
    "    :param winkler_ajustment: add an adjustment factor to the Jaro of the distance\n",
    "    :param scaling: scaling factor for the Winkler adjustment\n",
    "    :return: Jaro distance adjusted (or not)\n",
    "    \"\"\"\n",
    "    if sort_tokens:\n",
    "        first = sort_token_alphabetically(first)\n",
    "        second = sort_token_alphabetically(second)\n",
    "\n",
    "    if not first or not second:\n",
    "        raise JaroDistanceException(\n",
    "            \"Cannot calculate distance from NoneType ({0}, {1})\".format(\n",
    "                first.__class__.__name__,\n",
    "                second.__class__.__name__))\n",
    "\n",
    "    jaro = _score(first, second)\n",
    "    cl = min(len(_get_prefix(first, second)), 4)\n",
    "\n",
    "    if all([winkler, winkler_ajustment]):  # 0.1 as scaling factor\n",
    "        return round((jaro + (scaling * cl * (1.0 - jaro))) * 100.0) / 100.0\n",
    "\n",
    "    return jaro\n",
    "\n",
    "def _score(first, second):\n",
    "    shorter, longer = first.lower(), second.lower()\n",
    "\n",
    "    if len(first) > len(second):\n",
    "        longer, shorter = shorter, longer\n",
    "\n",
    "    m1 = _get_matching_characters(shorter, longer)\n",
    "    m2 = _get_matching_characters(longer, shorter)\n",
    "\n",
    "    if len(m1) == 0 or len(m2) == 0:\n",
    "        return 0.0\n",
    "\n",
    "    return (float(len(m1)) / len(shorter) +\n",
    "            float(len(m2)) / len(longer) +\n",
    "            float(len(m1) - _transpositions(m1, m2)) / len(m1)) / 3.0\n",
    "\n",
    "def _get_diff_index(first, second):\n",
    "    if first == second:\n",
    "        pass\n",
    "\n",
    "    if not first or not second:\n",
    "        return 0\n",
    "\n",
    "    max_len = min(len(first), len(second))\n",
    "    for i in range(0, max_len):\n",
    "        if not first[i] == second[i]:\n",
    "            return i\n",
    "\n",
    "    return max_len\n",
    "\n",
    "def _get_prefix(first, second):\n",
    "    if not first or not second:\n",
    "        return \"\"\n",
    "\n",
    "    index = _get_diff_index(first, second)\n",
    "    if index == -1:\n",
    "        return first\n",
    "\n",
    "    elif index == 0:\n",
    "        return \"\"\n",
    "\n",
    "    else:\n",
    "        return first[0:index]\n",
    "\n",
    "def _get_matching_characters(first, second):\n",
    "    common = []\n",
    "    limit = math.floor(min(len(first), len(second)) / 2)\n",
    "\n",
    "    for i, l in enumerate(first):\n",
    "        left, right = int(max(0, i - limit)), int(\n",
    "            min(i + limit + 1, len(second)))\n",
    "        if l in second[left:right]:\n",
    "            common.append(l)\n",
    "            second = second[0:second.index(l)] + '*' + second[\n",
    "                                                       second.index(l) + 1:]\n",
    "\n",
    "    return ''.join(common)\n",
    "\n",
    "def _transpositions(first, second):\n",
    "    return math.floor(\n",
    "        len([(f, s) for f, s in zip(first, second) if not f == s]) / 2.0)\n",
    "\n",
    "def get_top_matches(reference, value_list, max_results=None):\n",
    "    scores = []\n",
    "    if not max_results:\n",
    "        max_results = len(value_list)\n",
    "    for val in value_list:\n",
    "        score_sorted = get_jaro_distance(reference, val)\n",
    "        score_unsorted = get_jaro_distance(reference, val, sort_tokens=False)\n",
    "        scores.append((val, max(score_sorted, score_unsorted)))\n",
    "    scores.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    return scores[:max_results]\n",
    "\n",
    "class JaroDistanceException(Exception):\n",
    "    def __init__(self, message):\n",
    "        super(Exception, self).__init__(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply Jaro and Sequencer function scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parent_org_name</th>\n",
       "      <th>children_org_name_list</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>children_org_name</th>\n",
       "      <th>parent_trigram</th>\n",
       "      <th>child_trigram</th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>jaro_func_score_trigram_parent</th>\n",
       "      <th>jaro_func_score_trigram_child</th>\n",
       "      <th>total_chars_positions</th>\n",
       "      <th>consecutive_chars</th>\n",
       "      <th>sequence_matcher_ratio</th>\n",
       "      <th>count_same_position</th>\n",
       "      <th>count_total_unique_chars</th>\n",
       "      <th>count_common_chars</th>\n",
       "      <th>percent_matched</th>\n",
       "      <th>percent_same_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"NEXARK INC\"</td>\n",
       "      <td>[NEWKIRK PRODUCTS]</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NEWKIRK PRODUCTS</td>\n",
       "      <td>[\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...</td>\n",
       "      <td>[NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+DESCONOCIDO</td>\n",
       "      <td>[DEGENKOLB]</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>LOW</td>\n",
       "      <td>DEGENKOLB</td>\n",
       "      <td>[+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...</td>\n",
       "      <td>[DEG, EGE, GEN, ENK, NKO, KOL, OLB]</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+EMBOL LTDA</td>\n",
       "      <td>[EMPLOYBRIDGE]</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EMPLOYBRIDGE</td>\n",
       "      <td>[+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]</td>\n",
       "      <td>[EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>[0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>[0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+MARTINS</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>SAN MARINA</td>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS]</td>\n",
       "      <td>[SAN, AN , N M,  MA, MAR, ARI, RIN, INA]</td>\n",
       "      <td>0.78</td>\n",
       "      <td>[0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]</td>\n",
       "      <td>[0.84, 0.62, 0.62, 0.48, 0.48, 0.48]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+RED COMUNICACAO &amp; MARKETING LTDA</td>\n",
       "      <td>[RED ENVELOPE ENTERTAINMENT]</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>RED ENVELOPE ENTERTAINMENT</td>\n",
       "      <td>[+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...</td>\n",
       "      <td>[RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>[0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...</td>\n",
       "      <td>[0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>+TEGAPE IMPORTACAO E COMERCIO DE TECIDOS TECNI...</td>\n",
       "      <td>[ARTHUR LUNDGREN TECIDOS]</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>LOW</td>\n",
       "      <td>ARTHUR LUNDGREN TECIDOS</td>\n",
       "      <td>[+TE, TEG, EGA, GAP, APE, PE , E I,  IM, IMP, ...</td>\n",
       "      <td>[ART, RTH, THU, HUR, UR , R L,  LU, LUN, UND, ...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>[0.57, 0.57, 0.45, 0.45, 0.45, 0.45, 0.45, 0.4...</td>\n",
       "      <td>[0.58, 0.58, 0.51, 0.51, 0.51, 0.46, 0.46, 0.4...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>+UNDEFINEDUNDEFINEDUNDEFINED</td>\n",
       "      <td>[UNITYPOINT HEALTH]</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>LOW</td>\n",
       "      <td>UNITYPOINT HEALTH</td>\n",
       "      <td>[+UN, UND, NDE, DEF, EFI, FIN, INE, NED, EDU, ...</td>\n",
       "      <td>[UNI, NIT, ITY, TYP, YPO, POI, OIN, INT, NT , ...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>[0.58, 0.46, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
       "      <td>[0.68, 0.68, 0.68, 0.59, 0.59, 0.59, 0.46, 0.4...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101 DO BRASIL INDUSTRIAL LTDA.</td>\n",
       "      <td>[FORCE 10 NETWORKS]</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>LOW</td>\n",
       "      <td>FORCE 10 NETWORKS</td>\n",
       "      <td>[101, 01 , 1 D,  DO, DO , O B,  BR, BRA, RAS, ...</td>\n",
       "      <td>[FOR, ORC, RCE, CE , E 1,  10, 10 , 0 N,  NE, ...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.79, 0.79, 0.51, 0.51, 0.46, 0.46, 0.0, 0.0,...</td>\n",
       "      <td>[0.68, 0.64, 0.46, 0.46, 0.46, 0.46, 0.46, 0.4...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>111 SOUTH</td>\n",
       "      <td>[1&amp;1 IONOS]</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>1&amp;1 IONOS</td>\n",
       "      <td>[111, 11 , 1 S,  SO, SOU, OUT, UTH]</td>\n",
       "      <td>[1&amp;1, &amp;1 , 1 I,  IO, ION, ONO, NOS]</td>\n",
       "      <td>0.67</td>\n",
       "      <td>[0.67, 0.63, 0.53, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.8, 0.67, 0.53, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2 DESIGN E COMUNICACAO</td>\n",
       "      <td>[FRANCE 2]</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>FRANCE 2</td>\n",
       "      <td>[2 D,  DE, DES, ESI, SIG, IGN, GN , N E,  E , ...</td>\n",
       "      <td>[FRA, RAN, ANC, NCE, CE , E 2]</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.67, 0.59, 0.46, 0.46, 0.0, 0.0]</td>\n",
       "      <td>[0.71, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.4...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     parent_org_name  \\\n",
       "0                                       \"NEXARK INC\"   \n",
       "1                                       +DESCONOCIDO   \n",
       "2                                        +EMBOL LTDA   \n",
       "3                                           +MARTINS   \n",
       "4                  +RED COMUNICACAO & MARKETING LTDA   \n",
       "5  +TEGAPE IMPORTACAO E COMERCIO DE TECIDOS TECNI...   \n",
       "6                       +UNDEFINEDUNDEFINEDUNDEFINED   \n",
       "7                     101 DO BRASIL INDUSTRIAL LTDA.   \n",
       "8                                          111 SOUTH   \n",
       "9                             2 DESIGN E COMUNICACAO   \n",
       "\n",
       "         children_org_name_list  match_confidence_score match_confidence  \\\n",
       "0            [NEWKIRK PRODUCTS]                0.533333              LOW   \n",
       "1                   [DEGENKOLB]                0.666667              LOW   \n",
       "2                [EMPLOYBRIDGE]                0.600000              LOW   \n",
       "3                  [SAN MARINA]                0.750000           MEDIUM   \n",
