testdata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test data 만들기 #######################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>userid</th>\n",
       "      <th>productid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  productid  rating\n",
       "0       1          1       5\n",
       "1       1          2       2\n",
       "2       1          3       3\n",
       "3       1          4       2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#user1~30까지 모든 제품에 대한 평점 랜덤으로 생성\n",
    "ratings={'userid':[i for i in range(1,31) for j in range(1, 5902) ],\n",
    "    'productid':[i for j in range(1, 31) for i in range(1,5902)],\n",
    "     'rating':[np.random.randint(1,6) for i in range(1,177031)]\n",
    "      }\n",
    "\n",
    "ratings = pd.DataFrame(ratings)\n",
    "ratings.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 평점 데이터 저장\n",
    "ratings.to_excel('./datasets/ratings.xlsx', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 저장한 학습data에서 testdata 추출\n",
    "ratings = pd.read_excel(\"./datasets/ratings.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ratings데이터에서 랜덤으로 테스트 데이터 10% 추출 \n",
    "index = [np.random.randint(0,177030) for i in range(0,53109)]\n",
    "ratings = ratings.iloc[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>userid</th>\n",
       "      <th>productid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>2410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>4564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>214</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>2845</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  productid  rating\n",
       "0      12       2410       1\n",
       "1       8       4564       1\n",
       "2       6        214       4\n",
       "3      25       2845       5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings.reset_index(drop=True)\n",
    "ratings.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings.to_excel('./datasets/test_data3.xlsx', index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#복합기 크롤링 데이터 수정###########"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"./datasets/복합기 크롤링 중복제거.xlsx\")\n",
    "data=data.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#나누어져 있는 스펙 목록 컬럼을 keyword로 합침\n",
    "cols = ['Unnamed: ' + str(i) for i in range(3,30)]\n",
    "data['keyword'] = data[cols].apply(lambda row: ''.join(row.values.astype(str)), axis=1)\n",
    "colss=['스펙 목록', 'keyword']\n",
    "data['keyword'] = data[colss].apply(lambda row: ''.join(row.values.astype(str)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 상품명, 가격, 스펙목록, keyword 남기고 컬럼 제거\n",
    "data = data.drop(cols, axis=1)\n",
    "data = data.drop('스펙 목록', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>상품명</th>\n",
       "      <th>가격</th>\n",
       "      <th>keyword</th>\n",
       "      <th>productid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ApeosPort 2560</td>\n",
       "      <td>2125000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ApeosPort 3060</td>\n",
       "      <td>2200000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ApeosPort 3560</td>\n",
       "      <td>2528000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ApeosPort C2060</td>\n",
       "      <td>2600000</td>\n",
       "      <td>컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               상품명       가격  \\\n",
       "0   ApeosPort 2560  2125000   \n",
       "1   ApeosPort 3060  2200000   \n",
       "2   ApeosPort 3560  2528000   \n",
       "3  ApeosPort C2060  2600000   \n",
       "\n",
       "                                             keyword  productid  \n",
       "0  흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          1  \n",
       "1  흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          2  \n",
       "2  흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...          3  \n",
       "3  컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...          4  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상품명 번호(productid) 컬럼 생성\n",
    "data['productid'] = [i for i in range(1,5902)]\n",
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_excel('./datasets/printer_data.xlsx', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 사용자 data추가 ###############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>userid</th>\n",
       "      <th>업종</th>\n",
       "      <th>용지사용량</th>\n",
       "      <th>내구성</th>\n",
       "      <th>근무자 수</th>\n",
