fuzzy_wuzzy_matching_company_name.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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\n",
    "from fuzzywuzzy import fuzz\n",
    "\n",
    "pd.set_option('display.max_columns', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0  [NEW, EWK, WKI, KIR, IRK, RK , K P,  PR, PRO, ...  \n",
       "1                [DEG, EGE, GEN, ENK, NKO, KOL, OLB]  \n",
       "2  [EMP, MPL, PLO, LOY, OYB, YBR, BRI, RID, IDG, ...  \n",
       "3           [SAN, AN , N M,  MA, MAR, ARI, RIN, INA]  \n",
       "4  [RED, ED , D E,  EN, ENV, NVE, VEL, ELO, LOP, ...  "
      ]
     },
     "execution_count": 12,
     "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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9110\n",
      "['OF', 'UNIVERSITY', 'COLLEGE', '&', 'GROUP', 'SERVICES', 'AND', 'DE', 'DOI', 'CORPORATION', 'HEALTH', 'COMMUNITY', 'TECHNOLOGY', 'THE', 'INSURANCE', 'MEDIA', 'INTERNATIONAL', 'INSTITUTE', 'STATE', 'COMPANY', 'SYSTEMS', 'ENERGY', 'NATIONAL', 'US', 'CENTER', 'TECHNOLOGIES', 'BANK', 'HOLDINGS', 'FINANCIAL', 'MEDICAL']\n"
     ]
    }
   ],
   "source": [
    "all_names = df['children_org_name'].unique()\n",
    "names_freq = Counter()\n",
    "for name in all_names:\n",
    "    names_freq.update(str(name).split(\" \"))\n",
    "key_words = [word for (word,_) in names_freq.most_common(30)]\n",
    "print(len(all_names))\n",
    "print(key_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sort_gp</th>\n",
       "      <th>names</th>\n",
       "      <th>alias</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1&amp;1 IONOS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1-800 SERVICE PARTNERS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>171263 CANADA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>20 MINUTES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>21ST CENTURY CASUALTY COMPANY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9105</th>\n",
       "      <td>Z</td>\n",
       "      <td>ZONEPERFECT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9106</th>\n",
       "      <td>Z</td>\n",
       "      <td>ZOOMBAK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9107</th>\n",
       "      <td>Z</td>\n",
       "      <td>ZURICH SEGUROS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9108</th>\n",
       "      <td>Z</td>\n",
       "      <td>ZWECKVERBAND KDN - DACHVERBAND KOMMUNALER IT-D...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9109</th>\n",
       "      <td>m</td>\n",
       "      <td>missingmissingmissingmissingmissing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sort_gp                                              names alias score\n",
       "0          1                                          1&1 IONOS   NaN   NaN\n",
       "1          1                             1-800 SERVICE PARTNERS   NaN   NaN\n",
       "2          1                                      171263 CANADA   NaN   NaN\n",
       "3          2                                         20 MINUTES   NaN   NaN\n",
       "4          2                      21ST CENTURY CASUALTY COMPANY   NaN   NaN\n",
       "...      ...                                                ...   ...   ...\n",
       "9105       Z                                        ZONEPERFECT   NaN   NaN\n",
       "9106       Z                                            ZOOMBAK   NaN   NaN\n",
       "9107       Z                                     ZURICH SEGUROS   NaN   NaN\n",
       "9108       Z  ZWECKVERBAND KDN - DACHVERBAND KOMMUNALER IT-D...   NaN   NaN\n",
       "9109       m                missingmissingmissingmissingmissing   NaN   NaN\n",
       "\n",
       "[9110 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_main_name = pd.DataFrame(columns=['sort_gp','names','alias','score'])\n",
    "all_names.sort()\n",
    "all_main_name['names'] = all_names\n",
    "all_main_name['sort_gp'] = all_main_name['names'].apply(lambda x: x[0])\n",
    "all_main_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress: 0.00%\n",
      "progress: 10.00%\n",
      "progress: 20.00%\n",
      "progress: 30.00%\n",
      "progress: 40.00%\n",
      "progress: 50.00%\n",