       "4  [RED ENVELOPE ENTERTAINMENT]                0.625000              LOW   \n",
       "5     [ARTHUR LUNDGREN TECIDOS]                0.512821              LOW   \n",
       "6           [UNITYPOINT HEALTH]                0.444444              LOW   \n",
       "7           [FORCE 10 NETWORKS]                0.433333              LOW   \n",
       "8                   [1&1 IONOS]                0.769231           MEDIUM   \n",
       "9                    [FRANCE 2]                0.375000              LOW   \n",
       "\n",
       "            children_org_name  \\\n",
       "0            NEWKIRK PRODUCTS   \n",
       "1                   DEGENKOLB   \n",
       "2                EMPLOYBRIDGE   \n",
       "3                  SAN MARINA   \n",
       "4  RED ENVELOPE ENTERTAINMENT   \n",
       "5     ARTHUR LUNDGREN TECIDOS   \n",
       "6           UNITYPOINT HEALTH   \n",
       "7           FORCE 10 NETWORKS   \n",
       "8                   1&1 IONOS   \n",
       "9                    FRANCE 2   \n",
       "\n",
       "                                      parent_trigram  \\\n",
       "0  [\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...   \n",
       "1  [+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...   \n",
       "2      [+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]   \n",
       "3                     [+MA, MAR, ART, RTI, TIN, INS]   \n",
       "4  [+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...   \n",
       "5  [+TE, TEG, EGA, GAP, APE, PE , E I,  IM, IMP, ...   \n",
       "6  [+UN, UND, NDE, DEF, EFI, FIN, INE, NED, EDU, ...   \n",
       "7  [101, 01 , 1 D,  DO, DO , O B,  BR, BRA, RAS, ...   \n",
       "8                [111, 11 , 1 S,  SO, SOU, OUT, UTH]   \n",
       "9  [2 D,  DE, DES, ESI, SIG, IGN, GN , N E,  E , ...   \n",
       "\n",
       "                                       child_trigram  jaro_func_score  \\\n",
       "0  [NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...             0.58   \n",
       "1                [DEG, EGE, GEN, ENK, NKO, KOL, OLB]             0.51   \n",
       "2  [EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...             0.63   \n",
       "3           [SAN, AN , N M,  MA, MAR, ARI, RIN, INA]             0.78   \n",
       "4  [RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...             0.57   \n",
       "5  [ART, RTH, THU, HUR, UR , R L,  LU, LUN, UND, ...             0.45   \n",
       "6  [UNI, NIT, ITY, TYP, YPO, POI, OIN, INT, NT , ...             0.47   \n",
       "7  [FOR, ORC, RCE, CE , E 1,  10, 10 , 0 N,  NE, ...             0.51   \n",
       "8                [1&1, &1 , 1 I,  IO, ION, ONO, NOS]             0.67   \n",
       "9                     [FRA, RAN, ANC, NCE, CE , E 2]             0.58   \n",
       "\n",
       "                      jaro_func_score_trigram_parent  \\\n",
       "0  [0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1             [0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]   \n",
       "2  [0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....   \n",
       "3     [0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]   \n",
       "4  [0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...   \n",
       "5  [0.57, 0.57, 0.45, 0.45, 0.45, 0.45, 0.45, 0.4...   \n",
       "6  [0.58, 0.46, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0...   \n",
       "7  [0.79, 0.79, 0.51, 0.51, 0.46, 0.46, 0.0, 0.0,...   \n",
       "8             [0.67, 0.63, 0.53, 0.0, 0.0, 0.0, 0.0]   \n",
       "9                 [0.67, 0.59, 0.46, 0.46, 0.0, 0.0]   \n",
       "\n",
       "                       jaro_func_score_trigram_child  total_chars_positions  \\\n",
       "0  [0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...                   16.0   \n",
       "1  [0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....                    9.0   \n",
       "2  [0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...                   12.0   \n",
       "3               [0.84, 0.62, 0.62, 0.48, 0.48, 0.48]                   10.0   \n",
       "4  [0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...                   26.0   \n",
       "5  [0.58, 0.58, 0.51, 0.51, 0.51, 0.46, 0.46, 0.4...                   23.0   \n",
       "6  [0.68, 0.68, 0.68, 0.59, 0.59, 0.59, 0.46, 0.4...                   17.0   \n",
       "7  [0.68, 0.64, 0.46, 0.46, 0.46, 0.46, 0.46, 0.4...                   17.0   \n",
       "8              [0.8, 0.67, 0.53, 0.0, 0.0, 0.0, 0.0]                    9.0   \n",
       "9  [0.71, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.4...                    8.0   \n",
       "\n",
       "   consecutive_chars  sequence_matcher_ratio  count_same_position  \\\n",
       "0                3.0                    0.43                  3.0   \n",
       "1                2.0                    0.38                  0.0   \n",
       "2                2.0                    0.35                  2.0   \n",
       "3                3.0                    0.56                  0.0   \n",
       "4                4.0                    0.31                  1.0   \n",
       "5                8.0                    0.41                  1.0   \n",
       "6                2.0                    0.22                  0.0   \n",
       "7                2.0                    0.26                  0.0   \n",
       "8                2.0                    0.44                  4.0   \n",
       "9                2.0                    0.20                  0.0   \n",
       "\n",
       "   count_total_unique_chars  count_common_chars  percent_matched  \\\n",
       "0                      24.0                 7.0             0.29   \n",
       "1                      16.0                 4.0             0.25   \n",
       "2                      21.0                 6.0             0.29   \n",
       "3                      15.0                 6.0             0.40   \n",
       "4                      30.0                11.0             0.37   \n",
       "5                      31.0                13.0             0.42   \n",
       "6                      19.0                 4.0             0.21   \n",
       "7                      28.0                 8.0             0.29   \n",
       "8                      14.0                 4.0             0.29   \n",
       "9                      21.0                 6.0             0.29   \n",
       "\n",
       "   percent_same_position  \n",
       "0                   0.19  \n",
       "1                   0.00  \n",
       "2                   0.17  \n",
       "3                   0.00  \n",
       "4                   0.04  \n",
       "5                   0.04  \n",
       "6                   0.00  \n",
       "7                   0.00  \n",
       "8                   0.44  \n",
       "9                   0.00  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def total_chars(row):\n",
    "\n",
    "    results0 = []\n",
    "    results1 = []\n",
    "    results2 = []\n",
    "    results3 = []\n",
    "    results4 = []\n",
    "    results5 = []\n",
    "    \n",
    "    p = row['parent_org_name'].lower()\n",
    "    for i in row['children_org_name_list']:\n",
    "        \n",
    "        t = i.lower()\n",
    "        \n",
    "        dict1 = Counter(p)\n",
    "        dict2 = Counter(t)\n",
    "        \n",
    "        commonDict = dict1 & dict2\n",
    "        allChars = len(dict1) + len(dict2)\n",
    "        childCharsPositions = len(t)\n",
    "        \n",
    "        results0.append((i, childCharsPositions)[1]) #totalCharacterPositions\n",
    "        results1.append((i, SequenceMatcher(None, t, p).find_longest_match(0, len(t), 0, len(p))[2])[1]) #consecutiveMatchingChars\n",
    "        results2.append((i, round(SequenceMatcher(None, t, p).ratio(),2))[1]) #sequence_matcher_ratio\n",
    "        results3.append((i, sum(int(k==v) for k,v in zip(t, p)))[1]) #countSamePosition\n",
    "        results4.append((i, allChars)[1])#countTotalUniqueChars\n",
    "        results5.append((i, len(commonDict))[1]) #countCommonChars\n",
    "\n",
    "    return pd.Series([results0, results1, results2, results3, results4, results5])\n",
    "\n",
    "df[['total_chars_positions'\n",
    "    , 'consecutive_chars'\n",
    "    , 'sequence_matcher_ratio'\n",
    "    , 'count_same_position'\n",
    "    , 'count_total_unique_chars'\n",
    "    , 'count_common_chars'\n",
    "   ]]  = df.apply(total_chars, axis=1)\n",
    "\n",
    "df['total_chars_positions'] = df.total_chars_positions.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['consecutive_chars'] = df.consecutive_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['sequence_matcher_ratio'] = df.sequence_matcher_ratio.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_same_position'] = df.count_same_position.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_total_unique_chars'] = df.count_total_unique_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_common_chars'] = df.count_common_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "\n",
    "df['percent_matched'] = round(df['count_common_chars'] / df['count_total_unique_chars'],2)\n",
    "df['percent_same_position'] = round(df['count_same_position'] / df['total_chars_positions'],2)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Bins columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parent_org_name</th>\n",
       "      <th>children_org_name_list</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>children_org_name</th>\n",
       "      <th>parent_trigram</th>\n",
       "      <th>child_trigram</th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>jaro_func_score_trigram_parent</th>\n",
       "      <th>jaro_func_score_trigram_child</th>\n",
       "      <th>...</th>\n",
       "      <th>count_total_unique_chars</th>\n",
       "      <th>count_common_chars</th>\n",
       "      <th>percent_matched</th>\n",
       "      <th>percent_same_position</th>\n",
       "      <th>jaro_score_bins</th>\n",
       "      <th>sequencer_ratio_bins</th>\n",
       "      <th>jaro_sequencer_equal</th>\n",
       "      <th>jaro_match_equal</th>\n",
       "      <th>sequencer_match_equal</th>\n",
       "      <th>jaro_sequencer_match_equal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"NEXARK INC\"</td>\n",
       "      <td>[NEWKIRK PRODUCTS]</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NEWKIRK PRODUCTS</td>\n",
       "      <td>[\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...</td>\n",
