13.9.3.연관규칙과 협업 필터링 (추천 모델).ipynb

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   "outputs": [],
   "source": [
    "# 연관규칙 실습용 필요한 패키지 설치\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09aceaa8",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_cd</th>\n",
       "      <th>std_dt</th>\n",
       "      <th>prdct_cd</th>\n",
       "      <th>prdct_nm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C617077280704</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>A10001</td>\n",
       "      <td>tropical fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C617077280704</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>A10002</td>\n",
       "      <td>whole milk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C617077280704</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>A10003</td>\n",
       "      <td>pip fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C617077280704</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>A10004</td>\n",
       "      <td>other vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C617077280704</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>A10005</td>\n",
       "      <td>cream</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cust_cd      std_dt prdct_cd          prdct_nm\n",
       "0  C617077280704  2021-06-19   A10001    tropical fruit\n",
       "1  C617077280704  2021-06-19   A10002        whole milk\n",
       "2  C617077280704  2021-06-19   A10003         pip fruit\n",
       "3  C617077280704  2021-06-19   A10004  other vegetables\n",
       "4  C617077280704  2021-06-19   A10005             cream"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연관규칙 실습용 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/sewonghwang/market-basket\n",
    "df = pd.read_csv(\"datasets/market_basket.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52048023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [beef, herbs, tropical fruit, whole milk, chic...\n",
       "1    [sugar, packaged fruit/vegetables, sausage, sp...\n",
       "2    [berries, tropical fruit, fruit/vegetable juic...\n",
       "3    [yogurt, beef, cream, herbs, chicken, bottled ...\n",
       "4    [berries, beef, yogurt, specialty bar, bottled...\n",
       "Name: prdct_nm, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apriori 모델 적용을 위한 품목 리스트 가공\n",
    "\n",
    "itemset = df.drop_duplicates(\n",
    "    ['cust_cd', 'std_dt', 'prdct_nm']).groupby(\n",
    "    ['cust_cd','std_dt'])['prdct_nm'].apply(list)\n",
    "\n",
    "itemset= pd.DataFrame(itemset).reset_index().drop(\n",
    "    ['cust_cd', 'std_dt'], axis='columns')\n",
    "\n",
    "itemset = itemset.squeeze()\n",
    "\n",
    "itemset.head()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "0f7da717",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beef</th>\n",
       "      <th>berries</th>\n",
       "      <th>beverages</th>\n",
       "      <th>bottled beer</th>\n",
       "      <th>bottled water</th>\n",
       "      <th>brown bread</th>\n",
       "      <th>butter</th>\n",
       "      <th>butter milk</th>\n",
       "      <th>canned beer</th>\n",
       "      <th>chicken</th>\n",
       "      <th>...</th>\n",
       "      <th>sparkling wine</th>\n",
       "      <th>specialty bar</th>\n",
       "      <th>specialty chocolate</th>\n",
       "      <th>sugar</th>\n",
       "      <th>syrup</th>\n",
       "      <th>tropical fruit</th>\n",
       "      <th>turkey</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole milk</th>\n",
       "      <th>yogurt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    beef  berries  beverages  bottled beer  bottled water  brown bread  \\\n",
       "0   True    False      False         False          False        False   \n",
       "1  False    False      False         False          False        False   \n",
       "2  False     True      False         False          False        False   \n",
       "3   True    False      False         False           True        False   \n",
       "4   True     True      False         False           True        False   \n",
       "\n",
       "   butter  butter milk  canned beer  chicken  ...  sparkling wine  \\\n",
       "0   False        False        False     True  ...           False   \n",
       "1   False        False        False    False  ...           False   \n",
       "2   False        False        False     True  ...           False   \n",
       "3   False        False        False     True  ...           False   \n",
       "4   False        False        False     True  ...           False   \n",
       "\n",
       "   specialty bar  specialty chocolate  sugar  syrup  tropical fruit  turkey  \\\n",
       "0          False                False  False  False            True   False   \n",
       "1          False                 True   True  False            True   False   \n",
       "2          False                False  False  False            True   False   \n",
       "3          False                False  False  False           False   False   \n",
       "4           True                False  False  False            True   False   \n",
       "\n",
       "   white wine  whole milk  yogurt  \n",
       "0       False        True   False  \n",
       "1       False       False   False  \n",
       "2       False       False   False  \n",
       "3       False       False    True  \n",
       "4       False       False    True  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# apriori 모델 적용을 위한 장바구니 - 품목 더미 가공\n",
    "\n",
    "encoder = TransactionEncoder()\n",
    "encoder_T = encoder.fit(itemset).transform(itemset)\n",
    "\n",
    "# 데이터프레임으로 변경\n",
