Part 4-3. Cluster 기반 Mapping_독주.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd0c1058",
   "metadata": {},
   "source": [
    "# PART 4-3 < Cluster 기반 Mapping : 독주 >"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "193b072b",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c602e513",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "44955489",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_solo = pd.read_csv('data/concert_독주_code3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c17a04",
   "metadata": {},
   "source": [
    "# 분포 시각화 및 Mapping 을 위한 테이블 분할 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be97fb18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# little preprocessing \n",
    "\n",
    "data_solo = data_solo.iloc[:,1:]\n",
    "data_solo['열'] = data_solo['열'].str.replace('열', '')\n",
    "data_solo = data_solo[~data_solo['열'].isin(['box'])]\n",
    "data_solo['열'] = data_solo['열'].fillna(0)\n",
    "data_solo['열'] = data_solo['열'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abe162a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 박스 별 구분 \n",
    "\n",
    "solo_C = data_solo[data_solo['블록(박스)'] == 'C']\n",
    "solo_B = data_solo[data_solo['블록(박스)'] == 'B']\n",
    "solo_D = data_solo[data_solo['블록(박스)'] == 'D']\n",
    "solo_A = data_solo[data_solo['블록(박스)'] == 'A']\n",
    "solo_E = data_solo[data_solo['블록(박스)'] == 'E']\n",
    "solo_G = data_solo[data_solo['블록(박스)'] == 'G']\n",
    "solo_M = data_solo[data_solo['블록(박스)'] == 'M']\n",
    "solo_F = data_solo[data_solo['블록(박스)'] == 'F']\n",
    "solo_N = data_solo[data_solo['블록(박스)'] == 'N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "121429de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 해당 박스의 층별 구분 \n",
    "solo_C_f1 = solo_C[solo_C['층'] == 1]\n",
    "solo_C_f2 = solo_C[solo_C['층'] == 2]\n",
    "solo_C_f3 = solo_C[solo_C['층'] == 3]\n",
    "\n",
    "solo_B_f1 = solo_B[solo_B['층'] == 1]\n",
    "solo_B_f2 = solo_B[solo_B['층'] == 2]\n",
    "solo_B_f3 = solo_B[solo_B['층'] == 3]\n",
    "\n",
    "solo_D_f1 = solo_D[solo_D['층'] == 1]\n",
    "solo_D_f2 = solo_D[solo_D['층'] == 2]\n",
    "solo_D_f3 = solo_D[solo_D['층'] == 3]\n",
    "\n",
    "solo_A_f1 = solo_A[solo_A['층'] == 1]\n",
    "solo_A_f2 = solo_A[solo_A['층'] == 2]\n",
    "solo_A_f3 = solo_A[solo_A['층'] == 3]\n",
    "\n",
    "solo_E_f1 = solo_E[solo_E['층'] == 1]\n",
    "solo_E_f2 = solo_E[solo_E['층'] == 2]\n",
    "solo_E_f3 = solo_E[solo_E['층'] == 3]\n",
    "\n",
    "solo_G_f1 = solo_G[solo_G['층'] == 1]\n",
    "solo_G_f2 = solo_G[solo_G['층'] == 2]\n",
    "solo_G_f3 = solo_G[solo_G['층'] == 3]\n",
    "\n",
    "solo_M_f1 = solo_M[solo_M['층'] == 1]\n",
    "solo_M_f2 = solo_M[solo_M['층'] == 2]\n",
    "solo_M_f3 = solo_M[solo_M['층'] == 3]\n",
    "\n",
    "solo_F_f1 = solo_F[solo_F['층'] == 1]\n",
    "solo_F_f2 = solo_F[solo_F['층'] == 2]\n",
    "solo_F_f3 = solo_F[solo_F['층'] == 3]\n",
    "\n",
    "solo_N_f1 = solo_N[solo_N['층'] == 1]\n",
    "solo_N_f2 = solo_N[solo_N['층'] == 2]\n",
    "solo_N_f3 = solo_N[solo_N['층'] == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f9c08b",
   "metadata": {},
   "source": [
    "# 분포 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffef2113",
   "metadata": {},
   "source": [
    "**나누어진 테이블 기반 시각화**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d38095",
   "metadata": {},
   "source": [
    "- 클러스터별 '열' 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a63dc63",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing \n",
    "datas = [solo_C_f1,solo_C_f2,solo_C_f3,solo_B_f3,solo_B_f2,solo_B_f1,solo_A_f3,solo_A_f2,solo_A_f1,solo_D_f1,solo_D_f2,solo_D_f3,\n",
    "         solo_E_f1,solo_E_f2,solo_E_f3,solo_F_f1,solo_F_f2,solo_F_f3,solo_G_f1,solo_G_f2,solo_G_f3,solo_M_f1,solo_M_f2,solo_M_f3,\n",
    "         solo_N_f1,solo_N_f2,solo_N_f3]\n",
    "datas_title = ['solo_C_f1','solo_C_f2','solo_C_f3','solo_B_f3','solo_B_f2','solo_B_f1','solo_A_f3','solo_A_f2','solo_A_f1','solo_D_f1','solo_D_f2','solo_D_f3',\n",
    "         'solo_E_f1','solo_E_f2','solo_E_f3','solo_F_f1','solo_F_f2','solo_F_f3','solo_G_f1','solo_G_f2','solo_G_f3','solo_M_f1','solo_M_f2','solo_M_f3',\n",
    "         'solo_N_f1','solo_N_f2','solo_N_f3']\n",
    "\n",
