3-Similarities.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa043715",
   "metadata": {},
   "source": [
    "# Problem formulation: \n",
    ">### * Differences between male and female in how they choose their mate\n",
    ">### * Which variable has biggest impact on getting a date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da6cbfe",
   "metadata": {},
   "source": [
    "# 1. Importing all the neccessary library\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6c11c17",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Basic Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt # we only need pyplot\n",
    "sb.set() # set the default Seaborn style for graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ac0fce2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/johntoro/Documents/Study Document/NTU/Sem 2 AN 2021-2022/SC1015/Project/SpeedDating-SC1015-project-/Developers/John\r\n"
     ]
    }
   ],
   "source": [
    "!PWD\n",
    "pd.options.display.max_rows = 400"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0cd5f7",
   "metadata": {},
   "source": [
    "First we have to read the data using csv reading function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b1ec432",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<p>10 rows × 195 columns</p>\n",
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      ],
      "text/plain": [
       "   iid   id  gender  idg  condtn  wave  round  position  positin1  order  ...  \\\n",
       "0    1  1.0       0    1       1     1     10         7       NaN      4  ...   \n",
       "1    1  1.0       0    1       1     1     10         7       NaN      3  ...   \n",
       "2    1  1.0       0    1       1     1     10         7       NaN     10  ...   \n",
       "3    1  1.0       0    1       1     1     10         7       NaN      5  ...   \n",
       "4    1  1.0       0    1       1     1     10         7       NaN      7  ...   \n",
       "5    1  1.0       0    1       1     1     10         7       NaN      6  ...   \n",
       "6    1  1.0       0    1       1     1     10         7       NaN      1  ...   \n",
       "7    1  1.0       0    1       1     1     10         7       NaN      2  ...   \n",
       "8    1  1.0       0    1       1     1     10         7       NaN      8  ...   \n",
       "9    1  1.0       0    1       1     1     10         7       NaN      9  ...   \n",
       "\n",
       "   attr3_3  sinc3_3  intel3_3  fun3_3  amb3_3  attr5_3  sinc5_3  intel5_3  \\\n",
       "0      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "1      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "2      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "3      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "4      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "5      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "6      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "7      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "8      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "9      5.0      7.0       7.0     7.0     7.0      NaN      NaN       NaN   \n",
       "\n",
       "   fun5_3  amb5_3  \n",
       "0     NaN     NaN  \n",
       "1     NaN     NaN  \n",
       "2     NaN     NaN  \n",
       "3     NaN     NaN  \n",
       "4     NaN     NaN  \n",
       "5     NaN     NaN  \n",
       "6     NaN     NaN  \n",
       "7     NaN     NaN  \n",
       "8     NaN     NaN  \n",
       "9     NaN     NaN  \n",
       "\n",
       "[10 rows x 195 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/data.csv\", encoding= 'ISO-8859-1')\n",
    "data.head(10)\n",
    "# data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b6b234f",
   "metadata": {},
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     "text": [
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       "fun7_3      float64\n",
       "amb7_3      float64\n",
       "shar7_3     float64\n",
       "attr4_3     float64\n",
       "sinc4_3     float64\n",
       "intel4_3    float64\n",
       "fun4_3      float64\n",
       "amb4_3      float64\n",
       "shar4_3     float64\n",
       "attr2_3     float64\n",
       "sinc2_3     float64\n",
       "intel2_3    float64\n",
       "fun2_3      float64\n",
       "amb2_3      float64\n",
       "shar2_3     float64\n",
       "attr3_3     float64\n",
       "sinc3_3     float64\n",
       "intel3_3    float64\n",
       "fun3_3      float64\n",
       "amb3_3      float64\n",
       "attr5_3     float64\n",
       "sinc5_3     float64\n",
       "intel5_3    float64\n",
       "fun5_3      float64\n",
