007. wide-deep-RecSys model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 블로그 설명\n",
    "\n",
    "해당 자료에 대한 설명은 아래 블로그에 올려두었습니다.\n",
    "- https://lsjsj92.tistory.com/597"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures, LabelEncoder\n",
    "from keras.layers import Input, Embedding, Dense, Flatten, Dropout, SpatialDropout1D, Activation, concatenate\n",
    "from keras.optimizers import Adam, SGD\n",
    "from keras.layers.advanced_activations import ReLU, PReLU, LeakyReLU, ELU\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.models import Model\n",
    "from tensorflow.keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLUMNS = [\n",
    "    \"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\", \"marital_status\", \n",
    "    \"occupation\", \"relationship\", \"race\", \"gender\", \"capital_gain\", \"capital_loss\", \n",
    "    \"hours_per_week\", \"native_country\", \"income_bracket\"\n",
    "]\n",
    "\n",
    "CATEGORICAL_COLUMNS = [\n",
    "    \"workclass\", \"education\", \"marital_status\", \"occupation\", \"relationship\", \n",
    "    \"race\", \"gender\", \"native_country\"\n",
    "]\n",
    "\n",
    "CONTINUOUS_COLUMNS = [\n",
    "    \"age\", \"education_num\", \"capital_gain\", \"capital_loss\", \"hours_per_week\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('./data/adult.data', names=COLUMNS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32561, 15)\n"
     ]
    },
    {
     "data": {
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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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country income_bracket  \n",
       "0          2174             0              40   United-States          <=50K  \n",
       "1             0             0              13   United-States          <=50K  \n",
       "2             0             0              40   United-States          <=50K  \n",
       "3             0             0              40   United-States          <=50K  \n",
       "4             0             0              40            Cuba          <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "gender            0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "income_bracket    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('./data/adult.test', skiprows=1, names=COLUMNS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16281, 15)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   workclass  fnlwgt      education  education_num       marital_status  \\\n",
       "0   25     Private  226802           11th              7        Never-married   \n",
       "1   38     Private   89814        HS-grad              9   Married-civ-spouse   \n",
       "2   28   Local-gov  336951     Assoc-acdm             12   Married-civ-spouse   \n",
       "3   44     Private  160323   Some-college             10   Married-civ-spouse   \n",
       "4   18           ?  103497   Some-college             10        Never-married   \n",
       "\n",
       "           occupation relationship    race   gender  capital_gain  \\\n",
       "0   Machine-op-inspct    Own-child   Black     Male             0   \n",
       "1     Farming-fishing      Husband   White     Male             0   \n",
       "2     Protective-serv      Husband   White     Male             0   \n",
       "3   Machine-op-inspct      Husband   Black     Male          7688   \n",
       "4                   ?    Own-child   White   Female             0   \n",
       "\n",
       "   capital_loss  hours_per_week  native_country income_bracket  \n",
       "0             0              40   United-States         <=50K.  \n",
       "1             0              50   United-States         <=50K.  \n",
       "2             0              40   United-States          >50K.  \n",
       "3             0              40   United-States          >50K.  \n",
       "4             0              30   United-States         <=50K.  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_data.shape)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education_num     0\n",
       "marital_status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "gender            0\n",
       "capital_gain      0\n",
       "capital_loss      0\n",
       "hours_per_week    0\n",
       "native_country    0\n",
       "income_bracket    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([train_data, test_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48842, 15)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country income_bracket  \n",
       "0          2174             0              40   United-States          <=50K  \n",