      "progress: 60.00%\n",
      "progress: 70.00%\n",
      "progress: 80.00%\n",
      "progress: 90.00%\n"
     ]
    }
   ],
   "source": [
    "all_sort_gp = all_main_name['sort_gp'].unique()\n",
    "\n",
    "def no_key_word(name):\n",
    "    \"\"\"check if the name contain the keywords in travel company\"\"\"\n",
    "    output = True\n",
    "    for key in key_words:\n",
    "        if key in name:\n",
    "            output = False\n",
    "    return output\n",
    "\n",
    "for sortgp in all_sort_gp:\n",
    "    this_gp = all_main_name.groupby(['sort_gp']).get_group(sortgp)\n",
    "    gp_start = this_gp.index.min()\n",
    "    gp_end = this_gp.index.max()\n",
    "    for i in range(gp_start,gp_end+1):\n",
    "    \n",
    "        # if self has not got alias, asign to be alias of itself\n",
    "        if pd.isna(all_main_name['alias'].iloc[i]):\n",
    "            all_main_name['alias'].iloc[i] = all_main_name['names'].iloc[i]\n",
    "            all_main_name['score'].iloc[i] = 100\n",
    "        \n",
    "        # if the following has not got alias and fuzzy match, asign to be alias of this one\n",
    "        for j in range(i+1,gp_end+1):\n",
    "            if pd.isna(all_main_name['alias'].iloc[j]):\n",
    "                fuzz_socre = fuzz.token_sort_ratio(all_main_name['names'].iloc[i],all_main_name['names'].iloc[j])\n",
    "                if not no_key_word(all_main_name['names'].iloc[j]):\n",
    "                    fuzz_socre -= 10\n",
    "                if (fuzz_socre > 85):\n",
    "                    all_main_name['alias'].iloc[j] = all_main_name['alias'].iloc[i]\n",
    "                    all_main_name['score'].iloc[j] = fuzz_socre\n",
    "                    \n",
    "        if i % (len(all_names)//10) == 0:\n",
    "            print(\"progress: %.2f\" % (100*i/len(all_names)) + \"%\")\n",
    "                \n",
    "all_main_name.to_csv('org_name_results_fuzzywuzzy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>sort_gp</th>\n",
       "      <th>names</th>\n",
       "      <th>alias</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>A</td>\n",
       "      <td>AFD INCORPORATED</td>\n",
       "      <td>ACS INCORPORATED</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>A</td>\n",
       "      <td>ALD AUTOMOTIVE</td>\n",
       "      <td>ADT AUTOMOTIVE</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>A</td>\n",
       "      <td>APHC INCORPORATED</td>\n",
       "      <td>ACS INCORPORATED</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>A</td>\n",
       "      <td>ARD INCORPORATED</td>\n",
       "      <td>ACS INCORPORATED</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>A</td>\n",
       "      <td>ARENA PHARMACEUTICALS</td>\n",
       "      <td>ANACOR PHARMACEUTICALS</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>A</td>\n",
       "      <td>ARROW PHARMACEUTICALS</td>\n",
       "      <td>ARAGON PHARMACEUTICALS</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>A</td>\n",
       "      <td>ASPECTA VERSICHERUNG</td>\n",
       "      <td>ASPECTA LEBENSVERSICHERUNG</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>A</td>\n",
       "      <td>AVEO PHARMACEUTICALS</td>\n",
       "      <td>ANACOR PHARMACEUTICALS</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>B</td>\n",
       "      <td>BT AMERICAS</td>\n",
       "      <td>BOAT AMERICA</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>C</td>\n",
       "      <td>COGEMAG</td>\n",
       "      <td>COGEMA</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>C</td>\n",
       "      <td>CONTINENTALE KRANKENVERSICHERUNG</td>\n",
       "      <td>CENTRAL KRANKENVERSICHERUNG</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>C</td>\n",
       "      <td>COSTAR</td>\n",
       "      <td>COINSTAR</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>C</td>\n",
       "      <td>CROSSMARK</td>\n",
       "      <td>CROSSMAR</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>D</td>\n",
       "      <td>DAIWABO INFORMATION SYSTEM</td>\n",
       "      <td>DAIKIN INFORMATION SYSTEM</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>D</td>\n",