       "      <th>가격</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>건설</td>\n",
       "      <td>1503</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>14000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>병원</td>\n",
       "      <td>2036</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>7000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>법</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>14000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>병원</td>\n",
       "      <td>253</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  업종  용지사용량  내구성  근무자 수        가격\n",
       "0       1  건설   1503    1     33  14000000\n",
       "1       2  병원   2036    1     41   7000000\n",
       "2       3   법    123    1     85  14000000\n",
       "3       4  병원    253    3     43  15000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job = ['법','의류','교육','서비스','사무','건설','은행','병원','행정','기타']\n",
    "user={'userid':[i for i in range(1,31)],\n",
    "     '업종':[job[np.random.randint(0,10)] for i in range(1,31)],\n",
    "     '용지사용량':[np.random.randint(100,3001) for i in range(1,31)],\n",
    "      '내구성': [np.random.randint(1,5) for i in range(1,31)],\n",
    "      '근무자 수' : [np.random.randint(1,101) for i in range(1,31)],\n",
    "      '가격' : [np.random.randint(0,20)*1000000 for i in range(1,31)]\n",
    "      }\n",
    "user = pd.DataFrame(user)\n",
    "user.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "user.to_excel('./datasets/user_data.xlsx', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-2c6f8266d9c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mkeyword\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'keyword'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkeyword\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'기본 급지용량'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[0mpaper\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'-1'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mkeyword_extract\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeyword\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkeyword\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'기본 급지용량'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mkeyword\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'기본 급지용량'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# 복합기 데이터에 용지 사용량 추가 #####################\n",
    "paper=''\n",
    "for i in range(0, 5901):\n",
    "    keyword = data.loc[i]['keyword']\n",
    "    if keyword.find('기본 급지용량') == -1:\n",
    "        paper[i]='-1'\n",
    "    else:\n",
    "        keyword_extract = keyword[keyword.find('기본 급지용량'):keyword.find('기본 급지용량')+15]\n",
    "        paper[i]= keyword_extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>상품명</th>\n",
       "      <th>가격</th>\n",
       "      <th>keyword</th>\n",
       "      <th>productid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ApeosPort 2560</td>\n",
       "      <td>2125000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ApeosPort 3060</td>\n",
       "      <td>2200000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ApeosPort 3560</td>\n",
       "      <td>2528000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ApeosPort C2060</td>\n",
       "      <td>2600000</td>\n",
       "      <td>컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ApeosPort C2560</td>\n",
       "      <td>2778170</td>\n",
       "      <td>컬러 디지털 복합기  인쇄  복사  스캔  팩스  컬러 속도: 23ppm  흑백 속...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               상품명       가격  \\\n",
       "0   ApeosPort 2560  2125000   \n",
       "1   ApeosPort 3060  2200000   \n",
       "2   ApeosPort 3560  2528000   \n",
       "3  ApeosPort C2060  2600000   \n",
       "4  ApeosPort C2560  2778170   \n",
       "\n",
       "                                             keyword  productid  \n",
       "0  흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          1  \n",
       "1  흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          2  \n",
       "2  흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...          3  \n",
       "3  컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...          4  \n",
       "4  컬러 디지털 복합기  인쇄  복사  스캔  팩스  컬러 속도: 23ppm  흑백 속...          5  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datata = pd.read_excel(\"./datasets/printer_data.xlsx\")\n",
    "datata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>상품명</th>\n",