       "1             0             0              13   United-States          <=50K  \n",
       "2             0             0              40   United-States          <=50K  \n",
       "3             0             0              40   United-States          <=50K  \n",
       "4             0             0              40            Cuba          <=50K  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 라벨 값 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['label'] = data['income_bracket'].apply(lambda x : \">50K\" in x).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income_bracket</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age   workclass  fnlwgt      education  education_num  \\\n",
       "10   37     Private  280464   Some-college             10   \n",
       "11   30   State-gov  141297      Bachelors             13   \n",
       "12   23     Private  122272      Bachelors             13   \n",
       "13   32     Private  205019     Assoc-acdm             12   \n",
       "14   40     Private  121772      Assoc-voc             11   \n",
       "\n",
       "         marital_status        occupation    relationship  \\\n",
       "10   Married-civ-spouse   Exec-managerial         Husband   \n",
       "11   Married-civ-spouse    Prof-specialty         Husband   \n",
       "12        Never-married      Adm-clerical       Own-child   \n",
       "13        Never-married             Sales   Not-in-family   \n",
       "14   Married-civ-spouse      Craft-repair         Husband   \n",
       "\n",
       "                   race   gender  capital_gain  capital_loss  hours_per_week  \\\n",
       "10                Black     Male             0             0              80   \n",
       "11   Asian-Pac-Islander     Male             0             0              40   \n",
       "12                White   Female             0             0              30   \n",
       "13                Black     Male             0             0              50   \n",
       "14   Asian-Pac-Islander     Male             0             0              40   \n",
       "\n",
       "    native_country income_bracket  label  \n",
       "10   United-States           >50K      1  \n",
       "11           India           >50K      1  \n",
       "12   United-States          <=50K      0  \n",
       "13   United-States          <=50K      0  \n",
       "14               ?           >50K      1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('income_bracket', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country  label  \n",
       "0          2174             0              40   United-States      0  \n",
       "1             0             0              13   United-States      0  \n",
       "2             0             0              40   United-States      0  \n",
       "3             0             0              40   United-States      0  \n",
       "4             0             0              40            Cuba      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['label'].values\n",
    "data.drop('label', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>39</td>\n",
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       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
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       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country  \n",
       "0          2174             0              40   United-States  \n",
       "1             0             0              13   United-States  \n",
       "2             0             0              40   United-States  \n",
       "3             0             0              40   United-States  \n",
       "4             0             0              40            Cuba  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 카테고리 값들을 숫자로 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in CATEGORICAL_COLUMNS:\n",
    "    le = LabelEncoder()\n",
    "    data[c] = le.fit_transform(data[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>77516</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>83311</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>215646</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>234721</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>338409</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt  education  education_num  marital_status  \\\n",
       "0   39          7   77516          9             13               4   \n",
       "1   50          6   83311          9             13               2   \n",
       "2   38          4  215646         11              9               0   \n",
       "3   53          4  234721          1              7               2   \n",