       "      <td>DMN INCORPORATED</td>\n",
       "      <td>DBC INCORPORATED</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2904</th>\n",
       "      <td>E</td>\n",
       "      <td>ENTEL</td>\n",
       "      <td>ENEL</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2944</th>\n",
       "      <td>E</td>\n",
       "      <td>EQUINOX</td>\n",
       "      <td>EQUINIX</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3620</th>\n",
       "      <td>G</td>\n",
       "      <td>GREE ELECTRIC APPLIANCES</td>\n",
       "      <td>GOME ELECTRICAL APPLIANCE</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3714</th>\n",
       "      <td>G</td>\n",
       "      <td>GVA CONSULTANTS</td>\n",
       "      <td>GAI CONSULTANTS</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4223</th>\n",
       "      <td>I</td>\n",
       "      <td>INIVIO</td>\n",
       "      <td>INDIVIOR</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4477</th>\n",
       "      <td>J</td>\n",
       "      <td>JCIM</td>\n",
       "      <td>JCI</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4683</th>\n",
       "      <td>K</td>\n",
       "      <td>KIO UNIVERSITY</td>\n",
       "      <td>KEIO UNIVERSITY</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4760</th>\n",
       "      <td>K</td>\n",
       "      <td>KWANSEI GAKUIN EDUCATIONAL FOUNDATION</td>\n",
       "      <td>KUWASAWA GAKUEN EDUCATIONAL FOUNDATION</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>L</td>\n",
       "      <td>LOEWS HOTELS</td>\n",
       "      <td>LOEWS HOTEL</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5172</th>\n",
       "      <td>M</td>\n",
       "      <td>MARSH INCORPORATED</td>\n",
       "      <td>MARS INCORPORATED</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>M</td>\n",
       "      <td>MAXIS COMMUNICATIONS</td>\n",
       "      <td>MALEKI COMMUNICATIONS</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5382</th>\n",
       "      <td>M</td>\n",
       "      <td>MGA ENTERTAINMENT</td>\n",
       "      <td>MARVEL ENTERTAINMENT</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5429</th>\n",
       "      <td>M</td>\n",
       "      <td>MILGARD MANUFACTURING</td>\n",
       "      <td>MELARD MANUFACTURING</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5510</th>\n",
       "      <td>M</td>\n",
       "      <td>MITSUMI ELECTRIC</td>\n",
       "      <td>MITSUBISHI ELECTRIC</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5523</th>\n",
       "      <td>M</td>\n",
       "      <td>MM INVESTMENT</td>\n",
       "      <td>M&amp;G INVESTMENTS</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5856</th>\n",
       "      <td>N</td>\n",
       "      <td>NEWSLE</td>\n",
       "      <td>NEWSCALE</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>O</td>\n",
       "      <td>ONYX PHARMACEUTICALS</td>\n",
       "      <td>OMJ PHARMACEUTICALS</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6736</th>\n",
       "      <td>R</td>\n",
       "      <td>RED BULL</td>\n",
       "      <td>RED BALL</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7189</th>\n",
       "      <td>S</td>\n",
       "      <td>SERVIER</td>\n",
       "      <td>SERVAIR</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7355</th>\n",
       "      <td>S</td>\n",
       "      <td>SLTC INCORPORATED</td>\n",
       "      <td>SLACK INCORPORATED</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7498</th>\n",
       "      <td>S</td>\n",
       "      <td>SPIRE INCORPORATED</td>\n",
       "      <td>SPARTA INCORPORATED</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7516</th>\n",
       "      <td>S</td>\n",
       "      <td>SPRINT COMMUNICATIONS</td>\n",
       "      <td>SMART COMMUNICATIONS</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7522</th>\n",
       "      <td>S</td>\n",
       "      <td>SRC INCORPORATED</td>\n",
       "      <td>SLACK INCORPORATED</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7680</th>\n",
       "      <td>S</td>\n",
       "      <td>SUNBEAM TELEVISION</td>\n",