       "      <td>[NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...</td>\n",
       "      <td>[0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.19</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>+DESCONOCIDO</td>\n",
       "      <td>[DEGENKOLB]</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>LOW</td>\n",
       "      <td>DEGENKOLB</td>\n",
       "      <td>[+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...</td>\n",
       "      <td>[DEG, EGE, GEN, ENK, NKO, KOL, OLB]</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>+EMBOL LTDA</td>\n",
       "      <td>[EMPLOYBRIDGE]</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EMPLOYBRIDGE</td>\n",
       "      <td>[+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]</td>\n",
       "      <td>[EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...</td>\n",
       "      <td>0.63</td>\n",
       "      <td>[0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>[0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+MARTINS</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>SAN MARINA</td>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS]</td>\n",
       "      <td>[SAN, AN , N M,  MA, MAR, ARI, RIN, INA]</td>\n",
       "      <td>0.78</td>\n",
       "      <td>[0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]</td>\n",
       "      <td>[0.84, 0.62, 0.62, 0.48, 0.48, 0.48]</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>+RED COMUNICACAO &amp; MARKETING LTDA</td>\n",
       "      <td>[RED ENVELOPE ENTERTAINMENT]</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>RED ENVELOPE ENTERTAINMENT</td>\n",
       "      <td>[+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...</td>\n",
       "      <td>[RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...</td>\n",
       "      <td>0.57</td>\n",
       "      <td>[0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...</td>\n",
       "      <td>[0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.04</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>+TEGAPE IMPORTACAO E COMERCIO DE TECIDOS TECNI...</td>\n",
       "      <td>[ARTHUR LUNDGREN TECIDOS]</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>LOW</td>\n",
       "      <td>ARTHUR LUNDGREN TECIDOS</td>\n",
       "      <td>[+TE, TEG, EGA, GAP, APE, PE , E I,  IM, IMP, ...</td>\n",
       "      <td>[ART, RTH, THU, HUR, UR , R L,  LU, LUN, UND, ...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>[0.57, 0.57, 0.45, 0.45, 0.45, 0.45, 0.45, 0.4...</td>\n",
       "      <td>[0.58, 0.58, 0.51, 0.51, 0.51, 0.46, 0.46, 0.4...</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.04</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>+UNDEFINEDUNDEFINEDUNDEFINED</td>\n",
       "      <td>[UNITYPOINT HEALTH]</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>LOW</td>\n",
       "      <td>UNITYPOINT HEALTH</td>\n",
       "      <td>[+UN, UND, NDE, DEF, EFI, FIN, INE, NED, EDU, ...</td>\n",
       "      <td>[UNI, NIT, ITY, TYP, YPO, POI, OIN, INT, NT , ...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>[0.58, 0.46, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
       "      <td>[0.68, 0.68, 0.68, 0.59, 0.59, 0.59, 0.46, 0.4...</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101 DO BRASIL INDUSTRIAL LTDA.</td>\n",
       "      <td>[FORCE 10 NETWORKS]</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>LOW</td>\n",
       "      <td>FORCE 10 NETWORKS</td>\n",
       "      <td>[101, 01 , 1 D,  DO, DO , O B,  BR, BRA, RAS, ...</td>\n",
       "      <td>[FOR, ORC, RCE, CE , E 1,  10, 10 , 0 N,  NE, ...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>[0.79, 0.79, 0.51, 0.51, 0.46, 0.46, 0.0, 0.0,...</td>\n",
       "      <td>[0.68, 0.64, 0.46, 0.46, 0.46, 0.46, 0.46, 0.4...</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>111 SOUTH</td>\n",
       "      <td>[1&amp;1 IONOS]</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>1&amp;1 IONOS</td>\n",
       "      <td>[111, 11 , 1 S,  SO, SOU, OUT, UTH]</td>\n",
       "      <td>[1&amp;1, &amp;1 , 1 I,  IO, ION, ONO, NOS]</td>\n",
       "      <td>0.67</td>\n",
       "      <td>[0.67, 0.63, 0.53, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>[0.8, 0.67, 0.53, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2 DESIGN E COMUNICACAO</td>\n",
       "      <td>[FRANCE 2]</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>LOW</td>\n",
       "      <td>FRANCE 2</td>\n",
       "      <td>[2 D,  DE, DES, ESI, SIG, IGN, GN , N E,  E , ...</td>\n",
       "      <td>[FRA, RAN, ANC, NCE, CE , E 2]</td>\n",
       "      <td>0.58</td>\n",
       "      <td>[0.67, 0.59, 0.46, 0.46, 0.0, 0.0]</td>\n",
       "      <td>[0.71, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.4...</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>EQUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     parent_org_name  \\\n",
       "0                                       \"NEXARK INC\"   \n",
       "1                                       +DESCONOCIDO   \n",
       "2                                        +EMBOL LTDA   \n",
       "3                                           +MARTINS   \n",
       "4                  +RED COMUNICACAO & MARKETING LTDA   \n",
       "5  +TEGAPE IMPORTACAO E COMERCIO DE TECIDOS TECNI...   \n",
       "6                       +UNDEFINEDUNDEFINEDUNDEFINED   \n",
       "7                     101 DO BRASIL INDUSTRIAL LTDA.   \n",
       "8                                          111 SOUTH   \n",
       "9                             2 DESIGN E COMUNICACAO   \n",
       "\n",
       "         children_org_name_list  match_confidence_score match_confidence  \\\n",
       "0            [NEWKIRK PRODUCTS]                0.533333              LOW   \n",
       "1                   [DEGENKOLB]                0.666667              LOW   \n",
       "2                [EMPLOYBRIDGE]                0.600000              LOW   \n",
       "3                  [SAN MARINA]                0.750000           MEDIUM   \n",
       "4  [RED ENVELOPE ENTERTAINMENT]                0.625000              LOW   \n",
       "5     [ARTHUR LUNDGREN TECIDOS]                0.512821              LOW   \n",
       "6           [UNITYPOINT HEALTH]                0.444444              LOW   \n",
       "7           [FORCE 10 NETWORKS]                0.433333              LOW   \n",
       "8                   [1&1 IONOS]                0.769231           MEDIUM   \n",
       "9                    [FRANCE 2]                0.375000              LOW   \n",
       "\n",
       "            children_org_name  \\\n",
       "0            NEWKIRK PRODUCTS   \n",
       "1                   DEGENKOLB   \n",
       "2                EMPLOYBRIDGE   \n",
       "3                  SAN MARINA   \n",
       "4  RED ENVELOPE ENTERTAINMENT   \n",
       "5     ARTHUR LUNDGREN TECIDOS   \n",
       "6           UNITYPOINT HEALTH   \n",
       "7           FORCE 10 NETWORKS   \n",
       "8                   1&1 IONOS   \n",
       "9                    FRANCE 2   \n",
       "\n",
       "                                      parent_trigram  \\\n",
       "0  [\"NE, NEX, EXA, XAR, ARK, RK , K I,  IN, INC, ...   \n",
       "1  [+DE, DES, ESC, SCO, CON, ONO, NOC, OCI, CID, ...   \n",
       "2      [+EM, EMB, MBO, BOL, OL , L L,  LT, LTD, TDA]   \n",
       "3                     [+MA, MAR, ART, RTI, TIN, INS]   \n",
       "4  [+RE, RED, ED , D C,  CO, COM, OMU, MUN, UNI, ...   \n",
       "5  [+TE, TEG, EGA, GAP, APE, PE , E I,  IM, IMP, ...   \n",
       "6  [+UN, UND, NDE, DEF, EFI, FIN, INE, NED, EDU, ...   \n",
       "7  [101, 01 , 1 D,  DO, DO , O B,  BR, BRA, RAS, ...   \n",
       "8                [111, 11 , 1 S,  SO, SOU, OUT, UTH]   \n",
       "9  [2 D,  DE, DES, ESI, SIG, IGN, GN , N E,  E , ...   \n",
       "\n",
       "                                       child_trigram  jaro_func_score  \\\n",
       "0  [NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...             0.58   \n",
       "1                [DEG, EGE, GEN, ENK, NKO, KOL, OLB]             0.51   \n",
       "2  [EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...             0.63   \n",
       "3           [SAN, AN , N M,  MA, MAR, ARI, RIN, INA]             0.78   \n",
       "4  [RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...             0.57   \n",
       "5  [ART, RTH, THU, HUR, UR , R L,  LU, LUN, UND, ...             0.45   \n",
       "6  [UNI, NIT, ITY, TYP, YPO, POI, OIN, INT, NT , ...             0.47   \n",
       "7  [FOR, ORC, RCE, CE , E 1,  10, 10 , 0 N,  NE, ...             0.51   \n",
       "8                [1&1, &1 , 1 I,  IO, ION, ONO, NOS]             0.67   \n",
       "9                     [FRA, RAN, ANC, NCE, CE , E 2]             0.58   \n",
       "\n",
       "                      jaro_func_score_trigram_parent  \\\n",
       "0  [0.61, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1             [0.61, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0]   \n",
       "2  [0.62, 0.47, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....   \n",
       "3     [0.79, 0.64, 0.49, 0.49, 0.49, 0.49, 0.0, 0.0]   \n",
       "4  [0.7, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45, 0.45...   \n",
       "5  [0.57, 0.57, 0.45, 0.45, 0.45, 0.45, 0.45, 0.4...   \n",
       "6  [0.58, 0.46, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0...   \n",
       "7  [0.79, 0.79, 0.51, 0.51, 0.46, 0.46, 0.0, 0.0,...   \n",
       "8             [0.67, 0.63, 0.53, 0.0, 0.0, 0.0, 0.0]   \n",
       "9                 [0.67, 0.59, 0.46, 0.46, 0.0, 0.0]   \n",
       "\n",
       "                       jaro_func_score_trigram_child  ...  \\\n",
       "0  [0.68, 0.6, 0.52, 0.47, 0.47, 0.47, 0.47, 0.47...  ...   \n",
       "1  [0.7, 0.63, 0.48, 0.48, 0.0, 0.0, 0.0, 0.0, 0....  ...   \n",
       "2  [0.69, 0.61, 0.47, 0.47, 0.47, 0.47, 0.0, 0.0,...  ...   \n",
       "3               [0.84, 0.62, 0.62, 0.48, 0.48, 0.48]  ...   \n",
       "4  [0.79, 0.71, 0.62, 0.62, 0.58, 0.58, 0.46, 0.4...  ...   \n",
       "5  [0.58, 0.58, 0.51, 0.51, 0.51, 0.46, 0.46, 0.4...  ...   \n",
       "6  [0.68, 0.68, 0.68, 0.59, 0.59, 0.59, 0.46, 0.4...  ...   \n",
       "7  [0.68, 0.64, 0.46, 0.46, 0.46, 0.46, 0.46, 0.4...  ...   \n",
       "8              [0.8, 0.67, 0.53, 0.0, 0.0, 0.0, 0.0]  ...   \n",
       "9  [0.71, 0.49, 0.49, 0.49, 0.49, 0.49, 0.49, 0.4...  ...   \n",
       "\n",
       "   count_total_unique_chars  count_common_chars  percent_matched  \\\n",