    "itemset_matrix = pd.DataFrame(encoder_T, columns=encoder.columns_) \n",
    "\n",
    "itemset_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e464ca3",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.166612</td>\n",
       "      <td>(beef)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.105074</td>\n",
       "      <td>(berries)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.017010</td>\n",
       "      <td>(beverages)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.025754</td>\n",
       "      <td>(bottled beer)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.095191</td>\n",
       "      <td>(bottled water)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    support         itemsets\n",
       "0  0.166612           (beef)\n",
       "1  0.105074        (berries)\n",
       "2  0.017010      (beverages)\n",
       "3  0.025754   (bottled beer)\n",
       "4  0.095191  (bottled water)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지지도 0.1 기준으로 apriori 적용\n",
    "\n",
    "itemset_apriori = apriori(itemset_matrix, min_support=0.01, use_colnames=True)\n",
    "itemset_apriori.head()"
   ]
  },
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(beef, ham)</td>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>0.028179</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.014585</td>\n",
       "      <td>0.517601</td>\n",
       "      <td>5.437508</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>1.875645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>(beef, ham)</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.028179</td>\n",
       "      <td>0.014585</td>\n",
       "      <td>0.153223</td>\n",
       "      <td>5.437508</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>1.147670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(beef, yogurt)</td>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>0.023844</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.502311</td>\n",
       "      <td>5.276885</td>\n",
       "      <td>0.009707</td>\n",
       "      <td>1.818022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>(beef, yogurt)</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.023844</td>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>5.276885</td>\n",
       "      <td>0.009707</td>\n",
       "      <td>1.116654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(pastry, beef, cream)</td>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>0.030163</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.493301</td>\n",
       "      <td>5.182229</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>1.785693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(pastry, beef)</td>\n",
       "      <td>(bottled water, cream)</td>\n",
       "      <td>0.046916</td>\n",
       "      <td>0.047797</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.317150</td>\n",
       "      <td>6.635276</td>\n",
       "      <td>0.012637</td>\n",
       "      <td>1.394453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(pastry, bottled water)</td>\n",
       "      <td>(beef, cream)</td>\n",
       "      <td>0.037474</td>\n",
       "      <td>0.069878</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>5.682200</td>\n",
       "      <td>0.012261</td>\n",
       "      <td>1.542642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(beef, cream)</td>\n",
       "      <td>(pastry, bottled water)</td>\n",
       "      <td>0.069878</td>\n",
       "      <td>0.037474</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.212934</td>\n",
       "      <td>5.682200</td>\n",
       "      <td>0.012261</td>\n",
       "      <td>1.222929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(bottled water, cream)</td>\n",
       "      <td>(pastry, beef)</td>\n",
       "      <td>0.047797</td>\n",
       "      <td>0.046916</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.311299</td>\n",
       "      <td>6.635276</td>\n",
       "      <td>0.012637</td>\n",
       "      <td>1.383887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(bottled water)</td>\n",
       "      <td>(pastry, beef, cream)</td>\n",
       "      <td>0.095191</td>\n",
       "      <td>0.030163</td>\n",
       "      <td>0.014879</td>\n",
       "      <td>0.156310</td>\n",
       "      <td>5.182229</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>1.149519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               antecedents              consequents  antecedent support  \\\n",
       "0              (beef, ham)          (bottled water)            0.028179   \n",
       "1          (bottled water)              (beef, ham)            0.095191   \n",
       "2           (beef, yogurt)          (bottled water)            0.023844   \n",
       "3          (bottled water)           (beef, yogurt)            0.095191   \n",
       "4    (pastry, beef, cream)          (bottled water)            0.030163   \n",
       "5           (pastry, beef)   (bottled water, cream)            0.046916   \n",
       "6  (pastry, bottled water)            (beef, cream)            0.037474   \n",
       "7            (beef, cream)  (pastry, bottled water)            0.069878   \n",
       "8   (bottled water, cream)           (pastry, beef)            0.047797   \n",
       "9          (bottled water)    (pastry, beef, cream)            0.095191   \n",
       "\n",
       "   consequent support   support  confidence      lift  leverage  conviction  \n",
       "0            0.095191  0.014585    0.517601  5.437508  0.011903    1.875645  \n",
       "1            0.028179  0.014585    0.153223  5.437508  0.011903    1.147670  \n",
       "2            0.095191  0.011977    0.502311  5.276885  0.009707    1.818022  \n",
