2-3 차원축소_복습과제.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. PCA 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)  wine data를 불러오고 스케일링을 하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.51861254, -0.5622498 ,  0.23205254, ...,  0.36217728,\n",
       "         1.84791957,  1.01300893],\n",
       "       [ 0.24628963, -0.49941338, -0.82799632, ...,  0.40605066,\n",
       "         1.1134493 ,  0.96524152],\n",
       "       [ 0.19687903,  0.02123125,  1.10933436, ...,  0.31830389,\n",
       "         0.78858745,  1.39514818],\n",
       "       ...,\n",
       "       [ 0.33275817,  1.74474449, -0.38935541, ..., -1.61212515,\n",
       "        -1.48544548,  0.28057537],\n",
       "       [ 0.20923168,  0.22769377,  0.01273209, ..., -1.56825176,\n",
       "        -1.40069891,  0.29649784],\n",
       "       [ 1.39508604,  1.58316512,  1.36520822, ..., -1.52437837,\n",
       "        -1.42894777, -0.59516041]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_scaled = StandardScaler().fit_transform(wine.data)\n",
    "wine_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 스케일링된 데이터에 주성분 수 7로 설정해서 PCA 데이터로 변환하고 개별 주성분이 차지하는 변동성 비율을 출력하세요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.36198848 0.1920749  0.11123631 0.0706903  0.06563294 0.04935823\n",
      " 0.04238679]\n"
     ]
    }
   ],
   "source": [
    "pca=PCA(n_components=7)\n",
    "\n",
    "pca=pca.fit(wine_scaled)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.31675081, -1.44346263, -0.16573904, ...,  0.69304284,\n",
       "        -0.22388013,  0.59642655],\n",
       "       [ 2.20946492,  0.33339289, -2.02645737, ..., -0.25765463,\n",
       "        -0.92712024,  0.05377561],\n",
       "       [ 2.51674015, -1.0311513 ,  0.98281867, ..., -0.25103312,\n",
       "         0.54927605,  0.42420545],\n",
       "       ...,\n",
       "       [-2.67783946, -2.76089913, -0.94094188, ...,  1.271355  ,\n",
       "         0.27306792,  0.67923541],\n",
       "       [-2.38701709, -2.29734668, -0.5506962 , ...,  0.81395522,\n",
       "         1.17878298,  0.63397527],\n",
       "       [-3.20875816, -2.76891957,  1.01391366, ..., -0.89519259,\n",
       "         0.29609218,  0.00574126]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_pca=pca.transform(wine_scaled)\n",
    "wine_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) 원본 와인 데이터세트와 PCA로 변환된 데이터 세트에 각각 분류를 적용한 후 결과를 출력하세요.\n",
    "####    - 분류기는 RandomFoestClassifier를 사용하세요\n",
    "####    - 교차 검증 폴드수 : 3\n",
    "####    - 출력 결과는\n",
    "       원본 데이터 평균 정확도 : [0.xxx,0.xxx,0.xxx]\n",
    "       PCA 변환 데이터 평균 정확도 : [0.xxx,0.xxx,0.xxx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcf=RandomForestClassifier(random_state=156)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 데이터 평균 정확도 :0.9165  \n",
      " [0.86666667 0.9        0.98275862]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "scores = cross_val_score(rcf,wine.data,wine.target,scoring='accuracy',cv=3)\n",
    "print('원본 데이터 평균 정확도 :{0:.4f} '.format(np.mean(scores)), '\\n', scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>comp5</th>\n",
       "      <th>comp6</th>\n",
       "      <th>comp7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.316751</td>\n",
       "      <td>-1.443463</td>\n",
       "      <td>-0.165739</td>\n",
       "      <td>-0.215631</td>\n",
       "      <td>0.693043</td>\n",
       "      <td>-0.223880</td>\n",
       "      <td>0.596427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.209465</td>\n",
       "      <td>0.333393</td>\n",
       "      <td>-2.026457</td>\n",
       "      <td>-0.291358</td>\n",
       "      <td>-0.257655</td>\n",
       "      <td>-0.927120</td>\n",
       "      <td>0.053776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.516740</td>\n",
       "      <td>-1.031151</td>\n",
       "      <td>0.982819</td>\n",
       "      <td>0.724902</td>\n",
       "      <td>-0.251033</td>\n",
       "      <td>0.549276</td>\n",
       "      <td>0.424205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.757066</td>\n",
       "      <td>-2.756372</td>\n",
       "      <td>-0.176192</td>\n",
       "      <td>0.567983</td>\n",
       "      <td>-0.311842</td>\n",
       "      <td>0.114431</td>\n",
       "      <td>-0.383337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.008908</td>\n",
       "      <td>-0.869831</td>\n",
       "      <td>2.026688</td>\n",
       "      <td>-0.409766</td>\n",
       "      <td>0.298458</td>\n",
       "      <td>-0.406520</td>\n",
       "      <td>0.444074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comp1     comp2     comp3     comp4     comp5     comp6     comp7\n",