       "amb5_3      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.shape)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c80df5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def countFreq(arr, n):\n",
    "    mp = dict()\n",
    "    for i in range(n):\n",
    "        if arr[i] in mp.keys():\n",
    "            mp[arr[i]] += 1\n",
    "        else:\n",
    "            mp[arr[i]] = 1\n",
    "    for x in mp:\n",
    "        print(x, \" \", mp[x])\n",
    "def splitGender(df):\n",
    "    mask_male = (df[\"gender\"] == 1)\n",
    "    mask_female = (df[\"gender\"] == 0)\n",
    "    return [df[mask_male].drop([\"gender\"], axis = 1), df[mask_female].drop([\"gender\"], axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abd126c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iid            0\n",
      "id             1\n",
      "gender         0\n",
      "idg            0\n",
      "condtn         0\n",
      "wave           0\n",
      "round          0\n",
      "position       0\n",
      "positin1    1846\n",
      "order          0\n",
      "partner        0\n",
      "pid           10\n",
      "match          0\n",
      "int_corr     158\n",
      "samerace       0\n",
      "age_o        104\n",
      "race_o        73\n",
      "pf_o_att      89\n",
      "pf_o_sin      89\n",
      "pf_o_int      89\n",
      "pf_o_fun      98\n",
      "pf_o_amb     107\n",
      "pf_o_sha     129\n",
      "dec_o          0\n",
      "attr_o       212\n",
      "sinc_o       287\n",
      "intel_o      306\n",
      "fun_o        360\n",
      "amb_o        722\n",
      "shar_o      1076\n",
      "like_o       250\n",
      "prob_o       318\n",
      "met_o        385\n",
      "age           95\n",
      "field         63\n",
      "field_cd      82\n",
      "undergra    3464\n",
      "mn_sat      5245\n",
      "tuition     4795\n",
      "race          63\n",
      "imprace       79\n",
      "imprelig      79\n",
      "from          79\n",
      "zipcode     1064\n",
      "income      4099\n",
      "goal          79\n",
      "date          97\n",
      "go_out        79\n",
      "career        89\n",
      "career_c     138\n",
      "sports        79\n",
      "tvsports      79\n",
      "exercise      79\n",
      "dining        79\n",
      "museums       79\n",
      "art           79\n",
      "hiking        79\n",
      "gaming        79\n",
      "clubbing      79\n",
      "reading       79\n",
      "tv            79\n",
      "theater       79\n",
      "movies        79\n",
      "concerts      79\n",
      "music         79\n",
      "shopping      79\n",
      "yoga          79\n",
      "exphappy     101\n",
      "expnum      6578\n",
      "attr1_1       79\n",
      "sinc1_1       79\n",
      "intel1_1      79\n",
      "fun1_1        89\n",
      "amb1_1        99\n",
      "shar1_1      121\n",
      "attr4_1     1889\n",
      "sinc4_1     1889\n",
      "intel4_1    1889\n",
      "fun4_1      1889\n",
      "amb4_1      1889\n",
      "shar4_1     1911\n",
      "attr2_1       79\n",
      "sinc2_1       79\n",
      "intel2_1      79\n",
      "fun2_1        79\n",
      "amb2_1        89\n",
      "shar2_1       89\n",
      "attr3_1      105\n",
      "sinc3_1      105\n",
      "fun3_1       105\n",
      "intel3_1     105\n",
      "amb3_1       105\n",
      "attr5_1     3472\n",
      "sinc5_1     3472\n",
      "intel5_1    3472\n",
      "fun5_1      3472\n",
      "amb5_1      3472\n",
      "dec            0\n",
      "attr         202\n",
      "sinc         277\n",
      "intel        296\n",
      "fun          350\n",
      "amb          712\n",
      "shar        1067\n",
      "like         240\n",
      "prob         309\n",
      "met          375\n",
      "match_es    1173\n",
      "attr1_s     4282\n",
      "sinc1_s     4282\n",
      "intel1_s    4282\n",
      "fun1_s      4282\n",
      "amb1_s      4282\n",
      "shar1_s     4282\n",
      "attr3_s     4378\n",
      "sinc3_s     4378\n",
      "intel3_s    4378\n",
      "fun3_s      4378\n",
      "amb3_s      4378\n",
      "satis_2      915\n",
      "length       915\n",
      "numdat_2     945\n",
      "attr7_2     6394\n",
      "sinc7_2     6423\n",
      "intel7_2    6394\n",
      "fun7_2      6394\n",
      "amb7_2      6423\n",
      "shar7_2     6404\n",
      "attr1_2      933\n",
      "sinc1_2      915\n",
      "intel1_2     915\n",
      "fun1_2       915\n",
      "amb1_2       915\n",
      "shar1_2      915\n",
      "attr4_2     2603\n",
      "sinc4_2     2603\n",
      "intel4_2    2603\n",
      "fun4_2      2603\n",
