06-3. Adam, relu+sigmoid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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('D:/stat_team/project/beers-breweries-and-beer-reviews/reviews.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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",
       "      <th>0</th>\n",
       "      <td>271781</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>125646</td>\n",
       "      <td>_dirty_</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125646</td>\n",
       "      <td>CJDUBYA</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125646</td>\n",
       "      <td>GratefulBeerGuy</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164935, 309542, 1.0, 5.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#딥러닝에 이용할 컬럼 변경: user, beer, rating\n",
    "\n",
    "user_enc = LabelEncoder()\n",
    "df['user'] = user_enc.fit_transform(df['userid'].tolist())\n",
    "n_users = df['user'].nunique()\n",
    "\n",
    "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": 5,
   "metadata": {},
   "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>user</th>\n",
       "      <th>beer</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102213</td>\n",
       "      <td>231507</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94429</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15635</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34826</td>\n",
       "      <td>112229</td>\n",
       "      <td>4.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7258502, 2), (1814626, 2), (7258502,), (1814626,))"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "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": 8,
   "metadata": {},
   "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, Flatten, Dense, Dropout, concatenate, multiply, Input\n",
    "from keras.optimizers import Adam\n",
    "from keras.regularizers import l2\n",
    "from keras.layers import Add, Activation, Lambda, Concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## ver3. relu->sigmoid , test MSE: 0.1827, 과적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RecommenderV5_3(n_users, n_beers, n_factors):\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')(x)\n",
    "    x = Activation('relu')(x)\n",
    "    \n",
    "    x = Dense(1, kernel_initializer='he_normal')(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_3 = Model(inputs=[user, beer], outputs=x)\n",
    "    opt = Adam(lr=0.001) #learning rate(학습률) 설정.\n",
    "    model_3.compile(loss=['mse'], metrics = ['mae','mse'] ,optimizer=opt) \n",
    "    return model_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_11 (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_12 (InputLayer)           (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_21 (Embedding)        (None, 1, 50)        8246750     input_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "embedding_23 (Embedding)        (None, 1, 50)        15477100    input_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_21 (Reshape)            (None, 50)           0           embedding_21[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "reshape_23 (Reshape)            (None, 50)           0           embedding_23[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 100)          0           reshape_21[0][0]                 \n",
      "                                                                 reshape_23[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 10)           1010        concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 10)           0           dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 1)            11          activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 1)            0           dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_6 (Lambda)               (None, 1)            0           activation_13[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 23,724,871\n",
      "Trainable params: 23,724,871\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "re5model_3 = RecommenderV5_3(n_users, n_beers, n_factors)\n",
    "re5model_3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7258502 samples, validate on 1814626 samples\n",
      "Epoch 1/10\n",
      "7258502/7258502 [==============================] - 4290s 591us/step - loss: 0.2009 - mae: 0.3186 - mse: 0.2009 - val_loss: 0.1811 - val_mae: 0.3004 - val_mse: 0.1811\n",
      "Epoch 2/10\n",
      "7258502/7258502 [==============================] - 4419s 609us/step - loss: 0.1701 - mae: 0.2909 - mse: 0.1701 - val_loss: 0.1771 - val_mae: 0.2973 - val_mse: 0.1771\n",
      "Epoch 3/10\n",
      "7258502/7258502 [==============================] - 4373s 602us/step - loss: 0.1541 - mae: 0.2756 - mse: 0.1541 - val_loss: 0.1768 - val_mae: 0.2955 - val_mse: 0.1768\n",
      "Epoch 5/10\n",
      "7258502/7258502 [==============================] - 4389s 605us/step - loss: 0.1491 - mae: 0.2707 - mse: 0.1491 - val_loss: 0.1776 - val_mae: 0.2960 - val_mse: 0.1776\n",
      "Epoch 6/10\n",
      "7258502/7258502 [==============================] - 4508s 621us/step - loss: 0.1420 - mae: 0.2638 - mse: 0.1420 - val_loss: 0.1805 - val_mae: 0.2983 - val_mse: 0.1805\n",
      "Epoch 8/10\n",
      "7258502/7258502 [==============================] - 4510s 621us/step - loss: 0.1394 - mae: 0.2612 - mse: 0.1394 - val_loss: 0.1811 - val_mae: 0.3001 - val_mse: 0.1811\n",
      "Epoch 9/10\n",