       "0  3.316751 -1.443463 -0.165739 -0.215631  0.693043 -0.223880  0.596427\n",
       "1  2.209465  0.333393 -2.026457 -0.291358 -0.257655 -0.927120  0.053776\n",
       "2  2.516740 -1.031151  0.982819  0.724902 -0.251033  0.549276  0.424205\n",
       "3  3.757066 -2.756372 -0.176192  0.567983 -0.311842  0.114431 -0.383337\n",
       "4  1.008908 -0.869831  2.026688 -0.409766  0.298458 -0.406520  0.444074"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "pca_columns=['comp1','comp2','comp3','comp4','comp5','comp6','comp7']\n",
    "wineDF_pca = pd.DataFrame(wine_pca,columns=pca_columns)\n",
    "#wineDF_pca['target']=wine.target\n",
    "wineDF_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA 변환 데이터 평균 정확도 :0.9330  \n",
      " [0.91666667 0.91666667 0.96551724]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "scores_pca = cross_val_score(rcf,wineDF_pca,wine.target,scoring='accuracy',cv=3)\n",
    "print('PCA 변환 데이터 평균 정확도 :{0:.4f} '.format(np.mean(scores_pca)), '\\n', scores_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. LDA 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)     1번에서 스케일링한 와인 데이터를 컴포넌트 수를 2로 하여 lda변환 후 shape을 출력하세요.\n",
    "    \n",
    "    출력 결과 : (178,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda=LinearDiscriminantAnalysis(n_components=2)\n",
    "lda.fit(wine_scaled,wine.target)\n",
    "wine_lda=lda.transform(wine_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_lda.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) lda변환된 데이터 값을 2차원 평면에 class별로 표현하세요.\n",
    "    \n",
    "    colors와 markers는 밑에 제시했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_columns=['lda_comp1','lda_comp2']\n",
    "wineDF_lda=pd.DataFrame(wine_lda,columns=lda_columns)\n",
    "wineDF_lda['target']=wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors =['r','b','g']\n",
    "markers=['s','x','o']\n",
    "\n",
    "for i,marker in enumerate(markers):\n",
    "    x_data=wineDF_lda[wineDF_lda['target']==i]['lda_comp1']\n",
    "    y_data=wineDF_lda[wineDF_lda['target']==i]['lda_comp2']\n",
    "    plt.scatter(x_data,y_data,marker=marker, label=wine.target_names[i])\n",
    "    \n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('lda_comp1')\n",
    "plt.ylabel('lda_comp2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. SVD 실습 (추천 시스템)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVD는 추천시스템 중 '협업 필터링 Collaborative Filtering'기법에서 자주 사용되는 방식입니다. <br /> 다음의 과정에 따라서 데이터 전처리 및 SVD를 수행하고 추천결과를 확인해보세요 \n",
    "     \n",
    "**다음의 자료에서 많은 도움을 얻었습니다**     \n",
    "https://lsjsj92.tistory.com/569?category=853217\n",
    "     \n",
    "3.1. 원자료를 불러온다     \n",
    "\n",
    "    1) movies.csv를 데이터프레임으로 불러와서 raw_movies_df라고 저장한다     \n",
    "    2) ratings.csv를 데이터프레임으로 불러와서 raw_ratings_df라고 저장한다  \n",
    "    3) raw_ratings_df를 이용하여 피벗된 객체 user_movie_rating_df를 생성한다.     \n",
    "    index는 userId, column은 movieId, value는 rating으로 한다\n",
    "    4) user_movie_rating_df의 결측값들을 모두 0으로 변환한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_movies_df=pd.read_csv('movies.csv')\n",
    "raw_ratings_df=pd.read_csv('ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>851868750</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1273541953</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>834828440</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847057202</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp                   title genres\n",
       "0       1       31     2.5  1260759144  Dangerous Minds (1995)  Drama\n",
       "1       7       31     3.0   851868750  Dangerous Minds (1995)  Drama\n",
       "2      31       31     4.0  1273541953  Dangerous Minds (1995)  Drama\n",
       "3      32       31     4.0   834828440  Dangerous Minds (1995)  Drama\n",
       "4      36       31     3.0   847057202  Dangerous Minds (1995)  Drama"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_df=pd.merge(raw_ratings_df,raw_movies_df, on='movieId')\n",
    "user_movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_movie_rating_df=user_movie_df.pivot_table('rating',index='userId',columns='movieId').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "5           0.0     0.0     4.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  9       10      ...  161084  161155  161594  161830  161918  161944  \\\n",
       "userId                   ...                                                   \n",