       "      <th>가격</th>\n",
       "      <th>keyword</th>\n",
       "      <th>productid</th>\n",
       "      <th>기본 급지용량</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ApeosPort 2560</td>\n",
       "      <td>2125000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ApeosPort 3060</td>\n",
       "      <td>2200000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ApeosPort 3560</td>\n",
       "      <td>2528000</td>\n",
       "      <td>흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ApeosPort C2060</td>\n",
       "      <td>2600000</td>\n",
       "      <td>컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...</td>\n",
       "      <td>4</td>\n",
       "      <td>1,200매</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ApeosPort C2560</td>\n",
       "      <td>2778170</td>\n",
       "      <td>컬러 디지털 복합기  인쇄  복사  스캔  팩스  컬러 속도: 23ppm  흑백 속...</td>\n",
       "      <td>5</td>\n",
       "      <td>1,350매</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5896</th>\n",
       "      <td>코니카미놀타 bizhub C451</td>\n",
       "      <td>0</td>\n",
       "      <td>컬러 디지털 복합기  레이저  컬러 출력  복사  스캔  컬러 속도: 21ppm  ...</td>\n",
       "      <td>5897</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5897</th>\n",
       "      <td>코니카미놀타 C022DN</td>\n",
       "      <td>0</td>\n",
       "      <td>컬러 디지털 복합기  잉크젯  컬러 출력  복사  스캔  컬러 속도(ISO): 6....</td>\n",
       "      <td>5898</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5898</th>\n",
       "      <td>코니카미놀타 C028DN</td>\n",
       "      <td>0</td>\n",
       "      <td>흑백 디지털 복합기  레이저젯  인쇄  컬러 속도: 38ppm  흑백 속도: 38p...</td>\n",
       "      <td>5899</td>\n",
       "      <td>650매</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5899</th>\n",
       "      <td>코니카미놀타 C036DN</td>\n",
       "      <td>0</td>\n",
       "      <td>흑백 디지털 복합기  레이저  컬러 출력  복사  스캔  팩스  컬러 속도: 4pp...</td>\n",
       "      <td>5900</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5900</th>\n",
       "      <td>코니카미놀타 MagiColor 4690MF</td>\n",
       "      <td>0</td>\n",
       "      <td>복합기  잉크젯  컬러 출력  복사  스캔  컬러 속도: 16ppm  흑백 속도: ...</td>\n",
       "      <td>5901</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5901 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          상품명       가격  \\\n",
       "0              ApeosPort 2560  2125000   \n",
       "1              ApeosPort 3060  2200000   \n",
       "2              ApeosPort 3560  2528000   \n",
       "3             ApeosPort C2060  2600000   \n",
       "4             ApeosPort C2560  2778170   \n",
       "...                       ...      ...   \n",
       "5896       코니카미놀타 bizhub C451        0   \n",
       "5897            코니카미놀타 C022DN        0   \n",
       "5898            코니카미놀타 C028DN        0   \n",
       "5899            코니카미놀타 C036DN        0   \n",
       "5900  코니카미놀타 MagiColor 4690MF        0   \n",
       "\n",
       "                                                keyword  productid 기본 급지용량  \n",
       "0     흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          1      -1  \n",
       "1     흑백 디지털 복합기  잉크젯  포토프린터  컬러 출력  복사  스캔  팩스  인쇄 ...          2      -1  \n",
       "2     흑백 디지털 복합기  잉크젯  컬러 출력  복사  스캔  인쇄 해상도: 4,800d...          3      -1  \n",
       "3     컬러 디지털 복합기  인쇄  복사  스캔  컬러 속도: 23ppm  흑백 속도: 2...          4  1,200매  \n",
       "4     컬러 디지털 복합기  인쇄  복사  스캔  팩스  컬러 속도: 23ppm  흑백 속...          5  1,350매  \n",
       "...                                                 ...        ...     ...  \n",
       "5896  컬러 디지털 복합기  레이저  컬러 출력  복사  스캔  컬러 속도: 21ppm  ...       5897      -1  \n",
       "5897  컬러 디지털 복합기  잉크젯  컬러 출력  복사  스캔  컬러 속도(ISO): 6....       5898      -1  \n",
       "5898  흑백 디지털 복합기  레이저젯  인쇄  컬러 속도: 38ppm  흑백 속도: 38p...       5899  650매    \n",
       "5899  흑백 디지털 복합기  레이저  컬러 출력  복사  스캔  팩스  컬러 속도: 4pp...       5900      -1  \n",
       "5900  복합기  잉크젯  컬러 출력  복사  스캔  컬러 속도: 16ppm  흑백 속도: ...       5901      -1  \n",
       "\n",
       "[5901 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(datata.loc[3]['keyword'])\n",
    "datata = pd.DataFrame(datata)\n",
    "datata['기본 급지용량'] = str(-1)\n",
    "aa = '기본 급지용량:'\n",
    "\n",
    "#print(datata.loc[2]['기본 급지용량'])\n",
    "#datata.at[2,'기본 급지용량'] = 99\n",
    "#print(datata.loc[2]['기본 급지용량'])\n",
    "\n",
    "\n",
    "for i in range(0,5901):\n",
    "\n",
    "    if( aa in datata.loc[i]['keyword']):\n",
    "        start = datata.loc[i]['keyword'].find(aa)\n",