       "0                      24.0                 7.0             0.29   \n",
       "1                      16.0                 4.0             0.25   \n",
       "2                      21.0                 6.0             0.29   \n",
       "3                      15.0                 6.0             0.40   \n",
       "4                      30.0                11.0             0.37   \n",
       "5                      31.0                13.0             0.42   \n",
       "6                      19.0                 4.0             0.21   \n",
       "7                      28.0                 8.0             0.29   \n",
       "8                      14.0                 4.0             0.29   \n",
       "9                      21.0                 6.0             0.29   \n",
       "\n",
       "   percent_same_position  jaro_score_bins  sequencer_ratio_bins  \\\n",
       "0                   0.19              LOW                   LOW   \n",
       "1                   0.00              LOW                   LOW   \n",
       "2                   0.17              LOW                   LOW   \n",
       "3                   0.00             HIGH                   LOW   \n",
       "4                   0.04              LOW                   LOW   \n",
       "5                   0.04              LOW                   LOW   \n",
       "6                   0.00              LOW                   LOW   \n",
       "7                   0.00              LOW                   LOW   \n",
       "8                   0.44              LOW                   LOW   \n",
       "9                   0.00              LOW                   LOW   \n",
       "\n",
       "   jaro_sequencer_equal  jaro_match_equal sequencer_match_equal  \\\n",
       "0                 EQUAL             EQUAL                 EQUAL   \n",
       "1                 EQUAL             EQUAL                 EQUAL   \n",
       "2                 EQUAL             EQUAL                 EQUAL   \n",
       "3             NOT EQUAL         NOT EQUAL             NOT EQUAL   \n",
       "4                 EQUAL             EQUAL                 EQUAL   \n",
       "5                 EQUAL             EQUAL                 EQUAL   \n",
       "6                 EQUAL             EQUAL                 EQUAL   \n",
       "7                 EQUAL             EQUAL                 EQUAL   \n",
       "8                 EQUAL         NOT EQUAL             NOT EQUAL   \n",
       "9                 EQUAL             EQUAL                 EQUAL   \n",
       "\n",
       "  jaro_sequencer_match_equal  \n",
       "0                      EQUAL  \n",
       "1                      EQUAL  \n",
       "2                      EQUAL  \n",
       "3                  NOT EQUAL  \n",
       "4                      EQUAL  \n",
       "5                      EQUAL  \n",
       "6                      EQUAL  \n",
       "7                      EQUAL  \n",
       "8                  NOT EQUAL  \n",
       "9                      EQUAL  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# def jaro_score_bins(df):\n",
    "#     if df.jaro_func_score >= .85:\n",
    "#         return 'HIGH'\n",
    "#     elif df.jaro_func_score >= .55 and df.jaro_func_score < .85:\n",
    "#         return 'MEDIUM'\n",
    "#     else:\n",
    "#         return 'LOW'\n",
    "    \n",
    "def jaro_score_bins(df):\n",
    "    if df.jaro_func_score >= .75:\n",
    "        return 'HIGH'\n",
    "    else:\n",
    "        return 'LOW'\n",
    "    \n",
    "df['jaro_score_bins'] = df.apply(jaro_score_bins, axis=1)\n",
    "\n",
    "# def sequencer_ratio_bins(df):\n",
    "#     if df.sequence_matcher_ratio >= .85:\n",
    "#         return 'HIGH'\n",
    "#     elif df.sequence_matcher_ratio >= .55 and df.sequence_matcher_ratio < .85:\n",
    "#         return 'MEDIUM'\n",
    "#     else:\n",
    "#         return 'LOW'\n",
    "    \n",
    "def sequencer_ratio_bins(df):\n",
    "    if df.sequence_matcher_ratio >= .75:\n",
    "        return 'HIGH'\n",
    "    else:\n",
    "        return 'LOW'\n",
    "    \n",
    "df['sequencer_ratio_bins'] = df.apply(sequencer_ratio_bins, axis=1)\n",
    "\n",
    "def jaro_sequencer_equal(df):\n",
    "    if df['jaro_score_bins'] == df['sequencer_ratio_bins']:\n",
    "        return 'EQUAL'\n",
    "    else:\n",
    "        return 'NOT EQUAL'\n",
    "    \n",
    "df['jaro_sequencer_equal'] = df.apply(jaro_sequencer_equal, axis=1)\n",
    "\n",
    "def jaro_match_equal(df):\n",
    "    if df['jaro_score_bins'] == df['match_confidence']:\n",
    "        return 'EQUAL'\n",
    "    else:\n",
    "        return 'NOT EQUAL'\n",
    "\n",
    "df['jaro_match_equal'] = df.apply(jaro_match_equal, axis=1)\n",
    "\n",
    "def sequencer_match_equal(df):\n",
    "    if df['sequencer_ratio_bins'] == df['match_confidence']:\n",
    "        return 'EQUAL'\n",
    "    else:\n",
    "        return 'NOT EQUAL'\n",
    "\n",
    "df['sequencer_match_equal'] = df.apply(sequencer_match_equal, axis=1)\n",
    "\n",
    "def jaro_sequencer_match_equal(df):\n",
    "    if df['jaro_score_bins'] == df['sequencer_ratio_bins'] and df['jaro_score_bins'] == df['match_confidence']:\n",
    "        return 'EQUAL'\n",
    "    else:\n",
    "        return 'NOT EQUAL'\n",
    "\n",
    "df['jaro_sequencer_match_equal'] = df.apply(jaro_sequencer_match_equal, axis=1)\n",
    "\n",
    "df.head(10)"
   ]
  },
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   "execution_count": 129,
   "metadata": {},
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     "name": "stdout",
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      "Jaro and Sequencer Bins: \n",
      "EQUAL        9838\n",
      "NOT EQUAL    6172\n",
      "Name: jaro_sequencer_equal, dtype: int64\n",
      "\n",
      "Jaro, Sequencer and Match Bins: \n",
      "EQUAL        8797\n",
      "NOT EQUAL    7213\n",
      "Name: jaro_sequencer_match_equal, dtype: int64\n",
      "\n",
      "Match Confidence Bins: \n",
      "LOW       12726\n",
      "MEDIUM     1857\n",
      "HIGH       1353\n",
      "HIGH-1       74\n",
      "Name: match_confidence, dtype: int64\n",
      "\n",
      "Jaro Score Bins: \n",
      "LOW     8279\n",
      "HIGH    7731\n",
      "Name: jaro_score_bins, dtype: int64\n",
      "\n",
      "Sequencer Ratio Bins: \n",
      "LOW     14409\n",
      "HIGH     1601\n",
      "Name: sequencer_ratio_bins, dtype: int64\n",
      "\n",
      "Jaro Match Bins: \n",
      "EQUAL        9103\n",
      "NOT EQUAL    6907\n",
      "Name: jaro_match_equal, dtype: int64\n",
      "\n",
      "Sequencer Match Bins: \n",
      "EQUAL        13488\n",
      "NOT EQUAL     2522\n",
      "Name: sequencer_match_equal, dtype: int64\n",
      "\n"
     ]
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   "source": [
    "Jaro_and_Sequencer_Bins = df['jaro_sequencer_equal'].value_counts()\n",
    "Jaro_Sequencer_and_Match_Bins = df['jaro_sequencer_match_equal'].value_counts()\n",
    "Jaro_Match_Bins = df['jaro_match_equal'].value_counts()\n",
    "Sequencer_Match_Bins = df['sequencer_match_equal'].value_counts()\n",
    "\n",
    "print('Jaro and Sequencer Bins: ')\n",
    "print(df['jaro_sequencer_equal'].value_counts())\n",
    "print('')\n",
    "print('Jaro, Sequencer and Match Bins: ')\n",
    "print(df['jaro_sequencer_match_equal'].value_counts())\n",
    "print('')\n",
    "print('Match Confidence Bins: ')\n",
    "print(df['match_confidence'].value_counts())\n",
    "print('')\n",
    "print('Jaro Score Bins: ')\n",
    "print(df['jaro_score_bins'].value_counts())\n",
    "print('')\n",
    "print('Sequencer Ratio Bins: ')\n",
    "print(df['sequencer_ratio_bins'].value_counts())\n",
    "print('')\n",
    "print('Jaro Match Bins: ')\n",
    "print(df['jaro_match_equal'].value_counts())\n",
    "print('')\n",
    "print('Sequencer Match Bins: ')\n",
    "print(df['sequencer_match_equal'].value_counts())\n",
    "print('')"
   ]
  },
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    "# Visualize Match Confidence, Jaro and Sequencer Breakdown"
   ]
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    "jsb_values = Jaro_and_Sequencer_Bins.values\n",
    "jsbm_values = Jaro_Sequencer_and_Match_Bins\n",
    "sm_values = Sequencer_Match_Bins\n",
    "jm_values = Jaro_Match_Bins\n",
    "\n",
    "labels = ['NOT EQUAL','EQUAL']\n",
    "values1 = jsb_values\n",
    "values2 = jsbm_values\n",
    "values3 = sm_values\n",
    "values4 = jm_values\n",
    "\n",
    "colors = ['darkblue', 'lightcyan']\n",
    "\n",
    "fig = make_subplots(rows=1\n",
    "                    , cols=4\n",
    "                    , specs=[[{'type':'domain'}\n",
    "                            , {'type':'domain'}\n",
    "                            , {'type':'domain'}\n",
    "                            , {'type':'domain'}]]\n",
    "                    ,subplot_titles=['JS'\n",
    "                                   , 'JSM'\n",
    "                                   , 'SM'\n",
    "                                   , 'JM'])\n",
    "\n",
    "fig.add_trace(go.Pie(labels=labels\n",
    "                     , values=values1\n",
    "                     , textinfo='text+label+value+percent'\n",
    "                     , name=\"Jaro_and_Sequencer_Bins\"\n",
    "                    ), 1, 1)\n",
    "fig.add_trace(go.Pie(labels=labels\n",
    "                     , values=values2\n",
    "                     , textinfo='text+label+value+percent'\n",
    "                     , name=\"Jaro_Sequencer_and_Match_Bins\"\n",
    "                    ), 1, 2)\n",
    "fig.add_trace(go.Pie(labels=labels\n",
    "                     , values=values3\n",
    "                     , textinfo='text+label+value+percent'\n",
    "                     , name=\"Sequencer_Match_Bins\"\n",
    "                    ), 1, 3)\n",
    "fig.add_trace(go.Pie(labels=labels\n",
    "                     , values=values4\n",
    "                     , textinfo='text+label+value+percent'\n",
    "                     , name=\"Jaro_Match_Bins\"\n",
    "                    ), 1, 4)\n",
    "\n",
    "# fig = go.Figure(data=[go.Pie(labels=labels, values=values)])\n",
    "fig.update_traces(textposition='inside'\n",
    "                 , textinfo='text+label+value+percent'\n",
    "                 , marker=dict(colors=colors)\n",
    "                 )\n",
    "\n",
    "fig.update_layout(\n",
    "    legend={'traceorder':'normal'}\n",