       "4   28          4  338409          9             13               2   \n",
       "\n",
       "   occupation  relationship  race  gender  capital_gain  capital_loss  \\\n",
       "0           1             1     4       1          2174             0   \n",
       "1           4             0     4       1             0             0   \n",
       "2           6             1     4       1             0             0   \n",
       "3           6             0     2       1             0             0   \n",
       "4          10             5     2       0             0             0   \n",
       "\n",
       "   hours_per_week  native_country  \n",
       "0              40              39  \n",
       "1              13              39  \n",
       "2              40              39  \n",
       "3              40              39  \n",
       "4              40               5  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다시 trian, test로 구분 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:train_size]\n",
    "y_train = y[:train_size]\n",
    "X_test = data.iloc[train_size:]\n",
    "y_test = y[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32561 32561 16281 16281\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), len(y_train), len(X_test), len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 카테고리 값들과 연속값들을 뽑아냄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_category = np.array(X_train[CATEGORICAL_COLUMNS])\n",
    "X_test_category = np.array(X_test[CATEGORICAL_COLUMNS])\n",
    "X_train_countinue = np.array(X_train[CONTINUOUS_COLUMNS], dtype='float64')\n",
    "X_test_countinue = np.array(X_test[CONTINUOUS_COLUMNS], dtype='float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7  9  4  1  1  4  1 39]]\n",
      "[[ 4  1  4  7  3  2  1 39]]\n",
      "[[  39.   13. 2174.    0.   40.]]\n",
      "[[25.  7.  0.  0. 40.]]\n"
     ]
    }
   ],
   "source": [
    "print( X_train_category[:1] )\n",
    "print(X_test_category[:1])\n",
    "print(X_train_countinue[:1])\n",
    "print(X_test_countinue[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정규화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_countinue = scaler.fit_transform(X_train_countinue)\n",
    "X_test_countinue = scaler.transform(X_test_countinue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.03067056  1.13473876  0.1484529  -0.21665953 -0.03542945]]\n",
      "[[-0.99570562 -1.19745882 -0.14592048 -0.21665953 -0.03542945]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_countinue[:1])\n",
    "print(X_test_countinue[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial 하게 바꿔줌 (비선형적인 설정으로 선형 회귀를 확장하는 방법. 즉 다항식 함수로 바꿔줌)\n",
    "\n",
    "카테고리 값을 Polynomial로 바꿔준다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=2, interaction_only=True)\n",
    "X_train_category_poly = poly.fit_transform(X_train_category)\n",
    "X_test_category_poly = poly.transform(X_test_category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.   7.   9.   4.   1.   1.   4.   1.  39.  63.  28.   7.   7.  28.\n",
      "    7. 273.  36.   9.   9.  36.   9. 351.   4.   4.  16.   4. 156.   1.\n",
      "    4.   1.  39.   4.   1.  39.   4. 156.  39.]]\n",
      "[[  1.   4.   1.   4.   7.   3.   2.   1.  39.   4.  16.  28.  12.   8.\n",
      "    4. 156.   4.   7.   3.   2.   1.  39.  28.  12.   8.   4. 156.  21.\n",
      "   14.   7. 273.   6.   3. 117.   2.  78.  39.]]\n"
     ]
    }
   ],
   "source": [
    "print( X_train_category_poly[:1] )\n",
    "print(X_test_category_poly[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 2. 3. 2. 3. 6.]]\n"
     ]
    }
   ],
   "source": [
    "n = np.array([[1,2,3]])\n",
    "poly2 = PolynomialFeatures(degree=2, interaction_only=True)\n",
    "print(poly.fit_transform(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 설계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_deep_model():\n",
    "\n",
    "    category_inputs = []\n",
    "    category_embeds = []\n",
    "    # 카테고리컬 데이터 임베딩\n",
    "    for i in range(len(CATEGORICAL_COLUMNS)):\n",
    "        input_i = Input(shape=(1,), dtype='int32')\n",
    "        dim = len(np.unique(data[CATEGORICAL_COLUMNS[i]]))\n",
    "        embed_dim = int(np.ceil(dim ** 0.5)) # embedding 차원을 0.5배 정도로 해서 한다.\n",
    "        embed_i = Embedding(dim, embed_dim, input_length=1)(input_i)\n",
    "        flatten_i = Flatten()(embed_i)\n",
    "        category_inputs.append(input_i)\n",
    "        category_embeds.append(flatten_i)\n",
    "    # continuous 데이터 input\n",
    "    continue_input = Input(shape=(len(CONTINUOUS_COLUMNS),))\n",
    "    continue_dense = Dense(256, use_bias=False)(continue_input)\n",
