06-12. Adadelta, relu+sigmoid, L2.ipynb

{
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     "end_time": "2019-11-27T16:41:01.694377Z",
     "start_time": "2019-11-27T16:40:54.210691Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:41:26.091872Z",
     "start_time": "2019-11-27T16:41:07.516688Z"
    }
   },
   "outputs": [
    {
     "data": {
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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>beer_id</th>\n",
       "      <th>username</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>look</th>\n",
       "      <th>smell</th>\n",
       "      <th>taste</th>\n",
       "      <th>feel</th>\n",
       "      <th>overall</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>271781</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>2017-03-17</td>\n",
       "      <td>750 ml bottle, 2016 vintage, bottle #304 of...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>125646</td>\n",
       "      <td>_dirty_</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td></td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>125646</td>\n",
       "      <td>CJDUBYA</td>\n",
       "      <td>2017-12-21</td>\n",
       "      <td></td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>125646</td>\n",
       "      <td>GratefulBeerGuy</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>0% 16 oz can. Funny story: As I finally wal...</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>125646</td>\n",
       "      <td>LukeGude</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>Classic TH NEIPA. Overflowing head and bouq...</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_id         username        date  \\\n",
       "0   271781     bluejacket74  2017-03-17   \n",
       "1   125646          _dirty_  2017-12-21   \n",
       "2   125646          CJDUBYA  2017-12-21   \n",
       "3   125646  GratefulBeerGuy  2017-12-20   \n",
       "4   125646         LukeGude  2017-12-20   \n",
       "\n",
       "                                                text  look  smell  taste  \\\n",
       "0     750 ml bottle, 2016 vintage, bottle #304 of...  4.00   4.00   4.00   \n",
       "1                                                     4.50   4.50   4.50   \n",
       "2                                                     4.75   4.75   4.75   \n",
       "3     0% 16 oz can. Funny story: As I finally wal...  4.75   4.75   4.50   \n",
       "4     Classic TH NEIPA. Overflowing head and bouq...  4.25   4.50   4.25   \n",
       "\n",
       "   feel  overall  score  \n",
       "0  4.25     4.00   4.03  \n",
       "1  4.50     4.50   4.50  \n",
       "2  4.75     4.75   4.75  \n",
       "3  4.50     4.50   4.58  \n",
       "4  4.25     4.25   4.31  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('reviews.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:31.199539Z",
     "start_time": "2019-11-27T16:42:31.197539Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9073128, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape) #원본 데이터 900만개를 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:35.466500Z",
     "start_time": "2019-11-27T16:42:35.277458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>beerid</th>\n",
       "      <th>userid</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>271781</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>125646</td>\n",
       "      <td>_dirty_</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>125646</td>\n",
       "      <td>CJDUBYA</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>125646</td>\n",
       "      <td>GratefulBeerGuy</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>125646</td>\n",
       "      <td>LukeGude</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beerid           userid  rating\n",
       "0  271781     bluejacket74    4.03\n",
       "1  125646          _dirty_    4.50\n",
       "2  125646          CJDUBYA    4.75\n",
       "3  125646  GratefulBeerGuy    4.58\n",
       "4  125646         LukeGude    4.31"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#잠깐 전처리\n",
    "\n",
    "cols = ['beer_id','username','score']\n",
    "df = df[cols]\n",
    "df.rename(columns = {'beer_id':'beerid','username':'userid','score':'rating'}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:43.052209Z",
     "start_time": "2019-11-27T16:42:38.373155Z"
    }
   },
   "outputs": [],