    "#     , title_text='Jaro and Sequencer Bins'\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create new df for Machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>total_chars_positions</th>\n",
       "      <th>consecutive_chars</th>\n",
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       "      <th>jaro_score_bins</th>\n",
       "      <th>sequencer_ratio_bins</th>\n",
       "      <th>jaro_sequencer_equal</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.73</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>[0.8, 0.74, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>0.9</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0.51, 0.51, 0.46, 0.0, 0.0]</td>\n",
       "      <td>[0.87, 0.81, 0.65, 0.54, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>LOW</td>\n",
       "      <td>LOW</td>\n",
       "      <td>EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.82</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>[0.78, 0.69, 0.0]</td>\n",
       "      <td>[0.91, 0.87, 0.75, 0.69, 0.56, 0.52, 0.51, 0.5...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.83</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>[0.79, 0.7, 0.0]</td>\n",
       "      <td>[0.91, 0.87, 0.56, 0.56, 0.51, 0.51, 0.51, 0.5...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.17</td>\n",
       "      <td>[0.81, 0.73, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>[0.8, 0.71, 0.51, 0.46, 0.46, 0.46, 0.46, 0.46...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>LOW</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "      <td>NOT EQUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jaro_func_score  total_chars_positions  consecutive_chars  \\\n",
       "0             0.73                   21.0                4.0   \n",
       "1             0.46                    7.0                4.0   \n",
       "2             0.82                    5.0                5.0   \n",
       "3             0.83                    5.0                5.0   \n",
       "4             0.75                   23.0                4.0   \n",
       "\n",
       "   sequence_matcher_ratio  count_same_position  count_total_unique_chars  \\\n",
       "0                    0.30                  4.0                      20.0   \n",
       "1                    0.39                  0.0                      24.0   \n",
       "2                    0.21                  5.0                      22.0   \n",
       "3                    0.29                  5.0                      20.0   \n",
       "4                    0.32                  4.0                      23.0   \n",
       "\n",
       "   count_common_chars  percent_matched  percent_same_position  \\\n",
       "0                 5.0             0.25                   0.19   \n",
       "1                 6.0             0.25                   0.00   \n",
       "2                 5.0             0.23                   1.00   \n",
       "3                 5.0             0.25                   1.00   \n",
       "4                 8.0             0.35                   0.17   \n",
       "\n",
       "                      jaro_func_score_trigram_parent  \\\n",
       "0  [0.82, 0.78, 0.52, 0.52, 0.52, 0.47, 0.47, 0.4...   \n",
       "1                       [0.51, 0.51, 0.46, 0.0, 0.0]   \n",
       "2                                  [0.78, 0.69, 0.0]   \n",
       "3                                   [0.79, 0.7, 0.0]   \n",
       "4  [0.81, 0.73, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....   \n",
       "\n",
       "                       jaro_func_score_trigram_child  match_confidence_score  \\\n",
       "0  [0.8, 0.74, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0....                     0.9   \n",
       "1  [0.87, 0.81, 0.65, 0.54, 0.0, 0.0, 0.0, 0.0, 0...                     0.9   \n",
       "2  [0.91, 0.87, 0.75, 0.69, 0.56, 0.52, 0.51, 0.5...                     0.9   \n",
       "3  [0.91, 0.87, 0.56, 0.56, 0.51, 0.51, 0.51, 0.5...                     0.9   \n",
       "4  [0.8, 0.71, 0.51, 0.46, 0.46, 0.46, 0.46, 0.46...                     0.9   \n",
       "\n",
       "  match_confidence jaro_score_bins sequencer_ratio_bins jaro_sequencer_equal  \\\n",
       "0             HIGH          MEDIUM                  LOW            NOT EQUAL   \n",
       "1             HIGH             LOW                  LOW                EQUAL   \n",
       "2             HIGH          MEDIUM                  LOW            NOT EQUAL   \n",
       "3             HIGH          MEDIUM                  LOW            NOT EQUAL   \n",
       "4             HIGH          MEDIUM                  LOW            NOT EQUAL   \n",
       "\n",
       "  jaro_sequencer_match_equal  \n",
       "0                  NOT EQUAL  \n",
       "1                  NOT EQUAL  \n",
       "2                  NOT EQUAL  \n",
       "3                  NOT EQUAL  \n",
       "4                  NOT EQUAL  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[['jaro_func_score' #0\n",
    "    , 'total_chars_positions' #1\n",
    "    , 'consecutive_chars'     #2\n",
    "    , 'sequence_matcher_ratio'#3\n",
    "    , 'count_same_position'   #4\n",
    "    , 'count_total_unique_chars' #5\n",
    "    , 'count_common_chars'       #6\n",
    "    , 'percent_matched'          #7\n",
    "    , 'percent_same_position'    #8\n",
    "    , 'jaro_func_score_trigram_parent' #9\n",
    "    , 'jaro_func_score_trigram_child'  #10\n",
    "    , 'match_confidence_score'         #11\n",
    "    , 'match_confidence'               #12\n",
    "    , 'jaro_score_bins'                #13\n",
    "    , 'sequencer_ratio_bins'           #14\n",
    "    , 'jaro_sequencer_equal'           #15\n",
    "    , 'jaro_sequencer_match_equal'     #16\n",
    "   ]]\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>total_chars_positions</th>\n",
       "      <th>consecutive_chars</th>\n",
       "      <th>sequence_matcher_ratio</th>\n",
       "      <th>count_same_position</th>\n",
       "      <th>count_total_unique_chars</th>\n",
       "      <th>count_common_chars</th>\n",
       "      <th>percent_matched</th>\n",
       "      <th>percent_same_position</th>\n",
       "      <th>jaro_func_score_trigram_parent</th>\n",
       "      <th>jaro_func_score_trigram_child</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>jaro_score_bins</th>\n",
       "      <th>sequencer_ratio_bins</th>\n",
       "      <th>jaro_sequencer_equal</th>\n",
       "      <th>jaro_sequencer_match_equal</th>\n",
       "      <th>jaro_prediction</th>\n",
       "      <th>sequencer_prediction</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.73</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.19</td>\n",
       "      <td>[0.82, 0.78, 0.52, 0.52, 0.52, 0.47, 0.47, 0.4...</td>\n",
       "      <td>[0.8, 0.74, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.46</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0.51, 0.51, 0.46, 0.0, 0.0]</td>\n",
       "      <td>[0.87, 0.81, 0.65, 0.54, 0.0, 0.0, 0.0, 0.0, 0...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.82</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>[0.78, 0.69, 0.0]</td>\n",
       "      <td>[0.91, 0.87, 0.75, 0.69, 0.56, 0.52, 0.51, 0.5...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.83</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>[0.79, 0.7, 0.0]</td>\n",
       "      <td>[0.91, 0.87, 0.56, 0.56, 0.51, 0.51, 0.51, 0.5...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.75</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.17</td>\n",
       "      <td>[0.81, 0.73, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....</td>\n",
       "      <td>[0.8, 0.71, 0.51, 0.46, 0.46, 0.46, 0.46, 0.46...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jaro_func_score  total_chars_positions  consecutive_chars  \\\n",
       "0             0.73                   21.0                4.0   \n",
       "1             0.46                    7.0                4.0   \n",
       "2             0.82                    5.0                5.0   \n",
       "3             0.83                    5.0                5.0   \n",
       "4             0.75                   23.0                4.0   \n",
       "\n",
       "   sequence_matcher_ratio  count_same_position  count_total_unique_chars  \\\n",
       "0                    0.30                  4.0                      20.0   \n",
       "1                    0.39                  0.0                      24.0   \n",
       "2                    0.21                  5.0                      22.0   \n",
       "3                    0.29                  5.0                      20.0   \n",
       "4                    0.32                  4.0                      23.0   \n",
       "\n",
       "   count_common_chars  percent_matched  percent_same_position  \\\n",
       "0                 5.0             0.25                   0.19   \n",
       "1                 6.0             0.25                   0.00   \n",
       "2                 5.0             0.23                   1.00   \n",
       "3                 5.0             0.25                   1.00   \n",
       "4                 8.0             0.35                   0.17   \n",
       "\n",
       "                      jaro_func_score_trigram_parent  \\\n",
       "0  [0.82, 0.78, 0.52, 0.52, 0.52, 0.47, 0.47, 0.4...   \n",
       "1                       [0.51, 0.51, 0.46, 0.0, 0.0]   \n",
       "2                                  [0.78, 0.69, 0.0]   \n",
       "3                                   [0.79, 0.7, 0.0]   \n",
       "4  [0.81, 0.73, 0.47, 0.0, 0.0, 0.0, 0.0, 0.0, 0....   \n",
       "\n",
       "                       jaro_func_score_trigram_child  match_confidence_score  \\\n",
       "0  [0.8, 0.74, 0.46, 0.46, 0.0, 0.0, 0.0, 0.0, 0....                     0.9   \n",
       "1  [0.87, 0.81, 0.65, 0.54, 0.0, 0.0, 0.0, 0.0, 0...                     0.9   \n",
       "2  [0.91, 0.87, 0.75, 0.69, 0.56, 0.52, 0.51, 0.5...                     0.9   \n",
       "3  [0.91, 0.87, 0.56, 0.56, 0.51, 0.51, 0.51, 0.5...                     0.9   \n",
       "4  [0.8, 0.71, 0.51, 0.46, 0.46, 0.46, 0.46, 0.46...                     0.9   \n",
       "\n",
       "  match_confidence jaro_score_bins sequencer_ratio_bins jaro_sequencer_equal  \\\n",
       "0                2               1                    0                    0   \n",
       "1                2               0                    0                    1   \n",
       "2                2               1                    0                    0   \n",