    "        #print(datata.loc[i]['기본 급지용량'])\n",
    "        datata.at[i,'기본 급지용량'] = datata.loc[i]['keyword'][start+9:start+15]\n",
    "        #print(datata.loc[i]['기본 급지용량'])\n",
    "\n",
    "datata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## test ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"./datasets/printer_data.xlsx\")\n",
    "data2 = pd.read_excel(\"./datasets/user_data.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = CountVectorizer(ngram_range=(1,3))\n",
    "vector_keyword = vector.fit_transform(data['업종'])\n",
    "vector_keyword2 = vector.fit_transform(data2['업종'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Incompatible dimension for X and Y matrices: X.shape[1] == 9 while Y.shape[1] == 7",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-4e41648461b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mkeyword_c_sim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcosine_similarity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvector_keyword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvector_keyword2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mkeyword_c_sim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mcosine_similarity\u001b[1;34m(X, Y, dense_output)\u001b[0m\n\u001b[0;32m   1177\u001b[0m     \u001b[1;31m# to avoid recursive import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1179\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_pairwise_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1181\u001b[0m     \u001b[0mX_normalized\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mcheck_pairwise_arrays\u001b[1;34m(X, Y, precomputed, dtype, accept_sparse, force_all_finite, copy)\u001b[0m\n\u001b[0;32m    157\u001b[0m                              (X.shape[0], X.shape[1], Y.shape[0]))\n\u001b[0;32m    158\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         raise ValueError(\"Incompatible dimension for X and Y matrices: \"\n\u001b[0m\u001b[0;32m    160\u001b[0m                          \"X.shape[1] == %d while Y.shape[1] == %d\" % (\n\u001b[0;32m    161\u001b[0m                              X.shape[1], Y.shape[1]))\n",
      "\u001b[1;31mValueError\u001b[0m: Incompatible dimension for X and Y matrices: X.shape[1] == 9 while Y.shape[1] == 7"
     ]
    }
   ],
   "source": [
    "keyword_c_sim = cosine_similarity(vector_keyword, vector_keyword2).argsort()[:, ::-1]\n",
    "keyword_c_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5901, 5901)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_c_sim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got scalar array instead:\narray=123.0.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-f77f5c1fc977>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcosine_similarity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m123\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mcosine_similarity\u001b[1;34m(X, Y, dense_output)\u001b[0m\n\u001b[0;32m   1177\u001b[0m     \u001b[1;31m# to avoid recursive import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1179\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_pairwise_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1181\u001b[0m     \u001b[0mX_normalized\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py\u001b[0m in \u001b[0;36mcheck_pairwise_arrays\u001b[1;34m(X, Y, precomputed, dtype, accept_sparse, force_all_finite, copy)\u001b[0m\n\u001b[0;32m    143\u001b[0m                             estimator=estimator)\n\u001b[0;32m    144\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m         X = check_array(X, accept_sparse=accept_sparse, dtype=dtype,\n\u001b[0m\u001b[0;32m    146\u001b[0m                         \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_all_finite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m                         estimator=estimator)\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    610\u001b[0m             \u001b[1;31m# If input is scalar raise error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 612\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m    613\u001b[0m                     \u001b[1;34m\"Expected 2D array, got scalar array instead:\\narray={}.\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    614\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=123.0.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "cosine_similarity(123, 0).argsort()[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}