   "source": [
    "#딥러닝에 이용할 컬럼 변경: user, beer, rating\n",
    "\n",
    "user_enc = LabelEncoder()\n",
    "uid = df['userid'].values\n",
    "df['user'] = user_enc.fit_transform(uid.tolist()) #array 인식이 안돼서 list로 빼냄.(userid의 경우만 오류 발생)\n",
    "n_users = df['user'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164935, 309542, 1.0, 5.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_enc = LabelEncoder()\n",
    "df['beer'] = item_enc.fit_transform(df['beerid'].values)\n",
    "n_beers = df['beer'].nunique()\n",
    "\n",
    "df['rating'] = df['rating'].values.astype(np.float32)\n",
    "min_rating = min(df['rating'])\n",
    "max_rating = max(df['rating'])\n",
    "\n",
    "n_users, n_beers, min_rating, max_rating #n_users, n_beers: 유니크한 값 개수, rating의 최솟값과 최댓값."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:46.866068Z",
     "start_time": "2019-11-27T16:42:46.855069Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>user</th>\n",
       "      <th>beer</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>102213</td>\n",
       "      <td>231507</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>94429</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>15635</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>34826</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>55007</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user    beer  rating\n",
       "0  102213  231507    4.03\n",
       "1   94429  112229    4.50\n",
       "2   15635  112229    4.75\n",
       "3   34826  112229    4.58\n",
       "4   55007  112229    4.31"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df[['user','beer','rating']] \n",
    "df2.head() #완료된 데이터셋. 여기서 'user','beer','rating'만 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:50.351854Z",
     "start_time": "2019-11-27T16:42:49.645695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7258502, 2), (1814626, 2), (7258502,), (1814626,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train, test 셋 분할: test size는 0.2, random_state는 임의로 지정\n",
    "X = df[['user', 'beer']].values\n",
    "y = df['rating'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:42:54.302743Z",
     "start_time": "2019-11-27T16:42:54.300743Z"
    }
   },
   "outputs": [],
   "source": [
    "#딥러닝 모델을 만들 때 필요한 또다른 변수는: 잠재요인의 개수를 정하는 것!\n",
    "#잠재요인의 개수는 원하는 대로 설정하면 되지만, 주의할 것은 user와 beer 모두에 대해 같은 사이즈를 줘야 한다는 점.\n",
    "\n",
    "#우리는 50개로 간다!(X에 대해서만 적용)\n",
    "n_factors = 50\n",
    "\n",
    "#X를 array로 만들기\n",
    "X_train_array = [X_train[:, 0], X_train[:, 1]]\n",
    "X_test_array = [X_test[:, 0], X_test[:, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>brewery_id</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>style</th>\n",
       "      <th>availability</th>\n",
       "      <th>abv</th>\n",
       "      <th>notes</th>\n",
       "      <th>retired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>202522</td>\n",
       "      <td>Olde Cogitator</td>\n",
       "      <td>2199</td>\n",
       "      <td>CA</td>\n",
       "      <td>US</td>\n",
       "      <td>English Oatmeal Stout</td>\n",
       "      <td>Rotating</td>\n",
       "      <td>7.3</td>\n",
       "      <td>No notes at this time.</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>82352</td>\n",
       "      <td>Konrads Stout Russian Imperial Stout</td>\n",
       "      <td>18604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>Russian Imperial Stout</td>\n",
       "      <td>Rotating</td>\n",
       "      <td>10.4</td>\n",
       "      <td>No notes at this time.</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>214879</td>\n",
       "      <td>Scottish Right</td>\n",
       "      <td>44306</td>\n",
       "      <td>IN</td>\n",
       "      <td>US</td>\n",
       "      <td>Scottish Ale</td>\n",
       "      <td>Year-round</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No notes at this time.</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>320009</td>\n",
       "      <td>MegaMeow Imperial Stout</td>\n",
       "      <td>4378</td>\n",
       "      <td>WA</td>\n",
       "      <td>US</td>\n",
       "      <td>American Imperial Stout</td>\n",
       "      <td>Winter</td>\n",
       "      <td>8.7</td>\n",
       "      <td>Every time this year</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>246438</td>\n",
       "      <td>Peaches-N-Cream</td>\n",