       "3            0.023844  0.011977    0.125820  5.276885  0.009707    1.116654  \n",
       "4            0.095191  0.014879    0.493301  5.182229  0.012008    1.785693  \n",
       "5            0.047797  0.014879    0.317150  6.635276  0.012637    1.394453  \n",
       "6            0.069878  0.014879    0.397059  5.682200  0.012261    1.542642  \n",
       "7            0.037474  0.014879    0.212934  5.682200  0.012261    1.222929  \n",
       "8            0.046916  0.014879    0.311299  6.635276  0.012637    1.383887  \n",
       "9            0.030163  0.014879    0.156310  5.182229  0.012008    1.149519  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 향상도 5 이상 상품 조합 추출\n",
    "\n",
    "association_rules(itemset_apriori, metric=\"lift\", min_threshold=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4210d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#################### 협업 필터링 #####################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdc4cab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 협업 필터링 실습용 필요한 패키지 설치\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f45618fb",
   "metadata": {},
   "outputs": [
    {
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       "   userId  movieId  rating   timestamp\n",
       "0       1       16     4.0  1217897793\n",
       "1       1       24     1.5  1217895807\n",
       "2       1       32     4.0  1217896246\n",
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 협업 필터링 실습용 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/ayushimishra2809/movielens-dataset\n",
    "df_movies = pd.read_csv(\"datasets/movies.csv\")\n",
    "df_ratings = pd.read_csv(\"datasets/ratings.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "181417f6",
   "metadata": {},
   "outputs": [
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       "userId                                   1    2    3    4    5    6    7    \\\n",
       "title                                                                        \n",
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       "userId                                   8    9    10   ...  659  660  661  \\\n",
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       "\n",
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       "title                                                                       \n",
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       "\n",
       "[5 rows x 668 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "# 고객, 영화 유사도 측정을 위한 전치 데이터셋 생성\n",
    "\n",
    "# ratings 데이터와 movies 데이터 결합\n",
    "df_merge = pd.merge(df_ratings, df_movies, on=\"movieId\")\n",
    "\n",
    "# 고객-아이템 평점 행렬 생성\n",
    "df_merge_pivot = df_merge.pivot_table(\"rating\", \"userId\", \"title\")\n",
    "\n",
    "# 결측 0으로 변환\n",
    "df_merge_pivot_null = df_merge_pivot.fillna(0)\n",
    "\n",
    "# 아이템-사용자 평점 행렬로 전치\n",
    "df_merge_pivot_T = df_merge_pivot_null.T\n",
    "df_merge_pivot_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ef06045",
   "metadata": {},
   "outputs": [
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       "title                                    '71 (2014)  \\\n",
       "title                                                 \n",
       "'71 (2014)                                      1.0   \n",
       "'Hellboy': The Seeds of Creation (2004)         0.0   \n",
       "'Round Midnight (1986)                          0.0   \n",
       "'Til There Was You (1997)                       0.0   \n",
       "'burbs, The (1989)                              0.0   \n",
       "\n",
       "title                                    'Hellboy': The Seeds of Creation (2004)  \\\n",
       "title                                                                              \n",
       "'71 (2014)                                                                   0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                                      1.0   \n",
       "'Round Midnight (1986)                                                       0.0   \n",
       "'Til There Was You (1997)                                                    0.0   \n",
       "'burbs, The (1989)                                                           0.0   \n",
       "\n",
       "title                                    'Round Midnight (1986)  \\\n",
       "title                                                             \n",
       "'71 (2014)                                                  0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                     0.0   \n",
       "'Round Midnight (1986)                                      1.0   \n",
       "'Til There Was You (1997)                                   0.0   \n",
       "'burbs, The (1989)                                          0.0   \n",
       "\n",
       "title                                    'Til There Was You (1997)  \\\n",
       "title                                                                \n",
       "'71 (2014)                                                     0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                        0.0   \n",
       "'Round Midnight (1986)                                         0.0   \n",
       "'Til There Was You (1997)                                      1.0   \n",
       "'burbs, The (1989)                                             0.0   \n",
       "\n",
       "title                                    'burbs, The (1989)  \\\n",
       "title                                                         \n",
       "'71 (2014)                                              0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                 0.0   \n",