      "7258502/7258502 [==============================] - 4614s 636us/step - loss: 0.1373 - mae: 0.2590 - mse: 0.1373 - val_loss: 0.1817 - val_mae: 0.2998 - val_mse: 0.1817\n",
      "Epoch 10/10\n",
      "7258502/7258502 [==============================] - 4722s 651us/step - loss: 0.1355 - mae: 0.2572 - mse: 0.1355 - val_loss: 0.1827 - val_mae: 0.3005 - val_mse: 0.1827\n"
     ]
    }
   ],
   "source": [
    "## fit model\n",
    "import numpy as np\n",
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "history_mf5_3 = re5model_3.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": 40,
   "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",
       "      <th>0</th>\n",
       "      <td>0.181086</td>\n",
       "      <td>0.300400</td>\n",
       "      <td>0.181086</td>\n",
       "      <td>0.200857</td>\n",
       "      <td>0.318599</td>\n",
       "      <td>0.200858</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.177150</td>\n",
       "      <td>0.297258</td>\n",
       "      <td>0.177150</td>\n",
       "      <td>0.170104</td>\n",
       "      <td>0.290928</td>\n",
       "      <td>0.170104</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.176283</td>\n",
       "      <td>0.295229</td>\n",
       "      <td>0.176283</td>\n",
       "      <td>0.160662</td>\n",
       "      <td>0.281928</td>\n",
       "      <td>0.160662</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.176814</td>\n",
       "      <td>0.295498</td>\n",
       "      <td>0.176814</td>\n",
       "      <td>0.154083</td>\n",
       "      <td>0.275602</td>\n",
       "      <td>0.154083</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.177609</td>\n",
       "      <td>0.295997</td>\n",
       "      <td>0.177609</td>\n",
       "      <td>0.149051</td>\n",
       "      <td>0.270729</td>\n",
       "      <td>0.149051</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.178975</td>\n",
       "      <td>0.297276</td>\n",
       "      <td>0.178975</td>\n",
       "      <td>0.145133</td>\n",
       "      <td>0.266900</td>\n",
       "      <td>0.145133</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.180542</td>\n",
       "      <td>0.298349</td>\n",
       "      <td>0.180542</td>\n",
       "      <td>0.141962</td>\n",
       "      <td>0.263762</td>\n",
       "      <td>0.141962</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.181133</td>\n",
       "      <td>0.300128</td>\n",
       "      <td>0.181133</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>0.261183</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.181697</td>\n",
       "      <td>0.299821</td>\n",
       "      <td>0.181698</td>\n",
       "      <td>0.137299</td>\n",
       "      <td>0.259011</td>\n",
       "      <td>0.137300</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.182746</td>\n",
       "      <td>0.300466</td>\n",
       "      <td>0.182746</td>\n",
       "      <td>0.135517</td>\n",
       "      <td>0.257172</td>\n",
       "      <td>0.135517</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  0.181086  0.300400  0.181086  0.200857  0.318599  0.200858      0\n",
       "1  0.177150  0.297258  0.177150  0.170104  0.290928  0.170104      1\n",
       "2  0.176283  0.295229  0.176283  0.160662  0.281928  0.160662      2\n",
       "3  0.176814  0.295498  0.176814  0.154083  0.275602  0.154083      3\n",
       "4  0.177609  0.295997  0.177609  0.149051  0.270729  0.149051      4\n",
       "5  0.178975  0.297276  0.178975  0.145133  0.266900  0.145133      5\n",
       "6  0.180542  0.298349  0.180542  0.141962  0.263762  0.141962      6\n",
       "7  0.181133  0.300128  0.181133  0.139445  0.261183  0.139445      7\n",
       "8  0.181697  0.299821  0.181698  0.137299  0.259011  0.137300      8\n",
       "9  0.182746  0.300466  0.182746  0.135517  0.257172  0.135517      9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## show loss & mae & mse at each epoch\n",
    "hist5_3 = pd.DataFrame(history_mf5_3.history)\n",
    "hist5_3['epoch'] = history_mf5_3.epoch\n",
    "hist5_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814626/1814626 [==============================] - 6s 3us/step\n",
      "## evaluation loss and_metrics ##\n",
      "[0.18274563826142814, 0.30046579241752625, 0.18274563550949097]\n"
     ]
    }
   ],
   "source": [
    "#모델 평가하기; model.evaluate() #test set에 평가했을 때, 맨 마지막 epoch의 val_loss, val_mae, val_mse\n",
    "loss_and_metrics_3 = re5model_3.evaluate(X_test_array, y_test, batch_size=1000)\n",
    "print('## evaluation loss and_metrics ##')\n",
    "print(loss_and_metrics_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309542, 50)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## get weights of the beers embedding matrix\n",
    "beer_embedding_weights_3 = re5model_3.layers[3].get_weights()[0] #3은 beer-embedding : layer 확인!\n",
    "beer_embedding_weights_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 309542)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## import PCA\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components = 3) ## use 3 components\n",
    "beer_embedding_weights_t_3 = np.transpose(beer_embedding_weights_3) ## pass the transpose of the embedding matrix\n",
    "beer_pca_3 = pca.fit(beer_embedding_weights_t_3) ## fit\n",
    "\n",
    "## display the resulting matrix dimensions\n",
    "beer_pca_3.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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(hist5_3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}