       "      <td>44617</td>\n",
       "      <td>PA</td>\n",
       "      <td>US</td>\n",
       "      <td>American Cream Ale</td>\n",
       "      <td>Rotating</td>\n",
       "      <td>5.1</td>\n",
       "      <td>No notes at this time.</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                                  name  brewery_id state country  \\\n",
       "0  202522                        Olde Cogitator        2199    CA      US   \n",
       "1   82352  Konrads Stout Russian Imperial Stout       18604   NaN      NO   \n",
       "2  214879                        Scottish Right       44306    IN      US   \n",
       "3  320009               MegaMeow Imperial Stout        4378    WA      US   \n",
       "4  246438                       Peaches-N-Cream       44617    PA      US   \n",
       "\n",
       "                     style availability   abv                   notes retired  \n",
       "0    English Oatmeal Stout     Rotating   7.3  No notes at this time.       f  \n",
       "1   Russian Imperial Stout     Rotating  10.4  No notes at this time.       f  \n",
       "2             Scottish Ale   Year-round   4.0  No notes at this time.       t  \n",
       "3  American Imperial Stout       Winter   8.7    Every time this year       f  \n",
       "4       American Cream Ale     Rotating   5.1  No notes at this time.       f  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers = pd.read_csv('beers.csv')\n",
    "beers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create a dictionary out of beerid, beer original title\n",
    "beers_dict = beers.set_index('id')['name'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##시각화(각 epoch마다의 MSE, MAE 값 비교)\n",
    "\n",
    "def plot_history(hist):\n",
    "    \n",
    "    plt.figure(figsize=(10,15))\n",
    "\n",
    "    plt.subplot(2,1,1)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Abs Error [rating]')\n",
    "    plt.plot(hist['epoch'], hist['mae'], label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mae'],label = 'Test Error')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Mean Square Error [$rating^2$]')\n",
    "    plt.plot(hist['epoch'], hist['mse'], label='Train Error')\n",
    "    plt.plot(hist['epoch'], hist['val_mse'],label = 'Test Error')\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-27T16:43:14.561307Z",
     "start_time": "2019-11-27T16:43:05.998377Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#keras 라이브러리 로드\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Reshape, Dot\n",
    "from keras.layers import Embedding, Dense, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.regularizers import l2\n",
    "from keras.layers import Add, Activation, Lambda, Concatenate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adadelta, relu+sigmoid, L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EmbeddingLayer:\n",
    "    def __init__(self, n_items, n_factors):\n",
    "        self.n_items = n_items\n",
    "        self.n_factors = n_factors\n",
    "    \n",
    "    def __call__(self, x):\n",
    "        x = Embedding(self.n_items, self.n_factors, embeddings_initializer='he_normal')(x) #initializer는 정규분포\n",
    "        x = Reshape((self.n_factors,))(x)\n",
    "        return x\n",
    "\n",
    "def RecommenderV18(n_users, n_beers, n_factors, min_rating, max_rating):\n",
    "    \n",
    "    #input: user, beer\n",
    "    user = Input(shape=(1,))\n",
    "    u = EmbeddingLayer(n_users, n_factors)(user) #u는 user * n_factor\n",
    "    ub = EmbeddingLayer(n_users, 1)(user) #user_bias\n",
    "    \n",
    "    beer = Input(shape=(1,))\n",
    "    b = EmbeddingLayer(n_beers, n_factors)(beer) #b은 beer * n_factor\n",
    "    bb = EmbeddingLayer(n_beers, 1)(beer) #beer_bias\n",
    "    \n",
    "    #input 연산- activation 2가지를 추가. relu와 sigmoid\n",
    "    x = Concatenate()([u, b])\n",
    "    \n",
    "    x = Dense(10, kernel_initializer='he_normal',kernel_regularizer=l2(0.001))(x)\n",
    "    x = Activation('relu')(x)\n",
    "    \n",
    "    x = Dense(1, kernel_initializer='he_normal',kernel_regularizer=l2(0.001))(x)\n",
    "    x = Activation('sigmoid')(x)\n",
    "    x = Lambda(lambda x: x * (max_rating - min_rating) + min_rating)(x)\n",
    "\n",
    "\n",
    "    #최종 모델 만들기(input은 user, beer 2개를 받고, output은 x를 반환.)\n",
    "    model = Model(inputs=[user, beer], outputs=x)\n",