       "'Round Midnight (1986)                                  0.0   \n",
       "'Til There Was You (1997)                               0.0   \n",
       "'burbs, The (1989)                                      1.0   \n",
       "\n",
       "title                                    'night Mother (1986)  \\\n",
       "title                                                           \n",
       "'71 (2014)                                                0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                   0.0   \n",
       "'Round Midnight (1986)                                    0.0   \n",
       "'Til There Was You (1997)                                 0.0   \n",
       "'burbs, The (1989)                                        0.0   \n",
       "\n",
       "title                                    (500) Days of Summer (2009)  \\\n",
       "title                                                                  \n",
       "'71 (2014)                                                  0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)                     0.000000   \n",
       "'Round Midnight (1986)                                      0.081094   \n",
       "'Til There Was You (1997)                                   0.000000   \n",
       "'burbs, The (1989)                                          0.031610   \n",
       "\n",
       "title                                    *batteries not included (1987)  \\\n",
       "title                                                                     \n",
       "'71 (2014)                                                     0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)                        0.000000   \n",
       "'Round Midnight (1986)                                         0.000000   \n",
       "'Til There Was You (1997)                                      0.000000   \n",
       "'burbs, The (1989)                                             0.231897   \n",
       "\n",
       "title                                    ...And Justice for All (1979)  \\\n",
       "title                                                                    \n",
       "'71 (2014)                                                    0.342682   \n",
       "'Hellboy': The Seeds of Creation (2004)                       0.000000   \n",
       "'Round Midnight (1986)                                        0.257012   \n",
       "'Til There Was You (1997)                                     0.000000   \n",
       "'burbs, The (1989)                                            0.100923   \n",
       "\n",
       "title                                    10 (1979)  ...  [REC] (2007)  \\\n",
       "title                                               ...                 \n",
       "'71 (2014)                                0.000000  ...      0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)   0.000000  ...      0.000000   \n",
       "'Round Midnight (1986)                    0.680414  ...      0.000000   \n",
       "'Til There Was You (1997)                 0.000000  ...      0.000000   \n",
       "'burbs, The (1989)                        0.000000  ...      0.057358   \n",
       "\n",
       "title                                    [REC]² (2009)  \\\n",
       "title                                                    \n",
       "'71 (2014)                                    0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)       0.000000   \n",
       "'Round Midnight (1986)                        0.227429   \n",
       "'Til There Was You (1997)                     0.000000   \n",
       "'burbs, The (1989)                            0.000000   \n",
       "\n",
       "title                                    [REC]³ 3 Génesis (2012)  \\\n",
       "title                                                              \n",
       "'71 (2014)                                              0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)                 0.000000   \n",
       "'Round Midnight (1986)                                  0.141421   \n",
       "'Til There Was You (1997)                               0.000000   \n",
       "'burbs, The (1989)                                      0.000000   \n",
       "\n",
       "title                                    a/k/a Tommy Chong (2005)  \\\n",
       "title                                                               \n",
       "'71 (2014)                                                    0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                       0.0   \n",
       "'Round Midnight (1986)                                        0.0   \n",
       "'Til There Was You (1997)                                     0.0   \n",
       "'burbs, The (1989)                                            0.0   \n",
       "\n",
       "title                                    eXistenZ (1999)  \\\n",
       "title                                                      \n",
       "'71 (2014)                                      0.000000   \n",
       "'Hellboy': The Seeds of Creation (2004)         0.000000   \n",
       "'Round Midnight (1986)                          0.100219   \n",
       "'Til There Was You (1997)                       0.000000   \n",
       "'burbs, The (1989)                              0.212684   \n",
       "\n",
       "title                                    loudQUIETloud: A Film About the Pixies (2006)  \\\n",
       "title                                                                                    \n",
       "'71 (2014)                                                                         0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                                            0.0   \n",