    "    # category와 continue를 합침\n",
    "    concat_embeds = concatenate([continue_dense] + category_embeds)\n",
    "    concat_embeds = Activation('relu')(concat_embeds)\n",
    "    bn_concat = BatchNormalization()(concat_embeds)\n",
    "\n",
    "    fc1 = Dense(512, use_bias=False)(bn_concat)\n",
    "    relu1 = ReLU()(fc1)\n",
    "    bn1 = BatchNormalization()(relu1)\n",
    "    fc2 = Dense(256, use_bias=False)(bn1)\n",
    "    relu2 = ReLU()(fc2)\n",
    "    bn2 = BatchNormalization()(relu2)\n",
    "    fc3 = Dense(128)(bn2)\n",
    "    relu3 = ReLU()(fc3)\n",
    "    \n",
    "    return category_inputs, continue_input, relu3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wide_model():\n",
    "    dim = X_train_category_poly.shape[1]\n",
    "    return Input(shape=(dim,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_inputs, continue_input, deep_model = get_deep_model()\n",
    "wide_model = get_wide_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## wide 모델과 deep model을 합쳐준다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_layer = concatenate([deep_model, wide_model])\n",
    "inputs = [continue_input] + category_inputs + [wide_model]\n",
    "output = Dense(1, activation='sigmoid')(out_layer)\n",
    "model = Model(inputs=inputs, outputs=output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_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",
      "input_3 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_4 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_5 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_6 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_7 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_8 (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_9 (InputLayer)            [(None, 5)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 1, 3)         27          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 1, 4)         64          input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 1, 3)         21          input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, 1, 4)         60          input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_4 (Embedding)         (None, 1, 3)         18          input_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_5 (Embedding)         (None, 1, 3)         15          input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_6 (Embedding)         (None, 1, 2)         4           input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_7 (Embedding)         (None, 1, 7)         294         input_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          1280        input_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 3)            0           embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 4)            0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 3)            0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 4)            0           embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 3)            0           embedding_4[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)             (None, 3)            0           embedding_5[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_6 (Flatten)             (None, 2)            0           embedding_6[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_7 (Flatten)             (None, 7)            0           embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 285)          0           dense[0][0]                      \n",
      "                                                                 flatten[0][0]                    \n",
      "                                                                 flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "                                                                 flatten_4[0][0]                  \n",
      "                                                                 flatten_5[0][0]                  \n",
      "                                                                 flatten_6[0][0]                  \n",
      "                                                                 flatten_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 285)          0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 285)          1140        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 512)          145920      batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "re_lu (ReLU)                    (None, 512)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 512)          2048        re_lu[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          131072      batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_1 (ReLU)                  (None, 256)          0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 256)          1024        re_lu_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 128)          32896       batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "re_lu_2 (ReLU)                  (None, 128)          0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_10 (InputLayer)           [(None, 37)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 165)          0           re_lu_2[0][0]                    \n",