    "    model.compile(loss=['mse'], metrics = ['mae','mse'] ,optimizer='Adadelta') \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1, 50)        8246750     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, 1, 50)        15477100    input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 50)           0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, 50)           0           embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 100)          0           reshape_1[0][0]                  \n",
      "                                                                 reshape_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 10)           1010        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 10)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1)            11          activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 1)            0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 1)            0           activation_2[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 23,724,871\n",
      "Trainable params: 23,724,871\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "re18model = RecommenderV18(n_users, n_beers, n_factors, min_rating, max_rating)\n",
    "re18model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\suyn3\\.conda\\envs\\tf0\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 7258502 samples, validate on 1814626 samples\n",
      "Epoch 1/10\n",
      "7258502/7258502 [==============================] - 2202s 303us/step - loss: 0.2254 - mae: 0.3253 - mse: 0.2095 - val_loss: 0.2056 - val_mae: 0.3107 - val_mse: 0.1926\n",
      "Epoch 2/10\n",
      "7258502/7258502 [==============================] - 2254s 311us/step - loss: 0.1989 - mae: 0.3054 - mse: 0.1863 - val_loss: 0.1993 - val_mae: 0.3057 - val_mse: 0.1874\n",
      "Epoch 3/10\n",
      "7258502/7258502 [==============================] - 2231s 307us/step - loss: 0.1929 - mae: 0.3010 - mse: 0.1812 - val_loss: 0.1962 - val_mae: 0.3045 - val_mse: 0.1850\n",
      "Epoch 4/10\n",
      "7258502/7258502 [==============================] - 2163s 298us/step - loss: 0.1893 - mae: 0.2984 - mse: 0.1781 - val_loss: 0.1943 - val_mae: 0.3029 - val_mse: 0.1836\n",
      "Epoch 5/10\n",
      "7258502/7258502 [==============================] - 2172s 299us/step - loss: 0.1868 - mae: 0.2966 - mse: 0.1760 - val_loss: 0.1933 - val_mae: 0.3024 - val_mse: 0.1826\n",
      "Epoch 6/10\n",
      "7258502/7258502 [==============================] - 2145s 296us/step - loss: 0.1849 - mae: 0.2952 - mse: 0.1743 - val_loss: 0.1923 - val_mae: 0.3007 - val_mse: 0.1819\n",
      "Epoch 7/10\n",
      "7258502/7258502 [==============================] - 2151s 296us/step - loss: 0.1834 - mae: 0.2941 - mse: 0.1730 - val_loss: 0.1921 - val_mae: 0.3023 - val_mse: 0.1818\n",
      "Epoch 8/10\n",
      "7258502/7258502 [==============================] - 2148s 296us/step - loss: 0.1821 - mae: 0.2932 - mse: 0.1720 - val_loss: 0.1909 - val_mae: 0.3004 - val_mse: 0.1810\n",
      "Epoch 9/10\n",
      "7258502/7258502 [==============================] - 2159s 297us/step - loss: 0.1811 - mae: 0.2925 - mse: 0.1710 - val_loss: 0.1907 - val_mae: 0.2991 - val_mse: 0.1809\n",
      "Epoch 10/10\n",
      "7258502/7258502 [==============================] - 2173s 299us/step - loss: 0.1801 - mae: 0.2918 - mse: 0.1702 - val_loss: 0.1905 - val_mae: 0.3009 - val_mse: 0.1807\n"
     ]
    }
   ],
   "source": [
    "## fit model\n",
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "history_mf = re18model.fit(x=X_train_array, y=y_train, batch_size=1000, epochs=10,\n",
    "                    verbose=1, validation_data=(X_test_array, y_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.205605</td>\n",
       "      <td>0.310741</td>\n",
       "      <td>0.192559</td>\n",
       "      <td>0.225381</td>\n",
       "      <td>0.325345</td>\n",
       "      <td>0.209488</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.199278</td>\n",
       "      <td>0.305735</td>\n",
       "      <td>0.187450</td>\n",
       "      <td>0.198945</td>\n",
       "      <td>0.305441</td>\n",
       "      <td>0.186341</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.196238</td>\n",
       "      <td>0.304538</td>\n",
       "      <td>0.185037</td>\n",
       "      <td>0.192905</td>\n",
       "      <td>0.300977</td>\n",
       "      <td>0.181183</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.194350</td>\n",