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.boxplot(y='열',x='cluster',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)])\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f38687c",
   "metadata": {},
   "source": [
    "- 클러스터별 '행'(자리번호) 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55798526",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.countplot(x='자리번호',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)],palette='pastel')\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f28099",
   "metadata": {},
   "source": [
    "# Mapping \n",
    "- 위 분포 시각화를 통해 콘서트홀 좌석배치표에 맵핑\n",
    "- 해당 맵핑 결과를 기반으로 데이터 라벨링"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b698aef3",
   "metadata": {},
   "source": [
    "### 1층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f026ed26",
   "metadata": {},
   "source": [
    "- B ,C , D 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98517a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f1['labels'] = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51a6a1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'X' if (row['열'] <= 18 ) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'S' if (row['열'] <= 15 ) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'R' if (row['열'] <= 12 ) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ef36ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f1['labels'] = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1949eace",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'X' if (row['열'] <= 18 ) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'S' if (row['열'] <= 15 ) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'R' if (row['열'] <= 12 ) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93d75f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f1['labels'] = 'A'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "233bc539",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'X' if (row['열'] <= 18 ) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'S' if (row['열'] <= 15 ) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'R' if (row['열'] <= 12 ) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8f7475",
   "metadata": {},
   "source": [
    "- A 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5dc6fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f1['labels'] = 'B'\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'A' if (row['자리번호'] >= 4) else row['labels'], axis=1)\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'X' if (row['자리번호'] >= 6) else row['labels'], axis=1)\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'S' if (row['자리번호'] >= 9) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f341b7",
   "metadata": {},
   "source": [
    "- E 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e14e376",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f1['labels'] = 'B'\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'S' if (row['자리번호'] >= 4) else row['labels'], axis=1)\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'X' if (row['자리번호'] >= 6) else row['labels'], axis=1)\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'A' if (row['자리번호'] >= 9) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1c9e8b",
   "metadata": {},
   "source": [
    "### 2층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b710b9",
   "metadata": {},
   "source": [
    "- B,c,d 블럭 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ee3a7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f2['labels'] = 'B'\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'A' if (row['열'] < 6) else row['labels'], axis=1)\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'X' if (row['열'] < 5) else row['labels'], axis=1)\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'S' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4c35ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f2['labels'] = 'B'\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'A' if (row['열'] < 6) else row['labels'], axis=1)\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'X' if (row['열'] < 5) else row['labels'], axis=1)\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'S' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e603214",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f2['labels'] = 'B'\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'A' if (row['열'] < 6) else row['labels'], axis=1)\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'X' if (row['열'] < 5) else row['labels'], axis=1)\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'S' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7996c2fb",