       "'Round Midnight (1986)                                                             0.0   \n",
       "'Til There Was You (1997)                                                          0.0   \n",
       "'burbs, The (1989)                                                                 0.0   \n",
       "\n",
       "title                                    xXx (2002)  \\\n",
       "title                                                 \n",
       "'71 (2014)                                 0.158272   \n",
       "'Hellboy': The Seeds of Creation (2004)    0.000000   \n",
       "'Round Midnight (1986)                     0.221581   \n",
       "'Til There Was You (1997)                  0.000000   \n",
       "'burbs, The (1989)                         0.104192   \n",
       "\n",
       "title                                    xXx: State of the Union (2005)  \\\n",
       "title                                                                     \n",
       "'71 (2014)                                                          0.0   \n",
       "'Hellboy': The Seeds of Creation (2004)                             0.0   \n",
       "'Round Midnight (1986)                                              0.0   \n",
       "'Til There Was You (1997)                                           0.0   \n",
       "'burbs, The (1989)                                                  0.0   \n",
       "\n",
       "title                                    ¡Three Amigos! (1986)  \\\n",
       "title                                                            \n",
       "'71 (2014)                                            0.098324   \n",
       "'Hellboy': The Seeds of Creation (2004)               0.147486   \n",
       "'Round Midnight (1986)                                0.098324   \n",
       "'Til There Was You (1997)                             0.000000   \n",
       "'burbs, The (1989)                                    0.161820   \n",
       "\n",
       "title                                    À nous la liberté (Freedom for Us) (1931)  \n",
       "title                                                                               \n",
       "'71 (2014)                                                                     0.0  \n",
       "'Hellboy': The Seeds of Creation (2004)                                        0.0  \n",
       "'Round Midnight (1986)                                                         1.0  \n",
       "'Til There Was You (1997)                                                      0.0  \n",
       "'burbs, The (1989)                                                             0.0  \n",
       "\n",
       "[5 rows x 10323 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 영화 유사도 행렬 생성\n",
    "item_sim = cosine_similarity(df_merge_pivot_T)\n",
    "\n",
    "# 데이터 프레임 형태 변환\n",
    "item_sim_df = pd.DataFrame(item_sim, index=df_merge_pivot_T.index,\n",
    "                           columns=df_merge_pivot_T.index)\n",
    "\n",
    "item_sim_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acefeb2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Scott Pilgrim vs. the World (2010)    0.502121\n",
       "Up in the Air (2009)                  0.498354\n",
       "Social Network, The (2010)            0.497004\n",
       "Forgetting Sarah Marshall (2008)      0.472271\n",
       "Shutter Island (2010)                 0.468202\n",
       "Name: (500) Days of Summer (2009), dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 500일의 썸머와 유사도가 높은 상위 5개 영화 추출\n",
    "\n",
    "item_sim_df[\"(500) Days of Summer (2009)\"].sort_values(ascending=False)[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f778fe1",
   "metadata": {},
   "outputs": [
    {
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       "userId                                                                         \n",
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       "\n",
       "userId       8         9         10   ...       659       660       661  \\\n",
       "userId                                ...                                 \n",
       "1       0.097434  0.239189  0.026663  ...  0.291162  0.144741  0.106583   \n",
       "2       0.471918  0.194232  0.000000  ...  0.068325  0.000000  0.477330   \n",
       "3       0.066620  0.459703  0.068454  ...  0.152078  0.301021  0.081626   \n",
       "4       0.024420  0.050572  0.000000  ...  0.055860  0.024329  0.040467   \n",
       "5       0.041536  0.023168  0.011915  ...  0.058450  0.007315  0.024708   \n",
       "\n",
       "userId       662       663       664       665       666       667       668  \n",
       "userId                                                                        \n",
       "1       0.091049  0.236805  0.154519  0.245071  0.238660  0.278217  0.153493  \n",
       "2       0.146887  0.163553  0.061737  0.050948  0.051423  0.035907  0.064822  \n",
       "3       0.098949  0.310234  0.079452  0.092821  0.080940  0.158943  0.109658  \n",
       "4       0.108881  0.076241  0.014011  0.042643  0.174275  0.061677  0.157809  \n",
       "5       0.038163  0.053085  0.048993  0.055431  0.026053  0.086667  0.068281  \n",
       "\n",
       "[5 rows x 668 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 고객 유사도 행렬 생성\n",
    "user_sim = cosine_similarity(df_merge_pivot_null)\n",
    "\n",
    "# 데이터 프레임 형태 변환\n",
    "user_sim_df = pd.DataFrame(user_sim, index=df_merge_pivot_null.index,\n",
    "                           columns=df_merge_pivot_null.index)\n",
    "\n",
    "user_sim_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb4ef822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "403    0.432287\n",
       "358    0.414600\n",