      "amb4_2      2603\n",
      "shar4_2     2603\n",
      "attr2_2     2603\n",
      "sinc2_2     2603\n",
      "intel2_2    2603\n",
      "fun2_2      2603\n",
      "amb2_2      2603\n",
      "shar2_2     2603\n",
      "attr3_2      915\n",
      "sinc3_2      915\n",
      "intel3_2     915\n",
      "fun3_2       915\n",
      "amb3_2       915\n",
      "attr5_2     4001\n",
      "sinc5_2     4001\n",
      "intel5_2    4001\n",
      "fun5_2      4001\n",
      "amb5_2      4001\n",
      "you_call    4404\n",
      "them_cal    4404\n",
      "date_3      4404\n",
      "numdat_3    6882\n",
      "num_in_3    7710\n",
      "attr1_3     4404\n",
      "sinc1_3     4404\n",
      "intel1_3    4404\n",
      "fun1_3      4404\n",
      "amb1_3      4404\n",
      "shar1_3     4404\n",
      "attr7_3     6362\n",
      "sinc7_3     6362\n",
      "intel7_3    6362\n",
      "fun7_3      6362\n",
      "amb7_3      6362\n",
      "shar7_3     6362\n",
      "attr4_3     5419\n",
      "sinc4_3     5419\n",
      "intel4_3    5419\n",
      "fun4_3      5419\n",
      "amb4_3      5419\n",
      "shar4_3     5419\n",
      "attr2_3     5419\n",
      "sinc2_3     5419\n",
      "intel2_3    5419\n",
      "fun2_3      5419\n",
      "amb2_3      5419\n",
      "shar2_3     6362\n",
      "attr3_3     4404\n",
      "sinc3_3     4404\n",
      "intel3_3    4404\n",
      "fun3_3      4404\n",
      "amb3_3      4404\n",
      "attr5_3     6362\n",
      "sinc5_3     6362\n",
      "intel5_3    6362\n",
      "fun5_3      6362\n",
      "amb5_3      6362\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105887e6",
   "metadata": {},
   "source": [
    "# 2. Similarity variables\n",
    "            - Does similarity of subject and partner decide anything\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1169d3f0",
   "metadata": {},
   "source": [
    "### Here i am looking for how important variables such as similarsites(Shared interest, race, same field, same region) are\n",
    "> We already have the subject's hobby preference: 17 variables ranged: 1-10 scale on how subject interested in activities. Sports, tvsports, exercise,... So to see whether subject and partner like each other or not we create SharedInterest variable which is the correlation coefficient among 2 people interest. This variable range from 0->1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f98f42",
   "metadata": {},
   "source": [
    "### Mapping partner position inside the similar dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "624804eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sports      79\n",
       "tvsports    79\n",
       "exercise    79\n",
       "dining      79\n",
       "museums     79\n",
       "art         79\n",
       "hiking      79\n",
       "gaming      79\n",
       "clubbing    79\n",
       "reading     79\n",
       "tv          79\n",
       "theater     79\n",
       "movies      79\n",
       "concerts    79\n",
       "music       79\n",
       "shopping    79\n",
       "yoga        79\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interests = [\"sports\", \"tvsports\", \"exercise\", \"dining\", \"museums\", \n",
    "             \"art\", \"hiking\", \"gaming\", \"clubbing\", \"reading\", \"tv\", \"theater\", \n",
    "             \"movies\", \"concerts\", \"music\", \"shopping\", \"yoga\"]\n",
    "data[interests].isnull().sum()\n",
    "# print(data[\"match\"].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fed591",
   "metadata": {},
   "source": [
    "#### Here we can see a systematically missing 79 values in ```interests``` category,  (This is due to all of 79 missing values come from exactly 79 rows, 79 people did not fill this part of survey) we just remove these rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dff7a8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8299, 195)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sim = data.copy()\n",
    "data_sim = data_sim[data_sim[\"sports\"].notna()]\n",
    "data_sim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c85fc50",
   "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>match</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8373</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8374</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8375</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8376</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8377</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8299 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match  gender\n",
       "0         0       0\n",