       "      <td>0.302882</td>\n",
       "      <td>0.183588</td>\n",
       "      <td>0.189327</td>\n",
       "      <td>0.298365</td>\n",
       "      <td>0.178115</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.193279</td>\n",
       "      <td>0.302358</td>\n",
       "      <td>0.182648</td>\n",
       "      <td>0.186822</td>\n",
       "      <td>0.296553</td>\n",
       "      <td>0.175966</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.192258</td>\n",
       "      <td>0.300747</td>\n",
       "      <td>0.181926</td>\n",
       "      <td>0.184937</td>\n",
       "      <td>0.295226</td>\n",
       "      <td>0.174346</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.192112</td>\n",
       "      <td>0.302290</td>\n",
       "      <td>0.181757</td>\n",
       "      <td>0.183412</td>\n",
       "      <td>0.294133</td>\n",
       "      <td>0.173038</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.190924</td>\n",
       "      <td>0.300398</td>\n",
       "      <td>0.181030</td>\n",
       "      <td>0.182150</td>\n",
       "      <td>0.293245</td>\n",
       "      <td>0.171959</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.190660</td>\n",
       "      <td>0.299107</td>\n",
       "      <td>0.180899</td>\n",
       "      <td>0.181069</td>\n",
       "      <td>0.292504</td>\n",
       "      <td>0.171036</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.190459</td>\n",
       "      <td>0.300925</td>\n",
       "      <td>0.180665</td>\n",
       "      <td>0.180102</td>\n",
       "      <td>0.291826</td>\n",
       "      <td>0.170215</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   val_loss   val_mae   val_mse      loss       mae       mse  epoch\n",
       "0  0.205605  0.310741  0.192559  0.225381  0.325345  0.209488      0\n",
       "1  0.199278  0.305735  0.187450  0.198945  0.305441  0.186341      1\n",
       "2  0.196238  0.304538  0.185037  0.192905  0.300977  0.181183      2\n",
       "3  0.194350  0.302882  0.183588  0.189327  0.298365  0.178115      3\n",
       "4  0.193279  0.302358  0.182648  0.186822  0.296553  0.175966      4\n",
       "5  0.192258  0.300747  0.181926  0.184937  0.295226  0.174346      5\n",
       "6  0.192112  0.302290  0.181757  0.183412  0.294133  0.173038      6\n",
       "7  0.190924  0.300398  0.181030  0.182150  0.293245  0.171959      7\n",
       "8  0.190660  0.299107  0.180899  0.181069  0.292504  0.171036      8\n",
       "9  0.190459  0.300925  0.180665  0.180102  0.291826  0.170215      9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## show loss & mae & mse at each epoch\n",
    "hist18 = pd.DataFrame(history_mf.history)\n",
    "hist18['epoch'] = history_mf.epoch\n",
    "hist18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814626/1814626 [==============================] - 6s 3us/step\n",
      "## evaluation loss and_metrics ##\n",
      "[0.19045906005374788, 0.3009251654148102, 0.18066465854644775]\n"
     ]
    }
   ],
   "source": [
    "#모델 평가하기; model.evaluate()\n",
    "loss_and_metrics = re18model.evaluate(X_test_array, y_test, batch_size=1000)\n",
    "print('## evaluation loss and_metrics ##')\n",
    "print(loss_and_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(hist18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unbeer_keras(df, userid):\n",
    "    beers = df[df['user']==userid]['beer'].unique().tolist() #해당 유저가 평가한 beer\n",
    "    total_beers = df['beer'].unique().tolist() #모든 beer\n",
    "    unbeers = [beer for beer in total_beers if beer not in beers] #해당 유저가 평가안한 beer\n",
    "    return unbeers\n",
    "\n",
    "def recomm_beer_by_keras(userid, model, top_n):\n",
    "    unbeers = get_unbeer_keras(df,userid)\n",
    "    predict =[v[0][0] for v in  [model.predict([[userid],[beer]]) for beer in unbeers]]\n",
    "    ndf= pd.DataFrame({'beer':unbeers,'rating':predict})\n",
    "    final = ndf.sort_values(by='rating', ascending=False)[:top_n]\n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4min 51s\n"
     ]
    },
    {
     "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>beer</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>186538</td>\n",
       "      <td>68022</td>\n",
       "      <td>4.821929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191828</td>\n",
       "      <td>52574</td>\n",
       "      <td>4.785089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210211</td>\n",