       "3                2               1                    0                    0   \n",
       "4                2               1                    0                    0   \n",
       "\n",
       "  jaro_sequencer_match_equal jaro_prediction sequencer_prediction Prediction  \n",
       "0                          0               1                    0          2  \n",
       "1                          0               0                    0          2  \n",
       "2                          0               1                    0          2  \n",
       "3                          0               1                    0          2  \n",
       "4                          0               1                    0          2  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def jaro_prediction(row):\n",
    "    if row['jaro_score_bins'] == 'HIGH':\n",
    "        return '2'\n",
    "    elif row['jaro_score_bins'] == 'MEDIUM':\n",
    "        return '1'\n",
    "    elif row['jaro_score_bins'] == 'LOW':\n",
    "        return '0'\n",
    "\n",
    "df2['jaro_prediction'] = df2.apply(jaro_prediction, axis=1)\n",
    "\n",
    "def sequencer_prediction(row):\n",
    "    if row['sequencer_ratio_bins'] == 'HIGH':\n",
    "        return '2'\n",
    "    elif row['sequencer_ratio_bins'] == 'MEDIUM':\n",
    "        return '1'\n",
    "    elif row['sequencer_ratio_bins'] == 'LOW':\n",
    "        return '0'\n",
    "\n",
    "df2['sequencer_prediction'] = df2.apply(sequencer_prediction, axis=1)\n",
    "\n",
    "def match_prediction(row):\n",
    "    if row['match_confidence'] == 'HIGH':\n",
    "        return '2'\n",
    "    elif row['match_confidence'] == 'MEDIUM':\n",
    "        return '1'\n",
    "    elif row['match_confidence'] == 'LOW':\n",
    "        return '0'\n",
    "\n",
    "df2['Prediction'] = df2.apply(match_prediction, axis=1)\n",
    "df2['match_confidence'] = df2.apply(match_prediction, axis=1)\n",
    "\n",
    "def jaro_score_bins(row):\n",
    "    if row['jaro_score_bins'] == 'HIGH':\n",
    "        return '2'\n",
    "    elif row['jaro_score_bins'] == 'MEDIUM':\n",
    "        return '1'\n",
    "    elif row['jaro_score_bins'] == 'LOW':\n",
    "        return '0'\n",
    "    \n",
    "df2['jaro_score_bins'] = df2.apply(jaro_score_bins, axis=1)\n",
    "\n",
    "def sequencer_ratio_bins(row):\n",
    "    if row['sequencer_ratio_bins'] == 'HIGH':\n",
    "        return '2'\n",
    "    elif row['sequencer_ratio_bins'] == 'MEDIUM':\n",
    "        return '1'\n",
    "    elif row['sequencer_ratio_bins'] == 'LOW':\n",
    "        return '0'\n",
    "\n",
    "df2['sequencer_ratio_bins'] = df2.apply(sequencer_ratio_bins, axis=1)\n",
    "\n",
    "def jaro_sequencer_equal(row):\n",
    "    if row['jaro_sequencer_equal'] == 'NOT EQUAL':\n",
    "        return '0'\n",
    "    elif row['jaro_sequencer_equal'] == 'EQUAL':\n",
    "        return '1'\n",
    "\n",
    "df2['jaro_sequencer_equal'] = df2.apply(jaro_sequencer_equal, axis=1)\n",
    "\n",
    "def jaro_sequencer_match_equal(row):\n",
    "    if row['jaro_sequencer_match_equal'] == 'NOT EQUAL':\n",
    "        return '0'\n",
    "    elif row['jaro_sequencer_match_equal'] == 'EQUAL':\n",
    "        return '1'\n",
    "\n",
    "df2['jaro_sequencer_match_equal'] = df2.apply(jaro_sequencer_match_equal, axis=1)\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'jaro_func_score')\n",
      "(1, 'total_chars_positions')\n",
      "(2, 'consecutive_chars')\n",
      "(3, 'sequence_matcher_ratio')\n",
      "(4, 'count_same_position')\n",
      "(5, 'count_total_unique_chars')\n",
      "(6, 'count_common_chars')\n",
      "(7, 'percent_matched')\n",
      "(8, 'percent_same_position')\n",
      "(9, 'jaro_func_score_trigram_parent')\n",
      "(10, 'jaro_func_score_trigram_child')\n",
      "(11, 'match_confidence_score')\n",
      "(12, 'match_confidence')\n",
      "(13, 'jaro_score_bins')\n",
      "(14, 'sequencer_ratio_bins')\n",
      "(15, 'jaro_sequencer_equal')\n",
      "(16, 'jaro_sequencer_match_equal')\n",
      "(17, 'jaro_prediction')\n",
      "(18, 'sequencer_prediction')\n",
      "(19, 'Prediction')\n"
     ]
    }
   ],
   "source": [
    "for i in enumerate(df2.columns):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaro Prediction: \n",
      " 2    371\n",
      "1    278\n",
      "0      9\n",
      "Name: jaro_prediction, dtype: int64\n",
      "Sequencer Prediction: \n",
      " 1    481\n",
      "0    146\n",
      "2     31\n",
      "Name: sequencer_prediction, dtype: int64\n",
      "Match Prediction: \n",
      " 0    494\n",
      "2    164\n",
      "Name: Prediction, dtype: int64\n",
      "\n",
      "X_train shape: \n",
      " (65, 16)\n",
      "X_test shape: \n",
      " (593, 16)\n",
      "y_train shape: \n",
      " (65,)\n",
      "y_test shape: \n",
      " (593,)\n",
      "\n",
      "x_train: [[0.92 7.0 6.0 ... '2' '1' '0']\n",
      " [0.87 31.0 13.0 ... '2' '1' '0']\n",
      " [0.86 5.0 5.0 ... '2' '0' '0']\n",
      " ...\n",
      " [0.78 11.0 6.0 ... '1' '1' '1']\n",
      " [0.79 8.0 4.0 ... '1' '0' '0']\n",
      " [0.82 12.0 3.0 ... '1' '1' '1']]\n",
      "y_train: ['2' '2' '2' '2' '2' '2' '2' '1' '1' '2' '2' '2' '2' '1' '2' '2' '1' '2'\n",
      " '1' '1' '2' '1' '1' '2' '2' '1' '2' '2' '1' '2' '1' '2' '1' '2' '2' '2'\n",
      " '1' '2' '2' '1' '1' '2' '2' '2' '2' '1' '2' '2' '1' '2' '2' '1' '1' '2'\n",
      " '1' '1' '1' '2' '2' '2' '2' '2' '1' '1' '1']\n",
      "x_test: [[0.88 12.0 4.0 ... '2' '1' '0']\n",
      " [0.94 11.0 11.0 ... '2' '1' '0']\n",
      " [0.88 36.0 11.0 ... '2' '1' '0']\n",
      " ...\n",
      " [0.88 9.0 5.0 ... '2' '1' '0']\n",
      " [0.96 4.0 4.0 ... '2' '2' '1']\n",
      " [0.86 21.0 6.0 ... '2' '1' '0']]\n",
      "y_test: ['2' '2' '2' '1' '2' '2' '2' '2' '2' '1' '0' '1' '1' '2' '1' '1' '2' '2'\n",
      " '2' '2' '2' '2' '2' '2' '1' '1' '1' '2' '1' '0' '2' '2' '1' '2' '2' '1'\n",
      " '1' '2' '1' '1' '2' '2' '2' '1' '1' '1' '1' '2' '1' '1' '2' '2' '2' '2'\n",
      " '2' '2' '2' '1' '1' '1' '2' '2' '2' '1' '2' '1' '1' '2' '1' '1' '2' '1'\n",
      " '2' '2' '1' '1' '2' '1' '2' '2' '2' '1' '1' '1' '2' '1' '2' '2' '1' '2'\n",
      " '1' '2' '1' '2' '1' '2' '2' '0' '1' '1' '2' '1' '1' '2' '2' '1' '2' '2'\n",
      " '2' '2' '2' '1' '2' '1' '2' '1' '0' '1' '2' '2' '2' '2' '2' '1' '2' '2'\n",
      " '2' '2' '2' '2' '1' '1' '2' '2' '2' '1' '1' '1' '1' '1' '1' '1' '2' '2'\n",
      " '2' '2' '2' '1' '2' '2' '1' '1' '2' '2' '1' '1' '2' '1' '2' '1' '2' '2'\n",
      " '2' '2' '2' '1' '1' '2' '1' '2' '1' '1' '1' '2' '2' '2' '1' '1' '2' '2'\n",
      " '1' '2' '1' '2' '2' '2' '2' '1' '1' '2' '0' '2' '1' '2' '2' '1' '2' '2'\n",
      " '2' '2' '1' '1' '1' '1' '2' '2' '2' '1' '2' '1' '1' '1' '1' '2' '1' '1'\n",
      " '2' '2' '1' '2' '2' '2' '0' '2' '1' '1' '2' '2' '1' '1' '2' '2' '1' '2'\n",
      " '2' '2' '2' '1' '2' '2' '1' '2' '2' '2' '1' '2' '2' '2' '1' '2' '1' '2'\n",
      " '1' '0' '2' '2' '1' '1' '2' '2' '2' '2' '1' '1' '2' '2' '1' '2' '1' '2'\n",
      " '2' '2' '2' '1' '1' '2' '1' '2' '2' '1' '1' '2' '2' '1' '1' '1' '2' '1'\n",
      " '2' '2' '1' '2' '2' '1' '2' '2' '2' '2' '2' '2' '1' '1' '1' '2' '1' '1'\n",
      " '1' '1' '2' '1' '1' '2' '2' '1' '2' '1' '2' '1' '1' '2' '1' '2' '1' '2'\n",
      " '1' '2' '2' '2' '1' '2' '1' '1' '2' '2' '2' '2' '1' '1' '2' '2' '1' '2'\n",
      " '2' '2' '2' '2' '1' '1' '1' '2' '1' '1' '1' '2' '1' '2' '2' '1' '2' '0'\n",
      " '2' '2' '2' '1' '2' '1' '2' '1' '1' '2' '2' '1' '1' '1' '1' '1' '1' '1'\n",
      " '2' '2' '2' '1' '2' '2' '2' '2' '2' '1' '1' '2' '2' '2' '2' '2' '2' '1'\n",
      " '2' '1' '2' '1' '1' '2' '2' '2' '2' '1' '1' '1' '2' '2' '1' '2' '2' '2'\n",
      " '1' '2' '2' '1' '2' '1' '2' '1' '2' '2' '2' '2' '2' '2' '1' '1' '1' '2'\n",
      " '1' '2' '1' '2' '1' '1' '1' '1' '2' '2' '1' '2' '1' '1' '2' '2' '2' '1'\n",
      " '1' '1' '1' '1' '2' '1' '2' '2' '1' '1' '2' '2' '1' '1' '2' '2' '1' '1'\n",
      " '1' '0' '1' '1' '2' '1' '2' '1' '1' '1' '2' '2' '2' '1' '1' '2' '2' '1'\n",
      " '2' '2' '2' '1' '2' '1' '2' '1' '2' '2' '2' '2' '2' '2' '2' '2' '2' '1'\n",
      " '1' '1' '2' '2' '1' '1' '2' '2' '2' '2' '1' '2' '2' '2' '2' '2' '2' '2'\n",
      " '1' '2' '2' '1' '1' '1' '2' '1' '2' '1' '2' '2' '1' '2' '2' '1' '2' '2'\n",
      " '1' '2' '2' '2' '2' '1' '1' '1' '1' '1' '1' '2' '2' '2' '1' '2' '1' '2'\n",
      " '2' '2' '2' '1' '2' '1' '2' '1' '1' '2' '1' '1' '1' '2' '2' '2' '1' '1'\n",
      " '1' '2' '1' '1' '2' '2' '2' '2' '2' '1' '2' '1' '1' '1' '2' '2' '2']\n"
     ]
    }
   ],
   "source": [
    "match_X = df2[df2.columns[0:16]].values\n",
    "\n",
    "X = df2[df2.columns[0:8]].values\n",
    "y = df2[df2.columns[17]].values\n",
    "jaro_y = df2[df2.columns[15]].values\n",
    "sequencer_y = df2[df2.columns[16]].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(match_X, y, test_size=0.9)\n",
    "jaro_X_train, jaro_X_test, jaro_y_train, jaro_y_test = model_selection.train_test_split(X, jaro_y, test_size=0.9)\n",
    "sequencer_X_train, sequencer_X_test, sequencer_y_train, sequencer_y_test = model_selection.train_test_split(X, sequencer_y, test_size=0.9)\n",
    "\n",
    "print('Jaro Prediction: \\n', df2.jaro_prediction.value_counts())\n",
    "print('Sequencer Prediction: \\n', df2.sequencer_prediction.value_counts())\n",
    "print('Match Prediction: \\n', df2.Prediction.value_counts())\n",
    "print('')\n",
    "print('X_train shape: \\n', X_train.shape)\n",
    "print('X_test shape: \\n', X_test.shape)\n",
    "print('y_train shape: \\n', y_train.shape)\n",
    "print('y_test shape: \\n', y_test.shape)\n",
    "print('')\n",
    "print('x_train:',X_train)\n",
    "print('y_train:',y_train)\n",
    "print('x_test:',X_test)\n",
    "print('y_test:',y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Classifiers and Confusion Matrices for Each Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-102-3accdad13431>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#logistic regression classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mlr_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1340\u001b[0m             \u001b[0m_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1342\u001b[0;31m         X, y = self._validate_data(X, y, accept_sparse='csr', dtype=_dtype,\n\u001b[0m\u001b[1;32m   1343\u001b[0m                                    \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"C\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1344\u001b[0m                                    accept_large_sparse=solver != 'liblinear')\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    794\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 796\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    797\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    597\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "#Match Confidence Classifiers\n",