       "1         0       0\n",
       "2         1       0\n",
       "3         1       0\n",
       "4         1       0\n",
       "...     ...     ...\n",
       "8373      0       1\n",
       "8374      0       1\n",
       "8375      0       1\n",
       "8376      0       1\n",
       "8377      0       1\n",
       "\n",
       "[8299 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar = pd.DataFrame(data_sim[[\"match\", \"gender\"]])\n",
    "similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71abddf3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing partner entry:  89\n",
      "Missing Values after mereged and the shape of desired similar dataset\n",
      "(8299, 3)\n",
      "match             0\n",
      "gender            0\n",
      "SharedInterest    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "id_table = {}\n",
    "cnt_missing_partner = 0\n",
    "for i in range(data_sim.shape[0]):\n",
    "    id_table[(data_sim.iloc[i][\"iid\"], data_sim.iloc[i][\"id\"])] = i\n",
    "\n",
    "partner = []\n",
    "temp = pd.Series(np.array([0.0 for i in range(data_sim.shape[0])]), index = data_sim.index)\n",
    "\n",
    "for i in range(data_sim.shape[0]):\n",
    "    own_in = data_sim[interests].iloc[i]\n",
    "    partner_in = []\n",
    "    partner_iid = data_sim.iloc[i][\"pid\"]\n",
    "    partner_id = data_sim.iloc[i][\"partner\"]\n",
    "    partner_index = 0\n",
    "    if(id_table.get((partner_iid, partner_id))):\n",
    "        partner_index = id_table.get((partner_iid, partner_id))\n",
    "        partner.append(partner_index)\n",
    "        partner_in = data_sim.iloc[partner_index][interests].astype(float)\n",
    "    else:\n",
    "        cnt_missing_partner += 1\n",
    "    if(len(partner_in) == len(own_in)):\n",
    "        temp.iloc[i] = (np.corrcoef(own_in, partner_in)[0, 1] + 1) / 2\n",
    "\n",
    "print(\"Number of missing partner entry: \", cnt_missing_partner)\n",
    "similar = pd.concat([similar, temp.rename(\"SharedInterest\")], axis=1)\n",
    "# Checking for the null value after concatenate: error might be \n",
    "# happen during the merge: missing index, missing value.\n",
    "print(\"Missing Values after mereged and the shape of desired similar dataset\")\n",
    "print(similar.shape)\n",
    "print(similar.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fafba7df",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(18, 8))\n",
    "sb.histplot(data = similar[\"SharedInterest\"], ax = axes[0])\n",
    "sb.violinplot(data = similar[\"SharedInterest\"], orient = \"h\", ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "442b0b77",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9dc785f0f0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.catplot(x=\"match\", y=\"SharedInterest\", kind=\"box\", data=similar).set_xlabels(\"Match\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4160bebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    sb.catplot(x=\"match\", y=\"SharedInterest\", kind=\"box\", data=similar).set_xlabels(\"Match\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fbd902d",
   "metadata": {},
   "source": [
    "### This code will combine 4 variables : SharedInterest, Samerace, Same Region(```from``` variable), same field (aggregated to Business, Law, Service, and Academic from field variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2512e87",
   "metadata": {},
   "source": [
    "Secondly, we go with ```race``` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a126efc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_sim[\"race_o\"] = data_sim[\"race_o\"].map({2.0 : \"Europe\", 4.0 : \"Asian\", 3.0 : \"Latin\", 6.0 : \"Other\", 1.0: \"Black\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33899d70",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sb.catplot(y = \"race_o\", data = data_sim, kind = \"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5aa0ab",
   "metadata": {},
   "source": [
    "Here we can extend our analysis further: to racial preference but due to the limit of the research scope, we place the problem in future analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1a173a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "similar[\"SameRace\"] = data_sim[\"samerace\"]\n",
    "print(similar[\"SameRace\"].value_counts())\n",
    "sb.catplot(y = \"SameRace\", data = similar, kind = \"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebce8e4e",
   "metadata": {},
   "source": [
    "Next we will head into the ```from``` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c763803",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Missing values in 'from' variable: \", data_sim[\"from\"].isnull().sum())\n",