       "1           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           0.0     4.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4           0.0     4.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "5           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  162376  162542  162672  163949  \n",
       "userId                                   \n",
       "1           0.0     0.0     0.0     0.0  \n",
       "2           0.0     0.0     0.0     0.0  \n",
       "3           0.0     0.0     0.0     0.0  \n",
       "4           0.0     0.0     0.0     0.0  \n",
       "5           0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_rating_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2. 데이터프레임안의 값들을 X로 추출하여서 표준화를 실시한다\n",
    "- 표준화는 각 데이터를 데이터가 속한 행의 평균값에서 빼는 방식으로 진행한다 (sklearn의 StandardScaler를 사용하는 것이 아님)\n",
    "- 표준화된 X를 **X_scaled**라고 저장한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
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       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>3.970770</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>3.902162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>3.956872</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId    1         2         3         4         5         6         7       \\\n",
       "userId                                                                          \n",
       "1       -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625   \n",
       "2       -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230   \n",
       "3       -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075   \n",
       "4       -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838   \n",
       "5       -0.043128 -0.043128  3.956872 -0.043128 -0.043128 -0.043128 -0.043128   \n",
       "\n",
       "movieId    8         9         10      ...    161084    161155    161594  \\\n",
       "userId                                 ...                                 \n",
       "1       -0.005625 -0.005625 -0.005625  ... -0.005625 -0.005625 -0.005625   \n",
       "2       -0.029230 -0.029230  3.970770  ... -0.029230 -0.029230 -0.029230   \n",
       "3       -0.020075 -0.020075 -0.020075  ... -0.020075 -0.020075 -0.020075   \n",
       "4       -0.097838 -0.097838  3.902162  ... -0.097838 -0.097838 -0.097838   \n",
       "5       -0.043128 -0.043128 -0.043128  ... -0.043128 -0.043128 -0.043128   \n",
       "\n",
       "movieId    161830    161918    161944    162376    162542    162672    163949  \n",
       "userId                                                                         \n",
       "1       -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625  \n",
       "2       -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230  \n",
       "3       -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075  \n",
       "4       -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838  \n",
       "5       -0.043128 -0.043128 -0.043128 -0.043128 -0.043128 -0.043128 -0.043128  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled=user_movie_rating_df.sub(user_movie_rating_df.mean(axis=1), axis=0)\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.3. X_scaled를 바탕으로 TruncatedSVD를 시행하여 사용자 평점 예측 객체를 생성한다.\n",
    "- sklearn 모듈을 사용하지 않고 from scipy.sparse.linalg import svds를 사용합니다\n",
    "- 사용방법은 인자로 Truncated SVD를 수행하고자 하는 행렬을 첫번째로,\n",
    "두번째 인자에는 최종 압축되어 산출되는 열의 개수 k를 입력합니다.\n",
    "- ex) U, sigma, Vt = svds(?, k = 12)\n",
    "- -> k = 12로 설정 \n",
    "- 생성된 Truncated SVD객체를 svd_preds_df라고 저장한다\n",
    "- -> columns는 user_movie_rating_df.columns로 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.linalg import svds\n",
    "from scipy.linalg import svd\n",
    "\n",
    "U, Sigma, Vt = svd(X_scaled)\n",
    "U_tr,Sigma_tr,Vt_tr=svds(X_scaled,k=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(671, 671) (671,) (9066, 9066)\n",
      "(671, 12) (12,) (12, 9066)\n"
     ]
    }
   ],
   "source": [
    "print(U.shape,Sigma.shape,Vt.shape)  \n",
    "print(U_tr.shape,Sigma_tr.shape,Vt_tr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "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>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.085312</td>\n",
       "      <td>0.016154</td>\n",
       "      <td>-0.019462</td>\n",
       "      <td>-0.011496</td>\n",
       "      <td>-0.034502</td>\n",
       "      <td>0.026746</td>\n",
       "      <td>-0.004911</td>\n",
       "      <td>-0.010054</td>\n",
       "      <td>-0.010845</td>\n",
       "      <td>0.032570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009949</td>\n",
       "      <td>-0.009978</td>\n",
       "      <td>0.004853</td>\n",
       "      <td>-0.009882</td>\n",