    "#logistic regression classifier\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "lr_predictions = lr.predict(X_test)\n",
    "\n",
    "print('LR Confusion Matrix: \\n', confusion_matrix(y_test, lr_predictions))\n",
    "# print('LR y_pred_class: \\n', y_pred_class)\n",
    "print('LR Accuracy Score: ', accuracy_score(y_test, lr_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#random forest classifier\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)\n",
    "rfc_predictions = rfc.predict(X_test)\n",
    "# print('rfc score train: ',rfc.score(X_train, y_train))\n",
    "# print('rfc score test: ',rfc.score(X_test, y_test))\n",
    "print('RFC Confusion Matrix: \\n', confusion_matrix(y_test, rfc_predictions))\n",
    "print('RFC Accuracy Score: ', accuracy_score(y_test, rfc_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#knn classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=9)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_predictions = knn.predict(X_test)\n",
    "\n",
    "print('KNN Confusion Matrix: \\n', confusion_matrix(y_test, knn_predictions))\n",
    "# print('knn_predictions: \\n',knn_predictions)\n",
    "print('KNN Accuracy Score: ', accuracy_score(y_test, knn_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#svc classifier\n",
    "svc = SVC(C=1.0)\n",
    "svc.fit(X_train, y_train)\n",
    "svc_predictions = svc.predict(X_test)\n",
    "# print('svc score train: ',svc.score(X_train, y_train))\n",
    "# print('svc score test: ',svc.score(X_test, y_test))\n",
    "print('SVC Confusion Matrix: \\n', confusion_matrix(y_test, svc_predictions))\n",
    "print('SVC Accuracy Score: ', accuracy_score(y_test, svc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Confusion Matrix: \n",
      " [[410  17]\n",
      " [125  41]]\n",
      "LR Accuracy Score:  0.760539629005059\n",
      "\n",
      "RFC Confusion Matrix: \n",
      " [[414  13]\n",
      " [ 17 149]]\n",
      "RFC Accuracy Score:  0.9494097807757167\n",
      "\n",
      "KNN Confusion Matrix: \n",
      " [[388  39]\n",
      " [129  37]]\n",
      "KNN Accuracy Score:  0.7166947723440135\n",
      "\n",
      "SVC Confusion Matrix: \n",
      " [[422   5]\n",
      " [164   2]]\n",
      "SVC Accuracy Score:  0.715008431703204\n"
     ]
    }
   ],
   "source": [
    "#Jaro Classifiers\n",
    "#logistic regression classifier\n",
    "lr = LogisticRegression()\n",
    "lr.fit(jaro_X_train, jaro_y_train)\n",
    "lr_predictions = lr.predict(jaro_X_test)\n",
    "\n",
    "print('LR Confusion Matrix: \\n', confusion_matrix(jaro_y_test, lr_predictions))\n",
    "# print('LR y_pred_class: \\n', y_pred_class)\n",
    "print('LR Accuracy Score: ', accuracy_score(jaro_y_test, lr_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#random forest classifier\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(jaro_X_train, jaro_y_train)\n",
    "rfc_predictions = rfc.predict(jaro_X_test)\n",
    "# print('rfc score train: ',rfc.score(jaro_X_train, jaro_y_train))\n",
    "# print('rfc score test: ',rfc.score(jaro_X_test, jaro_y_test))\n",
    "print('RFC Confusion Matrix: \\n', confusion_matrix(jaro_y_test, rfc_predictions))\n",
    "print('RFC Accuracy Score: ', accuracy_score(jaro_y_test, rfc_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#knn classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=9)\n",
    "knn.fit(jaro_X_train, jaro_y_train)\n",
    "knn_predictions = knn.predict(jaro_X_test)\n",
    "\n",
    "print('KNN Confusion Matrix: \\n', confusion_matrix(jaro_y_test, knn_predictions))\n",
    "# print('knn_predictions: \\n',knn_predictions)\n",
    "print('KNN Accuracy Score: ', accuracy_score(jaro_y_test, knn_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#svc classifier\n",
    "svc = SVC(C=1.0)\n",
    "svc.fit(jaro_X_train, jaro_y_train)\n",
    "svc_predictions = svc.predict(jaro_X_test)\n",
    "# print('svc score train: ',svc.score(jaro_X_train, jaro_y_train))\n",
    "# print('svc score test: ',svc.score(jaro_X_test, jaro_y_test))\n",
    "print('SVC Confusion Matrix: \\n', confusion_matrix(jaro_y_test, svc_predictions))\n",
    "print('SVC Accuracy Score: ', accuracy_score(jaro_y_test, svc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Confusion Matrix: \n",
      " [[574   4]\n",
      " [ 10   5]]\n",
      "LR Accuracy Score:  0.9763912310286678\n",
      "\n",
      "RFC Confusion Matrix: \n",
      " [[577   1]\n",
      " [ 10   5]]\n",
      "RFC Accuracy Score:  0.9814502529510961\n",
      "\n",
      "KNN Confusion Matrix: \n",
      " [[578   0]\n",
      " [ 15   0]]\n",
      "KNN Accuracy Score:  0.9747048903878583\n",
      "\n",
      "SVC Confusion Matrix: \n",
      " [[578   0]\n",
      " [ 15   0]]\n",
      "SVC Accuracy Score:  0.9747048903878583\n"
     ]
    }
   ],
   "source": [
    "#Sequencer Ratio Classifiers\n",
    "#logistic regression classifier\n",
    "lr = LogisticRegression()\n",
    "lr.fit(sequencer_X_train, sequencer_y_train)\n",
    "lr_predictions = lr.predict(sequencer_X_test)\n",
    "\n",
    "print('LR Confusion Matrix: \\n', confusion_matrix(sequencer_y_test, lr_predictions))\n",
    "# print('LR y_pred_class: \\n', y_pred_class)\n",
    "print('LR Accuracy Score: ', accuracy_score(sequencer_y_test, lr_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#random forest classifier\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(sequencer_X_train, sequencer_y_train)\n",
    "rfc_predictions = rfc.predict(sequencer_X_test)\n",
    "# print('rfc score train: ',rfc.score(sequencer_X_train, sequencer_y_train))\n",
    "# print('rfc score test: ',rfc.score(sequencer_X_test, sequencer_y_test))\n",
    "print('RFC Confusion Matrix: \\n', confusion_matrix(sequencer_y_test, rfc_predictions))\n",
    "print('RFC Accuracy Score: ', accuracy_score(sequencer_y_test, rfc_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#knn classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=9)\n",
    "knn.fit(sequencer_X_train, sequencer_y_train)\n",
    "knn_predictions = knn.predict(sequencer_X_test)\n",
    "\n",
    "print('KNN Confusion Matrix: \\n', confusion_matrix(sequencer_y_test, knn_predictions))\n",
    "# print('knn_predictions: \\n',knn_predictions)\n",
    "print('KNN Accuracy Score: ', accuracy_score(sequencer_y_test, knn_predictions))\n",
    "\n",
    "print('')\n",
    "\n",
    "#svc classifier\n",
    "svc = SVC(C=1.0)\n",
    "svc.fit(sequencer_X_train, sequencer_y_train)\n",
    "svc_predictions = svc.predict(sequencer_X_test)\n",
    "# print('svc score train: ',svc.score(sequencer_X_train, sequencer_y_train))\n",
    "# print('svc score test: ',svc.score(sequencer_X_test, sequencer_y_test))\n",
    "print('SVC Confusion Matrix: \\n', confusion_matrix(sequencer_y_test, svc_predictions))\n",
    "print('SVC Accuracy Score: ', accuracy_score(sequencer_y_test, svc_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Confusion Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification report function and classifiers\n",
    "def pandas_classification_report(y_true, y_pred):\n",
    "    metrics_summary = precision_recall_fscore_support(\n",
    "        y_true=y_test\n",
    "        , y_pred=y_predictions\n",
    "        , labels=np.unique(y_pred)\n",
    "    )\n",
    "\n",
    "    avg = list(precision_recall_fscore_support(\n",
    "            y_true=y_test, \n",
    "            y_pred=y_predictions,\n",
    "            average='weighted'))\n",
    "\n",
    "    metrics_sum_index = ['precision', 'recall', 'f1-score', 'support']\n",
    "    class_report_df = pd.DataFrame(\n",
    "        list(metrics_summary),\n",
    "        index=metrics_sum_index)\n",
    "\n",
    "    support = class_report_df.loc['support']\n",
    "    total = support.sum() \n",
    "    avg[-1] = total\n",
    "\n",
    "    class_report_df['avg / total'] = avg\n",
    "\n",
    "    return class_report_df.T\n",
    "\n",
    "classifiers = {\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier(),\n",
    "    \"RandomForestClassifier\": RandomForestClassifier(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-65a92f3e5fbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0my_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mdf_class_report\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas_classification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0mself\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \"\"\"\n\u001b[0;32m--> 210\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m         return self._partial_fit(X, y, np.unique(y), _refit=True,\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    794\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 796\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    797\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    597\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "\n",
    "f, axes = plt.subplots(1, 6, figsize=(20, 5), sharey='row')\n",
    "\n",
    "print('Classification Report: \\n')\n",
    "\n",
    "for i, (key, classifier) in enumerate(classifiers.items()):\n",
    "    y_pred = classifier.fit(X_train, y_train).predict(X_test)\n",
    "    y_predictions = classifier.predict(X_test)\n",
    "    df_class_report = pandas_classification_report(y_true=y_test, y_pred=y_predictions)\n",
    "    print((classifier),'\\n',df_class_report)\n",
    "    cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(cf_matrix)\n",
    "#                                   display_labels=)\n",
    "    disp.plot(ax=axes[i], xticks_rotation=0)\n",
    "    disp.ax_.set_title(key)\n",
    "    disp.im_.colorbar.remove()\n",
    "    disp.ax_.set_xlabel('')\n",
    "    if i!=0:\n",
    "        disp.ax_.set_ylabel('')\n",
    "        \n",
    "f.text(0.4, 0.1, 'Predicted label', ha='left')\n",
    "plt.subplots_adjust(wspace=0.40, hspace=0.1)\n",
    "\n",
    "f.colorbar(disp.im_, ax=axes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaro Score Classification Report: \n",
      "\n",
      "GaussianNB() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.008000  0.444444  0.015717      9.0\n",
      "1             0.408602  0.150198  0.219653    253.0\n",
      "avg / total   0.174449  0.070826  0.093952    262.0\n",