    "data_sim[\"from\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20e90b8",
   "metadata": {},
   "source": [
    "So ```from``` does not have any missing value, that is safe. Continue on.\n",
    "\n",
    "Here we can see the ```from``` variable consists of city's name where subject live, but we only care about whether two people in a date is come from one region or not, it is natural if we aggregate ```from``` values into categories: [North America, Western Europe, Eastern Europe, Central Asia, Middle East, South Asia, East Asia, Latin America,\n",
    "or Africa]\n",
    "\n",
    "We can use a library to classify these cities into above categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a963435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checkpoint during running code\n",
    "similar.shape\n",
    "similar.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c018432",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "city_name = data_sim[\"from\"].unique()\n",
    "print(len(city_name))\n",
    "print(city_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70b2f14",
   "metadata": {},
   "source": [
    "We can see here, there is a lot of ancronym and flaw format answer ( most of them come from USA ), so we just mapping some flaw answer city name into already defined map and the rest go with USA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05cb1883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "flaw_address = [\"94115\", \"Wash DC (4 yrs)\", \"I am from NYC\", \"San Francisco(home)/Los Angeles(undergrad)\", \n",
    " \"nyc\", \"way too little space here. world citizen.\"\n",
    ", \"NYC-6 yrs. Grew up in Nebraska\", \"Born in Montana, raised in South Jersey (nr. Philadelphia)\", \n",
    " \"raised in South Jersey (nr. Philadelphia)\",  \"J.P. Morgan\", \"CT, FL, TN\", \"Katonah, NY (more recently, Boston)\"]\n",
    "\n",
    "US = ['Brooklyn', 'Florida', 'Tuscaloosa, Alabama', 'Albuquerque, NM'\n",
    " 'Palo Alto, California','Boston, MA', 'Mexico', 'Torrance, CA',\n",
    " 'St. Louis, MO','South Orange, New Jersey',\n",
    " 'Boston, Ma' , 'Kansas City, Missouri', 'Upstate New York']\n",
    "\n",
    "usRegex = re.compile(r\"New York|NY|N.Y|NYC|PA|[C-c]alifornia|CA|Los Angeles|DC|D.C|USA|TX|MA|GA|NJ|nyc|San Francisco\")\n",
    "country_mapping = {}\n",
    "for i in city_name:\n",
    "    res = usRegex.search(i)\n",
    "    if(res is not None):\n",
    "        US.append(i)\n",
    "for i in flaw_address:\n",
    "    country_mapping[i] = \"United States\"\n",
    "for i in US:\n",
    "    country_mapping[i] = \"United States\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e7048af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(country_mapping)\n",
    "print(len(country_mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1725f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopy as gp\n",
    "from geopy import geocoders\n",
    "from geopy.geocoders import Nominatim\n",
    "from geopy.exc import GeocoderTimedOut\n",
    "\n",
    "# A recursive function used to continue request the data even it is bad request\n",
    "def do_geocode(address, attempt=1, max_attempts=5):\n",
    "    try:\n",
    "        return geolocator.geocode(address, exactly_one = False, language=\"en\")\n",
    "    except GeocoderTimedOut:\n",
    "        if attempt <= max_attempts:\n",
    "            return do_geocode(address, attempt=attempt+1)\n",
    "        raise\n",
    "\n",
    "gp.geocoders.options.default_user_agent = \"Phan Nhat Hoang\"\n",
    "geolocator = Nominatim(timeout=5, scheme='http', user_agent = gp.geocoders.options.default_user_agent)\n",
    "\n",
    "city_name = data_sim[\"from\"].unique()\n",
    "len_city = len(city_name)\n",
    "\n",
    "for i in city_name:\n",
    "    if(i in flaw_address or i in US):\n",
    "        continue\n",
    "    res = do_geocode(i)\n",
    "    if(res is not None):\n",
    "        location = geolocator.reverse(str(res[0].latitude) + \",\" + str(res[0].longitude), language=\"en\")\n",
    "        country = location.raw['address'][\"country\"]\n",
    "        country_mapping[i] = country\n",
    "    elif not (country_mapping.get(i)):\n",
    "        country_mapping[i] = \"United States\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3921842",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_sim[\"Region\"] = pd.Series([\"\" for i in range(data_sim.shape[0])]).astype(\"string\")\n",
    "for i in range(data_sim.shape[0]):\n",