       "228    0.396949\n",
       "328    0.391268\n",
       "590    0.387817\n",
       "Name: 7, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7번 고객과 유사도가 높은 상위 5명 추출\n",
    "\n",
    "user_sim_df[7].sort_values(ascending=False)[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b3d7189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "고객 행렬 확인: (668, 30)\n",
      "영화 행렬 확인: (30, 30)\n"
     ]
    }
   ],
   "source": [
    "# 협업 필터링용 샘플 행렬 생성\n",
    "\n",
    "# 잠재요인 차원 30으로 설정\n",
    "K=30\n",
    "\n",
    "# 샘플용 영화 30개만 필터링\n",
    "df_merge_sample = df_merge_pivot.iloc[:,0:30]\n",
    "df_array = df_merge_sample.values\n",
    "user_cnt, item_cnt = df_array.shape\n",
    "\n",
    "# 고객수, 영화 수 x 자원 수 행렬 행성\n",
    "np.random.seed(47)\n",
    "user_matrix = np.random.normal(scale=1./K, size=(user_cnt, K))\n",
    "item_matrix = np.random.normal(scale=1./K, size=(item_cnt, K))\n",
    "\n",
    "print(\"고객 행렬 확인:\", user_matrix.shape)\n",
    "print(\"영화 행렬 확인:\", item_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d12ab56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMSE 함수 정의\n",
    "\n",
    "def get_rmse(df_array, user_matrix, item_matrix, not_nan_index):\n",
    "    error = 0\n",
    "    # 예측용 df_array 생성\n",
    "    pred_rating_matrix = user_matrix @ item_matrix.T\n",
    "    \n",
    "    # 결측 없는 실제 행렬과 예측 행렬 생성\n",
    "    df_array_not_null = df_array[not_nan_index]\n",
    "    pred_rating_matrix_not_null = pred_rating_matrix[not_nan_index]\n",
    "    \n",
    "    # RMSE 산출\n",
    "    mse = mean_squared_error(df_array_not_null, pred_rating_matrix_not_null)\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29d17d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 행렬 분해 함수 정의\n",
    "\n",
    "def matrix_factorization(df_array, K, steps=1000, \n",
    "                         learning_rate=0.01, r_lambda = 0.01):\n",
    "     \n",
    "    # 결측값이 아닌 df_array의 index 생성\n",
    "    not_nan_index = np.where(np.isnan(df_array) == False)\n",
    "    \n",
    "    # SGD 행렬 분해 알고리즘 적용\n",
    "    for step in range(steps):\n",
    "        for p, q, r in zip(not_nan_index[0], not_nan_index[1]\n",
    "                           ,df_array[not_nan_index]):\n",
    "            # 실제 값과 예측 값 차이 계산\n",
    "            r_pq = user_matrix[p, :] @ item_matrix[q, :].T\n",
    "            error_pq = r - r_pq\n",
    "\n",
    "            # SGD \n",
    "            user_matrix[p,:] = user_matrix[p,:] + learning_rate*(\n",
    "                error_pq * item_matrix[q, :] - r_lambda*user_matrix[p,:])\n",
    "            \n",
    "            item_matrix[q,:] = item_matrix[q,:] + learning_rate*(\n",
    "                error_pq * user_matrix[p, :] - r_lambda*item_matrix[q,:])\n",
    "\n",
    "        rmse = get_rmse(df_array, user_matrix, item_matrix, not_nan_index)\n",
    "\n",
    "        if ( (step + 1)  % 100) == 0 :\n",
    "            print(\"반복 횟수: \", step + 1 ,\" RMSE: \", np.round(rmse,3))\n",
    "            \n",
    "    return user_matrix, item_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30742a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "반복 횟수:  100  RMSE:  0.097\n",
      "반복 횟수:  200  RMSE:  0.027\n",
      "반복 횟수:  300  RMSE:  0.024\n",
      "반복 횟수:  400  RMSE:  0.023\n",
      "반복 횟수:  500  RMSE:  0.021\n",
      "반복 횟수:  600  RMSE:  0.02\n",
      "반복 횟수:  700  RMSE:  0.02\n",
      "반복 횟수:  800  RMSE:  0.019\n",
      "반복 횟수:  900  RMSE:  0.019\n",
      "반복 횟수:  1000  RMSE:  0.018\n"
     ]
    }
   ],
   "source": [
    "# 행렬 분해, 내적\n",
    "\n",
    "user_matrix, item_matrix = matrix_factorization(\n",
    "    df_array, K, steps=1000,\n",
    "    learning_rate=0.01, r_lambda = 0.01)\n",
    "\n",
    "pred_matrix = user_matrix @ item_matrix.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea6645b4",
   "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>title</th>\n",
       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <th>10 (1979)</th>\n",
       "      <th>...</th>\n",
       "      <th>10th Kingdom, The (2000)</th>\n",
       "      <th>11-11-11 (11-11-11: The Prophecy) (2011)</th>\n",
       "      <th>11:14 (2003)</th>\n",
       "      <th>11th Hour, The (2007)</th>\n",
       "      <th>12 (2007)</th>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <th>12 Angry Men (1997)</th>\n",
       "      <th>12 Rounds (2009)</th>\n",
       "      <th>12 Years a Slave (2013)</th>\n",
       "      <th>127 Hours (2010)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.051631</td>\n",
       "      <td>-0.083938</td>\n",
       "      <td>-0.069660</td>\n",
       "      <td>-0.105834</td>\n",
       "      <td>-0.186691</td>\n",
       "      <td>-0.094306</td>\n",
       "      <td>-0.098581</td>\n",
       "      <td>-0.177139</td>\n",
       "      <td>-0.103241</td>\n",
       "      <td>-0.082794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045882</td>\n",
       "      <td>-0.079783</td>\n",
       "      <td>-0.112016</td>\n",
       "      <td>-0.110756</td>\n",
       "      <td>-0.034177</td>\n",
       "      <td>-0.163101</td>\n",
       "      <td>-0.076059</td>\n",
       "      <td>-0.051933</td>\n",
       "      <td>-0.128448</td>\n",
       "      <td>-0.113047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.068995</td>\n",
       "      <td>0.063763</td>\n",
       "      <td>0.029378</td>\n",
       "      <td>0.103909</td>\n",
       "      <td>0.070665</td>\n",
       "      <td>0.035862</td>\n",
       "      <td>0.094289</td>\n",
       "      <td>0.170707</td>\n",
       "      <td>0.130561</td>\n",
       "      <td>0.053904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134833</td>\n",