       "      <td>122288</td>\n",
       "      <td>4.777422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85480</td>\n",
       "      <td>86880</td>\n",
       "      <td>4.764430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108760</td>\n",
       "      <td>34828</td>\n",
       "      <td>4.761798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12954</td>\n",
       "      <td>76214</td>\n",
       "      <td>4.753008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169444</td>\n",
       "      <td>98340</td>\n",
       "      <td>4.751093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187446</td>\n",
       "      <td>58238</td>\n",
       "      <td>4.744469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83413</td>\n",
       "      <td>164241</td>\n",
       "      <td>4.743034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109217</td>\n",
       "      <td>80528</td>\n",
       "      <td>4.737667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          beer    rating\n",
       "186538   68022  4.821929\n",
       "191828   52574  4.785089\n",
       "210211  122288  4.777422\n",
       "85480    86880  4.764430\n",
       "108760   34828  4.761798\n",
       "12954    76214  4.753008\n",
       "169444   98340  4.751093\n",
       "187446   58238  4.744469\n",
       "83413   164241  4.743034\n",
       "109217   80528  4.737667"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "unbeers = get_unbeer_keras(df2, 190)\n",
    "recomm_beer_by_keras(190, re18model, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309542, 50)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## get weights of the beers embedding matrix\n",
    "beer_embedding_weights = re18model.layers[3].get_weights()[0] #3은 beer-embedding : layer 확인!\n",
    "beer_embedding_weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 309542)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## import PCA\n",
    "pca = PCA(n_components = 3) ## use 3 components\n",
    "beer_embedding_weights_t = np.transpose(beer_embedding_weights) ## pass the transpose of the embedding matrix\n",
    "beer_pca = pca.fit(beer_embedding_weights_t) ## fit\n",
    "\n",
    "## display the resulting matrix dimensions\n",
    "beer_pca.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85195583, 0.03671474, 0.02450253], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## display the variance explained by the 3 components\n",
    "beer_pca.explained_variance_ratio_\n",
    " #variance 가 아주 낮으면 좋은 해석을 기대하기 어렵다.\n",
    "    #첫번째 PC 분산은 약 얼마? 78%로, dropout 규제를 넣었는데도 성능이 좋다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter\n",
    "\n",
    "## extract first PCA\n",
    "pca0 = beer_pca.components_[0]\n",
    "\n",
    "## get the value (pca0, beer title)\n",
    "beer_comp0 = [(f, beers_dict[i]) for f,i in zip(pca0, list(beers_dict.keys()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.016340973, 'Dirty Bastard - Bourbon-Soaked Apricots And Ginger'),\n",
       " (0.01593608, 'Sir Phobos'),\n",
       " (0.014925864, 'Bowie'),\n",
       " (0.01490037, 'Ahhh You Again'),\n",
       " (0.014755908, 'Grid Iron Irish Red'),\n",
       " (0.014744917, \"Weekend At Brett's Barrel Aged\"),\n",
       " (0.014719812, 'Pomegranate kolsch'),\n",
       " (0.014440763, 'Rolo Tony Brown Town'),\n",
       " (0.014308332, 'Sunday Driving'),\n",
       " (0.0141425105, 'Andre the Brut')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## beers corresponding to the highest values of pca0\n",
    "sorted(beer_comp0, key = itemgetter(0), reverse = True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.0154747, 'Snowman Make Like A Tree And Leaf Maple Porter'),\n",
       " (-0.013042646, 'Jolly Golden Ale'),\n",
       " (-0.013013942, \"Monk's Phunk\"),\n",
       " (-0.012685816, 'Saison Garni'),\n",
       " (-0.012292439, 'Buckeye Beer'),\n",
       " (-0.012289422, 'Full Rosenberry'),\n",
       " (-0.012263286, 'Hop De Mensenge'),\n",
       " (-0.012157698, 'Bare Tree Weiss Wine Vintage 2003'),\n",
       " (-0.01189573, 'Handlebar Hefe'),\n",
       " (-0.011881831, 'One-Eyed Jack Imperial Pilsner')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## beers corresponding to the lowest values of pca0\n",
    "sorted(beer_comp0, key = itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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