      "LogisticRegression() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.013084  0.777778  0.025735      9.0\n",
      "1             0.448276  0.102767  0.167203    253.0\n",
      "avg / total   0.191453  0.055649  0.071727    262.0\n",
      "DecisionTreeClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.014388  0.666667  0.028169      9.0\n",
      "1             0.448864  0.312253  0.368298    253.0\n",
      "avg / total   0.191723  0.143339  0.157560    262.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.013825  0.666667  0.027088      9.0\n",
      "1             0.446541  0.280632  0.344660    253.0\n",
      "avg / total   0.190724  0.129848  0.147458    262.0\n",
      "KNeighborsClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.014523  0.777778  0.028513      9.0\n",
      "1             0.504505  0.221344  0.307692    253.0\n",
      "avg / total   0.215464  0.106239  0.131708    262.0\n",
      "SVC() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015358  1.000000  0.030252      9.0\n",
      "1             0.428571  0.011858  0.023077    253.0\n",
      "avg / total   0.183081  0.020236  0.010305    262.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 6, figsize=(20, 5), sharey='row')\n",
    "\n",
    "print('Jaro Score Classification Report: \\n')\n",
    "\n",
    "for i, (key, classifier) in enumerate(classifiers.items()):\n",
    "    y_pred = classifier.fit(jaro_X_train, jaro_y_train).predict(jaro_X_test)\n",
    "    y_predictions = classifier.predict(jaro_X_test)\n",
    "    df_class_report = pandas_classification_report(y_true=jaro_y_test, y_pred=y_predictions)\n",
    "    print((classifier),'\\n', df_class_report)\n",
    "    cf_matrix = confusion_matrix(jaro_y_test, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(cf_matrix)\n",
    "#                                   display_labels=)\n",
    "    disp.plot(ax=axes[i], xticks_rotation=0)\n",
    "    disp.ax_.set_title(key)\n",
    "    disp.im_.colorbar.remove()\n",
    "    disp.ax_.set_xlabel('')\n",
    "    if i!=0:\n",
    "        disp.ax_.set_ylabel('')\n",
    "        \n",
    "f.text(0.4, 0.1, 'Predicted label', ha='left')\n",
    "plt.subplots_adjust(wspace=0.40, hspace=0.1)\n",
    "\n",
    "f.colorbar(disp.im_, ax=axes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequencer Ratio Model Classification Report: \n",
      "\n",
      "GaussianNB() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015437  1.000000  0.030405      9.0\n",
      "1             0.500000  0.019763  0.038023    253.0\n",
      "avg / total   0.213556  0.023609  0.016684    262.0\n",
      "LogisticRegression() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015411  1.000000  0.030354      9.0\n",
      "1             0.555556  0.019763  0.038168    253.0\n",
      "avg / total   0.237258  0.023609  0.016745    262.0\n",
      "DecisionTreeClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015491  1.000000  0.030508      9.0\n",
      "1             0.416667  0.019763  0.037736    253.0\n",
      "avg / total   0.178004  0.023609  0.016563    262.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "/Users/acc48668/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning:\n",
      "\n",
      "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015358  1.000000  0.030252      9.0\n",
      "1             0.285714  0.007905  0.015385    253.0\n",
      "avg / total   0.122131  0.018550  0.007023    262.0\n",
      "KNeighborsClassifier() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015177  1.000000  0.029900      9.0\n",
      "avg / total   0.000230  0.015177  0.000454      9.0\n",
      "SVC() \n",
      "              precision    recall  f1-score  support\n",
      "0             0.015177  1.000000  0.029900      9.0\n",
      "avg / total   0.000230  0.015177  0.000454      9.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 6, figsize=(20, 5), sharey='row')\n",
    "\n",
    "print('Sequencer Ratio Model Classification Report: \\n')\n",
    "\n",
    "for i, (key, classifier) in enumerate(classifiers.items()):\n",
    "    y_pred = classifier.fit(sequencer_X_train, sequencer_y_train).predict(sequencer_X_test)\n",
    "    y_predictions = classifier.predict(sequencer_X_test)\n",
    "    df_class_report = pandas_classification_report(y_true=sequencer_y_test, y_pred=y_predictions)\n",
    "    print((classifier),'\\n', df_class_report)\n",
    "    cf_matrix = confusion_matrix(sequencer_y_test, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(cf_matrix)\n",
    "#                                   display_labels=)\n",
    "    disp.plot(ax=axes[i], xticks_rotation=0)\n",
    "    disp.ax_.set_title(key)\n",
    "    disp.im_.colorbar.remove()\n",
    "    disp.ax_.set_xlabel('')\n",
    "    if i!=0:\n",
    "        disp.ax_.set_ylabel('')\n",
    "        \n",
    "f.text(0.4, 0.1, 'Predicted label', ha='left')\n",
    "plt.subplots_adjust(wspace=0.40, hspace=0.1)\n",
    "\n",
    "f.colorbar(disp.im_, ax=axes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, axes = plt.subplots(2, 2, figsize=(20, 5), sharey='row')\n",
    "\n",
    "matrix = plot_confusion_matrix(lr, X_test, y_test)\n",
    "matrix.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "\n",
    "matrix1 = plot_confusion_matrix(knn, X_test, y_test)\n",
    "matrix1.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "print('Accuracy of the knn algorithm is: {}'.format(accuracy_score(y_test, knn_predictions)))\n",
    "print('')\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test, knn_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, (key, classifier) in enumerate(classifiers.items()):\n",
    "    y_pred = classifier.fit(X_train, y_train).predict(X_test)\n",
    "    y_predictions = classifier.predict(X_test)\n",
    "#     print((classifier), classification_report(y_true=y_test, y_pred=y_predictions, digits=2))\n",
    "    df_class_report = pandas_classification_report(y_true=y_test, y_pred=y_predictions)\n",
    "    print((classifier),'\\n',df_class_report)\n",
    "\n",
    "    table = []\n",
    "    f1 = list(f1_score(y_test, y_pred, average=\"macro\"))\n",
    "    print((classifier),':'\n",
    "          , 'f1_score:', (round(f1_score(y_test, y_pred, average=\"macro\"),2))\n",
    "          , 'precision_score:', (round(precision_score(y_test, y_predictions, average=\"macro\"),2))\n",
    "          , 'recall_score:', (round(recall_score(y_test, y_predictions, average=\"macro\"),2))\n",
    "          , 'accuracy_score:', (round(accuracy_score(y_test, y_predictions),2))\n",
    "#           , 'support:', (round(support(y_test, y_predictions),2))\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = plot_confusion_matrix(lr, X_test, y_test)\n",
    "matrix.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "print('Accuracy of the lr algorithm is: {}'.format(accuracy_score(y_test, lr_predictions)))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test, lr_predictions))\n",
    "\n",
    "matrix1 = plot_confusion_matrix(rfc, X_test, y_test)\n",
    "matrix1.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "print('Accuracy of the rfc algorithm is: {}'.format(accuracy_score(y_test, rfc_predictions)))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test, rfc_predictions))\n",
    "\n",
    "matrix = plot_confusion_matrix(knn, X_test, y_test)\n",
    "matrix.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "print('Accuracy of the knn algorithm is: {}'.format(accuracy_score(y_test, knn_predictions)))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test, knn_predictions))\n",
    "\n",
    "matrix = plot_confusion_matrix(svc, X_test, y_test)\n",
    "matrix.ax_.set_title('Confusion Matrix',color='white')\n",
    "plt.xlabel('Predicted Label', color='white')\n",
    "plt.ylabel('True Label', color='white')\n",
    "plt.gcf().axes[0].tick_params(color='white')\n",
    "plt.gcf().axes[1].tick_params(color='white')\n",
    "plt.gcf().set_size_inches(8,6)\n",
    "plt.show()\n",
    "print('Accuracy of the svc algorithm is: {}'.format(accuracy_score(y_test, svc_predictions)))\n",
    "print('Confusion Matrix: \\n', confusion_matrix(y_test, svc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def cm_analysis(y_true, y_pred, filename, labels, ymap=None, figsize=(10,10)):\n",
    "    \"\"\"\n",
    "    Generate matrix plot of confusion matrix with pretty annotations.\n",
    "    The plot image is saved to disk.\n",
    "    args: \n",
    "      y_true:    true label of the data, with shape (nsamples,)\n",
    "      y_pred:    prediction of the data, with shape (nsamples,)\n",
    "      filename:  filename of figure file to save\n",
    "      labels:    string array, name the order of class labels in the confusion matrix.\n",
    "                 use `clf.classes_` if using scikit-learn models.\n",
    "                 with shape (nclass,).\n",
    "      ymap:      dict: any -> string, length == nclass.\n",
    "                 if not None, map the labels & ys to more understandable strings.\n",
    "                 Caution: original y_true, y_pred and labels must align.\n",
    "      figsize:   the size of the figure plotted.\n",
    "    \"\"\"\n",
    "    if ymap != None:\n",
    "        y_pred = [ymap[yi] for yi in y_pred]\n",
    "        y_true = [ymap[yi] for yi in y_true]\n",
    "        labels = [ymap[yi] for yi in labels]\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=labels)\n",
    "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
    "    cm_perc = cm / cm_sum * 100\n",
    "    annot = np.empty_like(cm).astype(str)\n",
    "    nrows, ncols = cm.shape\n",
    "    for i in range(nrows):\n",
    "        for j in range(ncols):\n",
    "            c = cm[i, j]\n",
    "            p = cm_perc[i, j]\n",
    "            if i == j:\n",
    "                s = cm_sum[i]\n",
    "                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
    "            elif c == 0:\n",
    "                annot[i, j] = ''\n",
    "            else:\n",
    "                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
    "    cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
    "    cm.index.name = 'Actual'\n",
    "    cm.columns.name = 'Predicted'\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    sns.heatmap(cm, annot=annot, fmt='', ax=ax)\n",
    "    plt.savefig(filename)\n",
    "    \n",
    "# cm_analysis(y_train,y_test,'test.csv',['1','2'])"
   ]
  }
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