       "      <td>-0.009570</td>\n",
       "      <td>-0.011300</td>\n",
       "      <td>0.012532</td>\n",
       "      <td>-0.011201</td>\n",
       "      <td>-0.010923</td>\n",
       "      <td>-0.009392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.399222</td>\n",
       "      <td>1.579611</td>\n",
       "      <td>0.500246</td>\n",
       "      <td>0.139048</td>\n",
       "      <td>0.491579</td>\n",
       "      <td>1.078243</td>\n",
       "      <td>0.500489</td>\n",
       "      <td>0.060146</td>\n",
       "      <td>0.267040</td>\n",
       "      <td>1.940801</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016003</td>\n",
       "      <td>-0.031505</td>\n",
       "      <td>-0.008550</td>\n",
       "      <td>-0.034475</td>\n",
       "      <td>-0.036874</td>\n",
       "      <td>-0.050249</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>-0.030187</td>\n",
       "      <td>-0.029983</td>\n",
       "      <td>-0.002329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.957171</td>\n",
       "      <td>0.376896</td>\n",
       "      <td>-0.019776</td>\n",
       "      <td>0.007369</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.121383</td>\n",
       "      <td>-0.077209</td>\n",
       "      <td>0.011558</td>\n",
       "      <td>-0.032613</td>\n",
       "      <td>0.363501</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017314</td>\n",
       "      <td>-0.015168</td>\n",
       "      <td>-0.034265</td>\n",
       "      <td>-0.020326</td>\n",
       "      <td>-0.026082</td>\n",
       "      <td>-0.023264</td>\n",
       "      <td>-0.046991</td>\n",
       "      <td>-0.005438</td>\n",
       "      <td>-0.006788</td>\n",
       "      <td>-0.025816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.773006</td>\n",
       "      <td>1.072155</td>\n",
       "      <td>0.154364</td>\n",
       "      <td>-0.003007</td>\n",
       "      <td>-0.279551</td>\n",
       "      <td>-0.609791</td>\n",
       "      <td>-0.125658</td>\n",
       "      <td>-0.240918</td>\n",
       "      <td>-0.084591</td>\n",
       "      <td>1.363856</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.071426</td>\n",
       "      <td>-0.125083</td>\n",
       "      <td>-0.043157</td>\n",
       "      <td>-0.079388</td>\n",
       "      <td>-0.063295</td>\n",
       "      <td>-0.133578</td>\n",
       "      <td>-0.008949</td>\n",
       "      <td>-0.117203</td>\n",
       "      <td>-0.114952</td>\n",
       "      <td>-0.031279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.139649</td>\n",
       "      <td>0.881775</td>\n",
       "      <td>0.032870</td>\n",
       "      <td>0.018377</td>\n",
       "      <td>0.559552</td>\n",
       "      <td>-0.202953</td>\n",
       "      <td>0.296796</td>\n",
       "      <td>0.038405</td>\n",
       "      <td>-0.122794</td>\n",
       "      <td>0.491889</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.072252</td>\n",
       "      <td>-0.072485</td>\n",
       "      <td>-0.034064</td>\n",
       "      <td>-0.072220</td>\n",
       "      <td>-0.074018</td>\n",
       "      <td>-0.100581</td>\n",
       "      <td>-0.016784</td>\n",
       "      <td>-0.067156</td>\n",
       "      <td>-0.067743</td>\n",
       "      <td>-0.075880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId    1         2         3         4         5         6         7       \\\n",
       "0       -0.085312  0.016154 -0.019462 -0.011496 -0.034502  0.026746 -0.004911   \n",
       "1        1.399222  1.579611  0.500246  0.139048  0.491579  1.078243  0.500489   \n",
       "2        0.957171  0.376896 -0.019776  0.007369  0.001212  0.121383 -0.077209   \n",
       "3        1.773006  1.072155  0.154364 -0.003007 -0.279551 -0.609791 -0.125658   \n",
       "4        1.139649  0.881775  0.032870  0.018377  0.559552 -0.202953  0.296796   \n",
       "\n",
       "movieId    8         9         10      ...    161084    161155    161594  \\\n",
       "0       -0.010054 -0.010845  0.032570  ... -0.009949 -0.009978  0.004853   \n",
       "1        0.060146  0.267040  1.940801  ... -0.016003 -0.031505 -0.008550   \n",
       "2        0.011558 -0.032613  0.363501  ... -0.017314 -0.015168 -0.034265   \n",
       "3       -0.240918 -0.084591  1.363856  ... -0.071426 -0.125083 -0.043157   \n",
       "4        0.038405 -0.122794  0.491889  ... -0.072252 -0.072485 -0.034064   \n",
       "\n",
       "movieId    161830    161918    161944    162376    162542    162672    163949  \n",
       "0       -0.009882 -0.009570 -0.011300  0.012532 -0.011201 -0.010923 -0.009392  \n",