       "      <td>0.066054</td>\n",
       "      <td>0.067439</td>\n",
       "      <td>0.097416</td>\n",
       "      <td>0.013622</td>\n",
       "      <td>0.127945</td>\n",
       "      <td>-0.041964</td>\n",
       "      <td>0.049973</td>\n",
       "      <td>0.106233</td>\n",
       "      <td>0.082817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128438</td>\n",
       "      <td>0.089740</td>\n",
       "      <td>0.090381</td>\n",
       "      <td>0.066533</td>\n",
       "      <td>0.103108</td>\n",
       "      <td>0.031881</td>\n",
       "      <td>0.212484</td>\n",
       "      <td>0.048289</td>\n",
       "      <td>0.182902</td>\n",
       "      <td>0.070970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142718</td>\n",
       "      <td>0.191402</td>\n",
       "      <td>0.256842</td>\n",
       "      <td>0.230997</td>\n",
       "      <td>0.064861</td>\n",
       "      <td>0.149501</td>\n",
       "      <td>-0.003335</td>\n",
       "      <td>0.198401</td>\n",
       "      <td>0.207483</td>\n",
       "      <td>0.198376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.895991</td>\n",
       "      <td>1.712282</td>\n",
       "      <td>1.787002</td>\n",
       "      <td>2.657818</td>\n",
       "      <td>2.820935</td>\n",
       "      <td>2.213617</td>\n",
       "      <td>2.082815</td>\n",
       "      <td>2.555981</td>\n",
       "      <td>3.306608</td>\n",
       "      <td>2.252366</td>\n",
       "      <td>...</td>\n",
       "      <td>1.648483</td>\n",
       "      <td>1.936613</td>\n",
       "      <td>2.729978</td>\n",
       "      <td>2.677794</td>\n",
       "      <td>1.100380</td>\n",
       "      <td>4.981980</td>\n",
       "      <td>0.620007</td>\n",
       "      <td>2.018014</td>\n",
       "      <td>3.723871</td>\n",
       "      <td>2.951902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.076697</td>\n",
       "      <td>-0.039216</td>\n",
       "      <td>-0.029270</td>\n",
       "      <td>-0.007633</td>\n",
       "      <td>-0.051874</td>\n",
       "      <td>-0.026496</td>\n",
       "      <td>0.004058</td>\n",
       "      <td>0.040571</td>\n",
       "      <td>-0.024389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042998</td>\n",
       "      <td>0.052845</td>\n",
       "      <td>0.024583</td>\n",
       "      <td>0.030629</td>\n",
       "      <td>-0.026081</td>\n",
       "      <td>-0.008237</td>\n",
       "      <td>0.040031</td>\n",
       "      <td>0.000849</td>\n",
       "      <td>0.007089</td>\n",
       "      <td>-0.021943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1        -0.051631                                -0.083938   \n",
       "2         0.068995                                 0.063763   \n",
       "3         0.128438                                 0.089740   \n",
       "4         2.895991                                 1.712282   \n",
       "5         0.001853                                 0.076697   \n",
       "\n",
       "title   'Round Midnight (1986)  'Til There Was You (1997)  'burbs, The (1989)  \\\n",
       "userId                                                                          \n",
       "1                    -0.069660                  -0.105834           -0.186691   \n",
       "2                     0.029378                   0.103909            0.070665   \n",
       "3                     0.090381                   0.066533            0.103108   \n",
       "4                     1.787002                   2.657818            2.820935   \n",
       "5                    -0.039216                  -0.029270           -0.007633   \n",
       "\n",
       "title   'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                      \n",
       "1                  -0.094306                    -0.098581   \n",
       "2                   0.035862                     0.094289   \n",
       "3                   0.031881                     0.212484   \n",
       "4                   2.213617                     2.082815   \n",
       "5                  -0.051874                    -0.026496   \n",
       "\n",
       "title   *batteries not included (1987)  ...And Justice for All (1979)  \\\n",
       "userId                                                                  \n",
       "1                            -0.177139                      -0.103241   \n",
       "2                             0.170707                       0.130561   \n",
       "3                             0.048289                       0.182902   \n",
       "4                             2.555981                       3.306608   \n",
       "5                             0.004058                       0.040571   \n",
       "\n",
       "title   10 (1979)  ...  10th Kingdom, The (2000)  \\\n",
       "userId             ...                             \n",
       "1       -0.082794  ...                 -0.045882   \n",
       "2        0.053904  ...                  0.134833   \n",
       "3        0.070970  ...                  0.142718   \n",
       "4        2.252366  ...                  1.648483   \n",
       "5       -0.024389  ...                  0.042998   \n",
       "\n",
       "title   11-11-11 (11-11-11: The Prophecy) (2011)  11:14 (2003)  \\\n",
       "userId                                                           \n",
       "1                                      -0.079783     -0.112016   \n",
       "2                                       0.066054      0.067439   \n",
       "3                                       0.191402      0.256842   \n",
       "4                                       1.936613      2.729978   \n",
       "5                                       0.052845      0.024583   \n",