   "metadata": {},
   "source": [
    "- A 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a98661c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f2['labels'] = 'B'\n",
    "solo_A_f2['labels'] = solo_A_f2.apply(lambda row: 'A' if (row['열'] <= 5) else row['labels'], axis=1)\n",
    "solo_A_f2['labels'] = solo_A_f2.apply(lambda row: 'X' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe733e95",
   "metadata": {},
   "source": [
    "- E 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d0418fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f2['labels'] = 'B'\n",
    "solo_E_f2['labels'] = solo_E_f2.apply(lambda row: 'A' if (row['열'] <= 5) else row['labels'], axis=1)\n",
    "solo_E_f2['labels'] = solo_E_f2.apply(lambda row: 'X' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d4fb30",
   "metadata": {},
   "source": [
    "### 3층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06450f7a",
   "metadata": {},
   "source": [
    "- b,c,d,e,f 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ce1a2284",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f3['labels'] = 'C'\n",
    "solo_B_f3['labels'] = solo_B_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0d6514b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f3['labels'] = 'C'\n",
    "solo_C_f3['labels'] = solo_C_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3b44811",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f3['labels'] = 'C'\n",
    "solo_D_f3['labels'] = solo_D_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9fde1df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f3['labels'] = 'C'\n",
    "solo_E_f3['labels'] = solo_E_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d301401d",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_F_f3['labels'] = 'C'\n",
    "solo_F_f3['labels'] = solo_F_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6673b555",
   "metadata": {},
   "source": [
    "- a, g 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99307645",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f3['labels'] = 'Y'\n",
    "solo_A_f3['labels'] = solo_A_f3.apply(lambda row: 'C' if (row['열'] <= 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6c2029e",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_G_f3['labels'] = 'Y'\n",
    "solo_G_f3['labels'] = solo_G_f3.apply(lambda row: 'C' if (row['열'] <= 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b9afcc",
   "metadata": {},
   "source": [
    "- m,n 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cdec505a",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_M_f3['labels'] = 'Y'\n",
    "solo_N_f3['labels'] = 'Y'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a613aa00",
   "metadata": {},
   "source": [
    "# 데이터 Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fda70c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff = pd.concat([solo_A_f1,solo_A_f2])\n",
    "ddff = pd.concat([ddff,solo_A_f3])\n",
    "ddff = pd.concat([ddff,solo_B_f1])\n",
    "ddff = pd.concat([ddff,solo_B_f2])\n",
    "ddff = pd.concat([ddff,solo_B_f3])\n",
    "ddff = pd.concat([ddff,solo_C_f1])\n",
    "ddff = pd.concat([ddff,solo_C_f2])\n",
    "ddff = pd.concat([ddff,solo_C_f3])\n",
    "ddff = pd.concat([ddff,solo_D_f1])\n",
    "ddff = pd.concat([ddff,solo_D_f2])\n",
    "ddff = pd.concat([ddff,solo_D_f3])\n",
    "ddff = pd.concat([ddff,solo_E_f1])\n",
    "ddff = pd.concat([ddff,solo_E_f2])\n",
    "ddff = pd.concat([ddff,solo_E_f3])\n",
    "ddff = pd.concat([ddff,solo_F_f3])\n",
    "ddff = pd.concat([ddff,solo_G_f3])\n",
    "ddff = pd.concat([ddff,solo_M_f3])\n",
    "ddff = pd.concat([ddff,solo_N_f3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "35d70bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff.to_csv('data/독주_라벨_code4.csv')"
   ]
  }
 ],
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