       "1       -0.034475 -0.036874 -0.050249  0.002013 -0.030187 -0.029983 -0.002329  \n",
       "2       -0.020326 -0.026082 -0.023264 -0.046991 -0.005438 -0.006788 -0.025816  \n",
       "3       -0.079388 -0.063295 -0.133578 -0.008949 -0.117203 -0.114952 -0.031279  \n",
       "4       -0.072220 -0.074018 -0.100581 -0.016784 -0.067156 -0.067743 -0.075880  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd_preds=np.dot(np.dot(U_tr,np.diag(Sigma_tr)),Vt_tr)\n",
    "svd_preds_df=pd.DataFrame(svd_preds,columns=user_movie_rating_df.columns)\n",
    "svd_preds_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.4. 아래에서 제시되는 사용자 정의함수를 이용하여 **user_id = 330**인 고객에게 5개의 영화를 추천해보는 알고리즘을 수행해보세요!\n",
    "\n",
    "- 인자의 등장순서는 \n",
    "\n",
    "    1) Truncated SVD를 통해 구해진 svd_preds_df    \n",
    "    2) 예측하고자 하는 user_id, 번호    \n",
    "    3) 원래 가지고 있는 영화들의 목록을 담은 raw_movies_df    \n",
    "    4) 원래 가지고 있는 유저별 영화의 평점 데이터 raw_ratings_df    \n",
    "    5) 해당 유저에게 추천해주고자 하는 영화의 개수   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://simonezz.tistory.com/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(svd_preds_df, user_id, raw_movies_df, raw_ratings_df, num_recommendations):\n",
    "    user_row_num=user_id-1\n",
    "    \n",
    "    # 사용자 index에 따라 영화 평점이 높은 순으로 정렬\n",
    "    sorted_user_predictions=svd_preds_df.iloc[user_row_num].sort_values(ascending=False)\n",
    "    \n",
    "    # 원본 평점데이터에서 user_id에 해당하는 데이터를 뽑아내기\n",
    "    user_data=raw_ratings_df[raw_ratings_df.userId==user_id]\n",
    "    \n",
    "    # 위에서 뽑은 user_data와 원본데이터를 합친다.\n",
    "    user_history=user_data.merge(raw_movies_df,on='movieId').sort_values(['rating'],ascending=False)\n",
    "    \n",
    "    # 원본 영화데이터에서 사용자가 본 영화데이터를 제외한 데이터 추출\n",
    "    recommendations=raw_movies_df[raw_movies_df['movieId'].isin(user_history['movieId'])]\n",
    "    # 사용자의 영화 평점이 높은 순으로 정렬된 데이터와 위 recommendation을 merge\n",
    "    recommendations=recommendations.merge(pd.DataFrame(sorted_user_predictions).reset_index(),on='movieId')\n",
    "    \n",
    "    # 컬럼이름을 바꾸고 정렬해서 return\n",
    "    recommendations=recommendations.rename(columns={user_row_num: 'Predictions'}).sort_values('Predictions',ascending=False)\n",
    "    \n",
    "    return user_history, recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "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>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>330</td>\n",
       "      <td>1094</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577200</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>330</td>\n",
       "      <td>213</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577039</td>\n",
       "      <td>Burnt by the Sun (Utomlyonnye solntsem) (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>330</td>\n",
       "      <td>527</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577164</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>330</td>\n",
       "      <td>1035</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948574126</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>330</td>\n",
       "      <td>1172</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948575236</td>\n",
       "      <td>Cinema Paradiso (Nuovo cinema Paradiso) (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating  timestamp  \\\n",
       "47     330     1094     5.0  948577200   \n",
       "14     330      213     5.0  948577039   \n",
       "34     330      527     5.0  948577164   \n",
       "45     330     1035     5.0  948574126   \n",
       "48     330     1172     5.0  948575236   \n",
       "\n",
       "                                             title                  genres  \n",
       "47                         Crying Game, The (1992)  Drama|Romance|Thriller  \n",
       "14  Burnt by the Sun (Utomlyonnye solntsem) (1994)                   Drama  \n",
       "34                         Schindler's List (1993)               Drama|War  \n",
       "45                      Sound of Music, The (1965)         Musical|Romance  \n",
       "48  Cinema Paradiso (Nuovo cinema Paradiso) (1989)                   Drama  "
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_history, predictions = recommend(svd_preds_df,330, raw_movies_df, raw_ratings_df, 5)\n",
    "user_history.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>2.042527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>593</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>Crime|Horror|Thriller</td>\n",
       "      <td>1.891536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>1.627316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>457</td>\n",