       "      <td>SUN TELEVISION</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7769</th>\n",
       "      <td>T</td>\n",
       "      <td>TA ASSOCIATES</td>\n",
       "      <td>T&amp;M ASSOCIATES</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8073</th>\n",
       "      <td>T</td>\n",
       "      <td>TOSEI GAKUEN EDUCATIONAL FOUNDATION</td>\n",
       "      <td>TANIOKA GAKUEN EDUCATIONAL FOUNDATION</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8153</th>\n",
       "      <td>T</td>\n",
       "      <td>TREMCO INCORPORATED</td>\n",
       "      <td>TCB INCORPORATED</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8239</th>\n",
       "      <td>T</td>\n",
       "      <td>TVO</td>\n",
       "      <td>TIVO</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8241</th>\n",
       "      <td>T</td>\n",
       "      <td>TW TELECOM</td>\n",
       "      <td>TSA TELECOM</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8357</th>\n",
       "      <td>U</td>\n",
       "      <td>UNIVERSIDAD CENTRO</td>\n",
       "      <td>UNIVERSIDAD CENTRAL</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8633</th>\n",
       "      <td>V</td>\n",
       "      <td>VELCON</td>\n",
       "      <td>VELCO</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8720</th>\n",
       "      <td>V</td>\n",
       "      <td>VITAKO</td>\n",
       "      <td>VISTAKON</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sort_gp                                  names  \\\n",
       "185        A                       AFD INCORPORATED   \n",
       "280        A                         ALD AUTOMOTIVE   \n",
       "504        A                      APHC INCORPORATED   \n",
       "551        A                       ARD INCORPORATED   \n",
       "554        A                  ARENA PHARMACEUTICALS   \n",
       "583        A                  ARROW PHARMACEUTICALS   \n",
       "636        A                   ASPECTA VERSICHERUNG   \n",
       "746        A                   AVEO PHARMACEUTICALS   \n",
       "1212       B                            BT AMERICAS   \n",
       "1756       C                                COGEMAG   \n",
       "1922       C       CONTINENTALE KRANKENVERSICHERUNG   \n",
       "1965       C                                 COSTAR   \n",
       "2036       C                              CROSSMARK   \n",
       "2132       D             DAIWABO INFORMATION SYSTEM   \n",
       "2372       D                       DMN INCORPORATED   \n",
       "2904       E                                  ENTEL   \n",
       "2944       E                                EQUINOX   \n",
       "3620       G               GREE ELECTRIC APPLIANCES   \n",
       "3714       G                        GVA CONSULTANTS   \n",
       "4223       I                                 INIVIO   \n",
       "4477       J                                   JCIM   \n",
       "4683       K                         KIO UNIVERSITY   \n",
       "4760       K  KWANSEI GAKUIN EDUCATIONAL FOUNDATION   \n",
       "4980       L                           LOEWS HOTELS   \n",
       "5172       M                     MARSH INCORPORATED   \n",
       "5230       M                   MAXIS COMMUNICATIONS   \n",
       "5382       M                      MGA ENTERTAINMENT   \n",
       "5429       M                  MILGARD MANUFACTURING   \n",
       "5510       M                       MITSUMI ELECTRIC   \n",
       "5523       M                          MM INVESTMENT   \n",
       "5856       N                                 NEWSLE   \n",
       "6144       O                   ONYX PHARMACEUTICALS   \n",
       "6736       R                               RED BULL   \n",
       "7189       S                                SERVIER   \n",
       "7355       S                      SLTC INCORPORATED   \n",
       "7498       S                     SPIRE INCORPORATED   \n",
       "7516       S                  SPRINT COMMUNICATIONS   \n",
       "7522       S                       SRC INCORPORATED   \n",
       "7680       S                     SUNBEAM TELEVISION   \n",
       "7769       T                          TA ASSOCIATES   \n",
       "8073       T    TOSEI GAKUEN EDUCATIONAL FOUNDATION   \n",