      "                                                                 input_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1)            166         concatenate_1[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 316,049\n",
      "Trainable params: 313,943\n",
      "Non-trainable params: 2,106\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = ModelCheckpoint(filepath='./data/wide-deep.h5', monitor='val_loss', verbose=1, save_best_only=True)\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 입력 데이터\n",
    "\n",
    "- conti 데이터 -> category 데이터 -> poly data 순으로 입력으로 넣어준다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = [X_train_countinue] + [X_train_category[:, i] for i in range(X_train_category.shape[1])] + [X_train_category_poly]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 30\n",
    "optimizer ='adam'\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "212/217 [============================>.] - ETA: 0s - loss: 3.8606 - accuracy: 0.7990\n",
      "Epoch 00001: val_loss improved from inf to 2.42448, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 3.8025 - accuracy: 0.7989 - val_loss: 2.4245 - val_accuracy: 0.6692\n",
      "Epoch 2/30\n",
      "211/217 [============================>.] - ETA: 0s - loss: 0.6523 - accuracy: 0.8123\n",
      "Epoch 00002: val_loss improved from 2.42448 to 0.88378, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.6471 - accuracy: 0.8129 - val_loss: 0.8838 - val_accuracy: 0.7204\n",
      "Epoch 3/30\n",
      "211/217 [============================>.] - ETA: 0s - loss: 0.4355 - accuracy: 0.8259\n",
      "Epoch 00003: val_loss improved from 0.88378 to 0.40923, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.4342 - accuracy: 0.8263 - val_loss: 0.4092 - val_accuracy: 0.8174\n",
      "Epoch 4/30\n",
      "210/217 [============================>.] - ETA: 0s - loss: 0.3841 - accuracy: 0.8345\n",
      "Epoch 00004: val_loss improved from 0.40923 to 0.35323, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3831 - accuracy: 0.8346 - val_loss: 0.3532 - val_accuracy: 0.8465\n",
      "Epoch 5/30\n",
      "207/217 [===========================>..] - ETA: 0s - loss: 0.3632 - accuracy: 0.8392\n",
      "Epoch 00005: val_loss did not improve from 0.35323\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3621 - accuracy: 0.8391 - val_loss: 0.3594 - val_accuracy: 0.8430\n",
      "Epoch 6/30\n",
      "210/217 [============================>.] - ETA: 0s - loss: 0.3545 - accuracy: 0.8398\n",
      "Epoch 00006: val_loss improved from 0.35323 to 0.34202, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3541 - accuracy: 0.8404 - val_loss: 0.3420 - val_accuracy: 0.8483\n",
      "Epoch 7/30\n",
      "212/217 [============================>.] - ETA: 0s - loss: 0.3407 - accuracy: 0.8454\n",
      "Epoch 00007: val_loss improved from 0.34202 to 0.33910, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3405 - accuracy: 0.8452 - val_loss: 0.3391 - val_accuracy: 0.8430\n",
      "Epoch 8/30\n",
      "206/217 [===========================>..] - ETA: 0s - loss: 0.3417 - accuracy: 0.8427\n",
      "Epoch 00008: val_loss did not improve from 0.33910\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3432 - accuracy: 0.8415 - val_loss: 0.3484 - val_accuracy: 0.8346\n",
      "Epoch 9/30\n",
      "214/217 [============================>.] - ETA: 0s - loss: 0.3322 - accuracy: 0.8470\n",
      "Epoch 00009: val_loss improved from 0.33910 to 0.31798, saving model to ./data/wide-deep.h5\n",
      "217/217 [==============================] - 1s 4ms/step - loss: 0.3315 - accuracy: 0.8474 - val_loss: 0.3180 - val_accuracy: 0.8524\n",
      "Epoch 10/30\n",
      "207/217 [===========================>..] - ETA: 0s - loss: 0.3312 - accuracy: 0.8470\n",
      "Epoch 00010: val_loss did not improve from 0.31798\n",
      "217/217 [==============================] - 1s 3ms/step - loss: 0.3320 - accuracy: 0.8469 - val_loss: 0.3434 - val_accuracy: 0.8465\n",
      "Epoch 11/30\n",
      "206/217 [===========================>..] - ETA: 0s - loss: 0.3227 - accuracy: 0.8503\n",
      "Epoch 00011: val_loss did not improve from 0.31798\n",