       "\n",
       "title   11th Hour, The (2007)  12 (2007)  12 Angry Men (1957)  \\\n",
       "userId                                                          \n",
       "1                   -0.110756  -0.034177            -0.163101   \n",
       "2                    0.097416   0.013622             0.127945   \n",
       "3                    0.230997   0.064861             0.149501   \n",
       "4                    2.677794   1.100380             4.981980   \n",
       "5                    0.030629  -0.026081            -0.008237   \n",
       "\n",
       "title   12 Angry Men (1997)  12 Rounds (2009)  12 Years a Slave (2013)  \\\n",
       "userId                                                                   \n",
       "1                 -0.076059         -0.051933                -0.128448   \n",
       "2                 -0.041964          0.049973                 0.106233   \n",
       "3                 -0.003335          0.198401                 0.207483   \n",
       "4                  0.620007          2.018014                 3.723871   \n",
       "5                  0.040031          0.000849                 0.007089   \n",
       "\n",
       "title   127 Hours (2010)  \n",
       "userId                    \n",
       "1              -0.113047  \n",
       "2               0.082817  \n",
       "3               0.198376  \n",
       "4               2.951902  \n",
       "5              -0.021943  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 프레임 변환\n",
    "ratings_pred_matrix = pd.DataFrame(data=pred_matrix, \n",
    "                                   index= df_merge_sample.index,\n",
    "                                   columns = df_merge_sample.columns)\n",
    "\n",
    "ratings_pred_matrix.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30fee405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 영화 추천을 위한 함수 설정\n",
    "\n",
    "# 미상영 영화 리스트 추출 함수\n",
    "def get_unseen_movies(df_merge_sample, userId):\n",
    "\n",
    "    # 모든 영화 리스트 생성\n",
    "    movies_list = df_merge_sample.columns.tolist()\n",
    "    \n",
    "    # 고객 별 평점 테이블 생성\n",
    "    ratings = df_merge_sample.loc[userId,:]\n",
    "    \n",
    "    # 평점을 메기지 않은 영화 리스트 생성\n",
    "    none_rating_list = ratings[ratings.isnull()].index.tolist()\n",
    "    \n",
    "    # 평점 없는 영화로 미상영 영화 리스트 생성\n",
    "    unseen_movie_list = [ movie for movie in movies_list if movie in none_rating_list]\n",
    "    \n",
    "    return unseen_movie_list\n",
    "\n",
    "# 미상영 영화 중 예측 점수가 높은 순으로 정렬\n",
    "def recomm_movie_by_userid(pred_df, userId, unseen_movie_list, top_n=10):    \n",
    "    recomm_movies = pred_df.loc[userId, unseen_movie_list\n",
    "                               ].sort_values(ascending=False)[:top_n]\n",
    "    \n",
    "    return recomm_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a157eba",
   "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>title</th>\n",
       "      <th>pred_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12 Years a Slave (2013)</td>\n",
       "      <td>3.569347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127 Hours (2010)</td>\n",
       "      <td>3.362532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101 Dalmatians (One Hundred and One Dalmatians...</td>\n",
       "      <td>2.968062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10 Items or Less (2006)</td>\n",
       "      <td>2.949394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11:14 (2003)</td>\n",
       "      <td>2.884700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11th Hour, The (2007)</td>\n",
       "      <td>2.742791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>*batteries not included (1987)</td>\n",
       "      <td>2.701928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>'71 (2014)</td>\n",
       "      <td>2.672483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12 Rounds (2009)</td>\n",
       "      <td>2.585290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10th &amp; Wolf (2006)</td>\n",
       "      <td>2.489459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  pred_score\n",
       "0                            12 Years a Slave (2013)    3.569347\n",
       "1                                   127 Hours (2010)    3.362532\n",
       "2  101 Dalmatians (One Hundred and One Dalmatians...    2.968062\n",
       "3                            10 Items or Less (2006)    2.949394\n",
       "4                                       11:14 (2003)    2.884700\n",
       "5                              11th Hour, The (2007)    2.742791\n",
       "6                     *batteries not included (1987)    2.701928\n",
       "7                                         '71 (2014)    2.672483\n",
       "8                                   12 Rounds (2009)    2.585290\n",
       "9                                 10th & Wolf (2006)    2.489459"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 575번 고객의 추천 영화 리스트 생성\n",
    "\n",
    "# 575번 고객의 미상영 영화 리스트 생성\n",
    "unseen_movie_list = get_unseen_movies(df_merge_sample, 575)\n",
    "\n",
    "# 미상영 영화 중 예측 평점 높은 영화 리스트 생성\n",
    "recomm_movies = recomm_movie_by_userid(ratings_pred_matrix, 575\n",
    "                                       ,unseen_movie_list, top_n=10)\n",
    "\n",
    "# 최종 데이터셋 생성\n",
    "recomm_movies = pd.DataFrame(data=recomm_movies.values,\n",
    "                             index=recomm_movies.index,\n",
    "                             columns=['pred_score']).reset_index()\n",
    "\n",
    "recomm_movies.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6774acdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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