       "8153       T                    TREMCO INCORPORATED   \n",
       "8239       T                                    TVO   \n",
       "8241       T                             TW TELECOM   \n",
       "8357       U                     UNIVERSIDAD CENTRO   \n",
       "8633       V                                 VELCON   \n",
       "8720       V                                 VITAKO   \n",
       "\n",
       "                                       alias score  \n",
       "185                         ACS INCORPORATED    88  \n",
       "280                           ADT AUTOMOTIVE    93  \n",
       "504                         ACS INCORPORATED    91  \n",
       "551                         ACS INCORPORATED    88  \n",
       "554                   ANACOR PHARMACEUTICALS    88  \n",
       "583                   ARAGON PHARMACEUTICALS    88  \n",
       "636               ASPECTA LEBENSVERSICHERUNG    87  \n",
       "746                   ANACOR PHARMACEUTICALS    86  \n",
       "1212                            BOAT AMERICA    87  \n",
       "1756                                  COGEMA    92  \n",
       "1922             CENTRAL KRANKENVERSICHERUNG    88  \n",
       "1965                                COINSTAR    86  \n",
       "2036                                CROSSMAR    94  \n",
       "2132               DAIKIN INFORMATION SYSTEM    86  \n",
       "2372                        DBC INCORPORATED    88  \n",
       "2904                                    ENEL    89  \n",
       "2944                                 EQUINIX    86  \n",
       "3620               GOME ELECTRICAL APPLIANCE    86  \n",
       "3714                         GAI CONSULTANTS    93  \n",
       "4223                                INDIVIOR    86  \n",
       "4477                                     JCI    86  \n",
       "4683                         KEIO UNIVERSITY    87  \n",
       "4760  KUWASAWA GAKUEN EDUCATIONAL FOUNDATION    88  \n",
       "4980                             LOEWS HOTEL    96  \n",
       "5172                       MARS INCORPORATED    97  \n",
       "5230                   MALEKI COMMUNICATIONS    88  \n",
       "5382                    MARVEL ENTERTAINMENT    86  \n",
       "5429                    MELARD MANUFACTURING    93  \n",
       "5510                     MITSUBISHI ELECTRIC    86  \n",
       "5523                         M&G INVESTMENTS    86  \n",
       "5856                                NEWSCALE    86  \n",
       "6144                     OMJ PHARMACEUTICALS    87  \n",
       "6736                                RED BALL    88  \n",
       "7189                                 SERVAIR    86  \n",
       "7355                      SLACK INCORPORATED    91  \n",
       "7498                     SPARTA INCORPORATED    86  \n",
       "7516                    SMART COMMUNICATIONS    88  \n",
       "7522                      SLACK INCORPORATED    88  \n",
       "7680                          SUN TELEVISION    88  \n",
       "7769                          T&M ASSOCIATES    89  \n",
       "8073   TANIOKA GAKUEN EDUCATIONAL FOUNDATION    89  \n",
       "8153                        TCB INCORPORATED    86  \n",
       "8239                                    TIVO    86  \n",
       "8241                             TSA TELECOM    86  \n",
       "8357                     UNIVERSIDAD CENTRAL    92  \n",
       "8633                                   VELCO    91  \n",
       "8720                                VISTAKON    86  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_main_name[(all_main_name['names']!=all_main_name['alias']) & (all_main_name['alias'].notna())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_special_characters(txt):\n",
    "    seps = [\" \",\";\",\":\",\".\",\",\",\"*\",\"#\",\"@\",\"|\",\"/\",\"\\\\\",\"-\",\"_\",\"?\",\"%\",\"!\",\"^\",\"(\",\")\"]\n",
    "    default_sep = seps[0]\n",
    "\n",
    "def clean_stopword(txt):\n",
    "    temp_list = txt.split(\" \")\n",
    "    temp_list = [i for i in temp_list if i not in stopwords]\n",
    "    return \" \".join(temp_list)\n",
    "\n",
    "def data_cleaning(data, nameCol='children_org_name', dropForeign = True):\n",