    "    data_sim.loc[:,\"Region\"].iloc[i] = country_mapping[data_sim.iloc[i][\"from\"]]\n",
    "data_sim[\"Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b998ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "region_data = pd.read_csv(\"data/necessary/continents2.csv\")\n",
    "# print(region_data.head(5))\n",
    "region_mapping = {}\n",
    "\n",
    "# print(region_data.loc[:,\"name\"].iloc[1])\n",
    "for i in range(region_data.shape[0]):\n",
    "    region_mapping[str(region_data.loc[:,\"name\"].iloc[i])] = region_data.loc[:,\"sub-region\"].iloc[i]\n",
    "\n",
    "for i in range(similar.shape[0]):\n",
    "    if(region_mapping.get(data_sim.iloc[i][\"Region\"]) is not None):\n",
    "        data_sim.loc[:,\"Region\"].iloc[i] = region_mapping.get(data_sim.loc[:, \"Region\"].iloc[i])\n",
    "#     else:       \n",
    "#         print(data_sim.iloc[i][\"Region\"])\n",
    "print(\"Missing value: \", data_sim[\"Region\"].isnull().sum())\n",
    "data_sim[\"Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccbb6239",
   "metadata": {},
   "outputs": [],
   "source": [
    "# id_table = {(iid, id) : position (reference to rows) in data_sim}\n",
    "\n",
    "similar[\"SameRegion\"] = pd.Series([0 for i in range(data_sim.shape[0])], index = data_sim.index).astype(\"int\")\n",
    "for i in range(data_sim.shape[0]):\n",
    "    partner_iid = data_sim.iloc[i][\"pid\"]\n",
    "    partner_id = data_sim.iloc[i][\"partner\"]\n",
    "    partner_index = 0\n",
    "    if(id_table.get((partner_iid, partner_id))):\n",
    "        partner_index = id_table.get((partner_iid, partner_id))\n",
    "        if (data_sim.iloc[i][\"Region\"] == data_sim.iloc[partner_index][\"Region\"]):\n",
    "            similar.at[similar.index[i], \"SameRegion\"] = 1\n",
    "        else:\n",
    "            similar.at[similar.index[i], \"SameRegion\"] = 0\n",
    "    else:\n",
    "        similar.at[similar.index[i], \"SameRegion\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b7d66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking for the null value after concatenate: error might be \n",
    "# happen during the merge: missing index, missing value.\n",
    "print(\"Missing Values after mereged and the shape of desired similar dataset\")\n",
    "print(similar.shape)\n",
    "print(similar.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a17f3f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "similar[\"SameRegion\"]\n",
    "sb.catplot(y = \"SameRegion\", data = similar, kind = \"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f1553e",
   "metadata": {},
   "source": [
    "### ```SameField``` variable,  \n",
    "Same Field is an indicator variable denoting that the subject and partner are in the same area of\n",
    "study (aggregated to Business, Law, Service, and Academic) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d5fdf9c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Econ         1925\n",
       "Bio. Sc.      993\n",
       "Eng.          864\n",
       "Pol. Sci.     709\n",
       "Soc. Sc.      680\n",
       "Law           665\n",
       "Educ.         626\n",
       "Soc. Wrk.     468\n",
       "Journ.        325\n",
       "Hist.         241\n",
       "Math          207\n",
       "Arts          187\n",
       "Med Sci       143\n",
       "Film          126\n",
       "Oth.           52\n",
       "Lang.          40\n",
       "NaN            19\n",
       "Undergrad      19\n",
       "Arch.          10\n",
       "Name: field_cd, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This series is solely serve the data visualization purpose\n",
    "temp_field = pd.DataFrame()\n",
    "temp_field[\"field_cd\"] = data_sim.field_cd.map({1: 'Law', 2: 'Math', 3: 'Soc. Sc.', 4: 'Med Sci',\n",
    "                                 5: 'Eng.', 6: 'Journ.', 7: 'Hist.', 8: 'Econ', 9: 'Educ.',\n",
    "                                 10: 'Bio. Sc.', 11: 'Soc. Wrk.', 12: 'Undergrad', 13: 'Pol. Sci.',\n",
    "                                 14: 'Film', 15: 'Arts', 16:'Lang.', 17: 'Arch.', 18: 'Oth.'}).fillna(data_sim.field_cd)\n",
    "temp_field[\"gender\"] = data_sim.gender\n",
    "temp_field[\"field_cd\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9e6a584c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9dc78f7ac8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# msk_gender = (similar[\"match\"] == 1)\n",