       "      <td>Fugitive, The (1993)</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>1.625933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Adventure|Drama|IMAX</td>\n",
       "      <td>1.593873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                             title                 genres  \\\n",
       "22      318  Shawshank Redemption, The (1994)            Crime|Drama   \n",
       "36      593  Silence of the Lambs, The (1991)  Crime|Horror|Thriller   \n",
       "34      527           Schindler's List (1993)              Drama|War   \n",
       "29      457              Fugitive, The (1993)               Thriller   \n",
       "10      150                  Apollo 13 (1995)   Adventure|Drama|IMAX   \n",
       "\n",
       "    Predictions  \n",
       "22     2.042527  \n",
       "36     1.891536  \n",
       "34     1.627316  \n",
       "29     1.625933  \n",
       "10     1.593873  "
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 답"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "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>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>330</td>\n",
       "      <td>1094</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577200</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>330</td>\n",
       "      <td>213</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577039</td>\n",
       "      <td>Burnt by the Sun (Utomlyonnye solntsem) (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>330</td>\n",
       "      <td>527</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577164</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>330</td>\n",
       "      <td>1035</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948574126</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>330</td>\n",
       "      <td>1172</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948575236</td>\n",
       "      <td>Cinema Paradiso (Nuovo cinema Paradiso) (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating  timestamp  \\\n",
       "47     330     1094     5.0  948577200   \n",
       "14     330      213     5.0  948577039   \n",
       "34     330      527     5.0  948577164   \n",
       "45     330     1035     5.0  948574126   \n",
       "48     330     1172     5.0  948575236   \n",
       "\n",
       "                                             title                  genres  \n",
       "47                         Crying Game, The (1992)  Drama|Romance|Thriller  \n",
       "14  Burnt by the Sun (Utomlyonnye solntsem) (1994)                   Drama  \n",
       "34                         Schindler's List (1993)               Drama|War  \n",
       "45                      Sound of Music, The (1965)         Musical|Romance  \n",
       "48  Cinema Paradiso (Nuovo cinema Paradiso) (1989)                   Drama  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#생성된 유저의 이전 평점 기록\n",
    "user_history.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>2.023774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "      <td>1.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>608</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>1.489821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>590</td>\n",
       "      <td>Dances with Wolves (1990)</td>\n",
       "      <td>Adventure|Drama|Western</td>\n",
       "      <td>1.472671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>380</td>\n",
       "      <td>True Lies (1994)</td>\n",
       "      <td>Action|Adventure|Comedy|Romance|Thriller</td>\n",
       "      <td>1.240356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                      title  \\\n",
       "246      296        Pulp Fiction (1994)   \n",
       "294      356        Forrest Gump (1994)   \n",
       "497      608               Fargo (1996)   \n",
       "487      590  Dances with Wolves (1990)   \n",
       "316      380           True Lies (1994)   \n",
       "\n",
       "                                       genres  Predictions  \n",
       "246               Comedy|Crime|Drama|Thriller     2.023774  \n",
       "294                  Comedy|Drama|Romance|War     1.736524  \n",
       "497               Comedy|Crime|Drama|Thriller     1.489821  \n",
       "487                   Adventure|Drama|Western     1.472671  \n",
       "316  Action|Adventure|Comedy|Romance|Thriller     1.240356  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#예측된 유저의 영화추천 목록\n",
    "recommendations.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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