    "    data.dropna(subset=[nameCol], inplace=True)\n",
    "    data = data.rename_axis('CompanyID').reset_index()\n",
    "    data['nonAscii_count'] = data[nameCol].apply(lambda x: sum([not c.isascii() for c in x]))\n",
    "    if dropForeign:\n",
    "        data = data[data.nonAscii_count==0]\n",
    "    else:\n",
    "        pass\n",
    "    data.drop('nonAscii_count', axis=1, inplace=True)\n",
    "    data_clean = data.copy()\n",
    "    data_clean['CompanyName_clean'] = data_clean[nameCol].apply(lambda x: x.lower())\n",
    "    data_clean['CompanyName_clean'] = data_clean['CompanyName_clean'].apply(clean_special_characters)\n",
    "    data_clean['CompanyName_clean'] = data_clean['CompanyName_clean'].apply(clean_stopword)\n",
    "    return data_clean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fuzz_similarity(comp_names):\n",
    "    similarity_array = np.ones((len(comp_names), (len(comp_names))))*100\n",
    "    \n",
    "    for i in range(1,len(comp_names)):\n",
    "        for j in range(i):\n",
    "            s1 = fuzz.token_set_ratio(comp_names[i],comp_names[j]) + 0.000000000001\n",
    "            s2 = fuzz.partial_ratio(comp_names[i],comp_names[j]) + 0.000000000001\n",
    "            similarity_array[i][j] = 2*s1*s2 / (s1+s2)\n",
    "    for i in range(len(comp_names)):\n",
    "        for j in range(i+1,len(comp_names)):\n",
    "            similarity_array[i][j] = similarity_array[j][i]\n",
    "            \n",
    "    np.fill_diagonal(similarity_array, 100)\n",
    "    return similarity_array\n",
    "\n",
    "def company_clusters(data, nameCol='children_org_name', dropForeign=True):\n",
    "    data_clean = data_cleaning(data, nameCol=nameCol, dropForeign=dropForeign)\n",
    "    comp_names = data_clean.CompanyName_clean.to_list()\n",
    "    cust_ids = data_clean.CompanyID.to_list()\n",
    "    \n",
    "    similarity_array = fuzz_similarity(comp_names)\n",
    "    clusters = cluster.AffinityPropagation(affinity='precomputed').fit_predict(similarity_array)\n",
    "    df_clusters = pd.DataFrame(list(zip(cust_ids, clusters)), columns=['CompanyID','clusters'])\n",
    "    \n",
    "    df_eval = df_clusters.merge(data_clean, on='CompanyID',how='left')\n",
    "    return df_eval\n",
    "\n",
    "def standard_name(df_eval):\n",
    "    d_standard_name = {}\n",
    "    for cluster in df_eval.cluster.unique():\n",
    "        names = df_eval[df_eval['cluster']==cluster].CompanyName_clean.to_list()\n",
    "        l_common_substring = []\n",
    "        if len(names)>1:\n",
    "            for i in range(0,len(names)):\n",
    "                for j in range(i+1,len(names)):\n",
    "                    seqMatch = SequenceMatcher(None,names[i],names[j])\n",
    "                    match = seqMatch.find_longest_match(0, len(names[i]), 0, len(names[j]))\n",
    "                    if (match.size!=0):\n",
    "                        l_common_substring.append(names[i][match.a: match.a + match.size].strip())\n",
    "            n= len(l_common_substring)\n",
    "            counts = Counter(l_common_substring)\n",
    "            get_mode = dict(counts)\n",
    "            mode = [k for k, v in get_mode.items() if v == max(list(counts.values()))]\n",
    "            d_standard_name[cluster] = \";\".join(mode)\n",
    "            \n",
    "        else:\n",
    "            d_standard_name[cluster] = names[0]\n",
    "\n",
    "    df_standard_names = pd.DataFrame(list(d_standard_name.items()), columns=['clusters','StandardName'])\n",
    "    df_eval = df_eval.merge(df_standard_names, on='cluster', how = 'left')\n",
    "    df_eval['standard_name_withoutSpaces'] = df_eval.StandardName.apply(lambda x: x.replace(\" \",\"\"))\n",
    "    for name in df_eval.standard_name_withoutSpaces.unique():\n",
    "        if len(df_eval[df_eval.standard_name_withoutSpaces==name].cluster.unique()) >1:\n",
    "            df_eval.loc[df_eval.standard_name_withoutSpsaces==name,'StandardName'] = name\n",
    "            \n",
    "    return df_eval.drop('standard_name_withoutSpaces', axis=1)\n"
   ]
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