    "sb.catplot(y = \"field_cd\", data = temp_field, kind = \"count\").set_xlabels(\"Count\").set_ylabels(\"Field\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cdfcdb45",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of people who does not have job yet:  19\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of people who does not have job yet: \" , temp_field[\"field_cd\"].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "030a8dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "studies = temp_field[['gender', 'field_cd']].groupby(['field_cd', 'gender']).size().unstack().fillna(0)\n",
    "ax = studies.plot(kind='bar', figsize=(15,8), stacked=True)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), fontsize=12, rotation=45)\n",
    "\n",
    "ax.set_title('Field of study by gender', fontsize=18)\n",
    "ax.set_xlabel('',fontsize=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "942beacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since the data is generated in Columbia business school, where most of students major in Business and Law. So \n",
    "#it is natural to see: people surround you are all same interest in study with you, \"what is your tendency to date\n",
    "#someone is not in your circle\"\n",
    "from random import randint\n",
    "Business = [8]\n",
    "Law = [1, 13]\n",
    "Engineering = [5, 17]\n",
    "Service = [6, 11, 14, 15, 18, 4, 12]\n",
    "Academic = [2, 3, 7, 9, 10, 16]\n",
    "\n",
    "array_field = [Business, Law, Engineering, Service, Academic]\n",
    "temp_string_field = [\"Business\", \"Law\", \"Engineering\", \"Service\", \"Academic\"]\n",
    "\n",
    "for i in range(data_sim.shape[0]):\n",
    "    found = False\n",
    "    for j in range(5):\n",
    "        if(data_sim.at[data_sim.index[i], \"field_cd\"] in array_field[j]):\n",
    "            data_sim.at[data_sim.index[i],\"field_cd\"] = j\n",
    "            found = True\n",
    "            break\n",
    "    if not found:\n",
    "        data_sim.at[data_sim.index[i],\"field_cd\"] = randint(0,4)\n",
    "data_sim.astype({\"field_cd\": str})\n",
    "# print(data_sim.shape[0], count)\n",
    "def decode_num(x):\n",
    "    return temp_string_field[int(x)]\n",
    "data_sim[\"field_cd\"] = data_sim[\"field_cd\"].apply(decode_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3e7c8835",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9dc9509400>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.catplot(y = \"field_cd\", data = data_sim, kind = \"count\").set_xlabels(\"Count\").set_ylabels(\"Field\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a56733c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values after mereged and the shape of desired similar dataset\n",
      "(8299, 4)\n",
      "match             0\n",
      "gender            0\n",
      "SharedInterest    0\n",
      "SameField         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "temp = pd.Series(np.array([0 for i in range(data_sim.shape[0])]), index = data_sim.index)\n",
    "cnt = 0\n",
    "for i in range(data_sim.shape[0]):\n",
    "    own_field = data_sim[\"field_cd\"].iloc[i]\n",
    "    partner_in = []\n",
    "    partner_iid = data_sim.iloc[i][\"pid\"]\n",
    "    partner_id = data_sim.iloc[i][\"partner\"]\n",
    "    partner_index = 0\n",
    "    if(id_table.get((partner_iid, partner_id))):\n",
    "        partner_index = id_table.get((partner_iid, partner_id))\n",
    "        partner_field = data_sim.iloc[partner_index][\"field_cd\"]\n",
    "        if(partner_field == own_field):\n",
    "            temp.iloc[i] = 1\n",
    "similar = pd.concat([similar, temp.rename(\"SameField\")], axis=1)\n",
    "# Checking for the null value after concatenate: error might be \n",
    "# happen during the merge: missing index, missing value.\n",
    "print(\"Missing Values after mereged and the shape of desired similar dataset\")\n",
    "print(similar.shape)\n",
    "print(similar.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0e4f702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9dc303d780>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.catplot(y = \"SameField\", data = similar, kind = \"count\").set_xlabels(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b82ffac",
   "metadata": {},
   "source": [
    "## Extracting csv files for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93194d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "similar = similar.iloc[:, ::-1]\n",
    "print(similar.head())\n",
    "similar_male, similar_female = splitGender(similar)\n",
    "similar_male.to_csv(\"data/similar_male.csv\", index=False)\n",
    "similar_female.to_csv(\"data/similar_female.csv\", index=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Module1] *",
   "language": "python",
   "name": "conda-env-Module1-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}