      "217/217 [==============================] - 1s 3ms/step - loss: 0.3236 - accuracy: 0.8497 - val_loss: 0.3266 - val_accuracy: 0.8479\n",
      "Epoch 12/30\n",
      "211/217 [============================>.] - ETA: 0s - loss: 0.3197 - accuracy: 0.8503\n",
      "Epoch 00012: val_loss did not improve from 0.31798\n",
      "217/217 [==============================] - 1s 3ms/step - loss: 0.3197 - accuracy: 0.8503 - val_loss: 0.3223 - val_accuracy: 0.8485\n",
      "Epoch 13/30\n",
      "207/217 [===========================>..] - ETA: 0s - loss: 0.3165 - accuracy: 0.8531\n",
      "Epoch 00013: val_loss did not improve from 0.31798\n",
      "217/217 [==============================] - 1s 3ms/step - loss: 0.3172 - accuracy: 0.8531 - val_loss: 0.3212 - val_accuracy: 0.8561\n",
      "Epoch 14/30\n",
      "208/217 [===========================>..] - ETA: 0s - loss: 0.3131 - accuracy: 0.8531\n",
      "Epoch 00014: val_loss did not improve from 0.31798\n",
      "217/217 [==============================] - 1s 3ms/step - loss: 0.3121 - accuracy: 0.8533 - val_loss: 0.3405 - val_accuracy: 0.8432\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f83dacb2310>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.fit(input_data, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.15, callbacks=[checkpoint, early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_input_data = [X_test_countinue] + [X_test_category[:, i] for i in range(X_test_category.shape[1])] + [X_test_category_poly]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "509/509 [==============================] - 0s 787us/step - loss: 0.3438 - accuracy: 0.8434\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(eval_input_data, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_loss: 0.3437998592853546 - test_acc: 0.8433757424354553\n"
     ]
    }
   ],
   "source": [
    "print(f'test_loss: {loss} - test_acc: {acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9NhpZunTp4MGDy8vLi4qK3njjjTVr1sTqBw8ebOESEAAAoE3IMwAAgHD55ZcvW7bszjvvfOWVV+bMmfPss8/G1mdkZ2dnZmZOmDChZ8+eXbt2ffXVV0MIc+bMmT59en5+fmzu8uXLlyxZkpWV9eKLL8ZO4DjtrIULF7bk1IrTikajd99999tvv920uH79+tmzZ9fX18cu165dm5WVFeshZuvWrUOGDJk5c+Z999136aWXTpw4MVY/fPjwwoULQwjLli376KOPQggFBQXZ2dmTJk0aOHDgwIEDb7755jFjxsQGr1u3Lisr68xfAQAAaKFIQ0NDvHsAAIDzz5QpU0IIeXl58W7k9FavXj116tSW/D//qqqqvXv3DhgwICUlpbFYUVHRtWvX2trapod7V1ZWdunSJYTQvXv3goKCQYMGnXQud/OzqqqqUlJSTrv9VCQSyc3NzcjIaGZMeXn53Llzc3NzmxaPHz/euXPnxsuXX365f//+w4cPb6yUlZVVVVWlpaU138DnKS4uvv/++1sSyZxH/z0BAIAEZ30GAADwn1JTUwcPHtw0zAghxJZcNI0lQgixWCKE0NDQUF9f36dPn5PCieZnpaamttVZGt26dZs3b95J0ULTMOPo0aPHjh1rGmaEEHr06NHqMKOkpOTxxx9/5plnWjcdAABoHXkGAADQSrm5uRUVFatWrSouLo5jG8OGDbvppps++eSTU37bq1ev6dOnt+HjPv744yeeeOKk1AcAADjbkk4/BAAA4FTGjRsXO+47NTU1vp307dv3nD1r2LBh5+xZAABAI3kGAADQSo37RwEAAJxt9psCAAAAAAASnTwDAAAAAABIdPIMAAAAAAAg0ckzAAAAAACAROc8cAAAaKX9+/evXr063l2c3saNG0MI50WrnxVr/vy1f//+iy++ON5dAABAexBpaGiIdw8AAHD+mTJlygsvvBDvLjgPTJ48OS8vL95dAADAeU+eAQAAtI2MjIxw3i4EAQAAEpzzMwAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh08gwAAAAAACDRyTMAAAAAAIBEJ88AAAAAAAASnTwDAAAAAABIdPIMAAAAAAAg0ckzAAAAAACARCfPAAAAAAAAEp08AwAAAAAASHTyDAAAAAAAINHJMwAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh08gwAAAAAACDRyTMAAAAAAIBEJ88AAAAAAAASnTwDAAAAAABIdPIMAAAAAAAg0ckzAAAAAACARCfPAAAAAAAAEp08AwAAAAAASHTyDAAAAAAAINHJMwAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh08gwAAAAAACDRyTMAAAAAAIBEJ88AAAAAAAASnTwDAAAAAABIdPIMAAAAAAAg0ckzAAAAAACARBdpaGiIdw8AAMB5adWqVc8++2x9fX3scu/evSGESy+9NHbZoUOH7373uzNnzoxbfwAAQDsizwAAAFpp27ZtI0aMaGbA1q1bhw8ffs76AQAA2jF5BgAA0HqDBw/etWvXKb+67LLLCgsLz3E/AABAe+X8DAAAoPUyMzOTk5M/W09OTp49e/a57wcAAGivrM8AAABab8+ePZdddtkpf1YUFhZedtll574lAACgXbI+AwAAaL0BAwb86Z/+aSQSaVqMRCJXXnmlMAMAAGhD8gwAAOCMzJo1KxqNNq1Eo9FZs2bFqx8AAKBdst8UAABwRg4fPnzRRRfV19c3Vjp06PDxxx+npaXFsSsAAKCdsT4DAAA4I3369Bk9enTjEo1oNHrDDTcIMwAAgLYlzwAAAM5UZmZm05XfmZmZcWwGAABol+w3BQAAnKljx4717t27uro6hJCcnHz48OEePXrEuykAAKBdsT4DAAA4U927d7/55puTkpKSkpJuueUWYQYAANDm5BkAAEAb+Na3vlVXV1dXVzdz5sx49wIAALRD9psCAADawIkTJ3r16tXQ0HD06NFOnTrFux0AAKC9kWcAAEArRSKReLfAecBvLgAAaBNJ8W4AAADOY3ffffc111wT7y5OY+rUqeemzy1btkQikREjRrTJ3XJyckII99xzT5vcLS42bty4ePHieHcBAADthPUZAADQSpFIJDc3NyMjI96NnMY567O2tjaEkJTUNv/W1JQpU0IIeXl5bXK3uFi9evXUqVP95gIAgDZhfQYAANA22irJAAAA+KwO8W4AAAAAAADgNOQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAPyRG2644Uc/+lG8u/gCamtrlyxZUllZmZ+fv2HDhocffvi666774IMPmp911VVXRSKRSCQycuTIxuLatWtHjBixffv2xsqKFSuGDh2anJw8bty4PXv2hBC2bNmyYcOGs/QuAADA55FnAAAAf+QXv/jF3//937fJrbZv337aXOEM1dXVZWdnT548uWPHjjk5OaNHj16wYEFaWtrSpUubmbVp06bMzMxdu3bt2rVr/fr1seKBAwf69u27bdu2xmGFhYWbN2/esGHDtm3bCgsLn3zyyRDCFVdcUVFRkZeXd1bfCwAAOIk8AwAA+CNf+cpXkpOTz/w+ZWVl06ZNq6ysPPNbNWPVqlUDBw5MS0uLRqPdunU7cuRICOGyyy7buXNnM7OeeuqpioqK48ePDxo0qGfPnrHiRRddNHz48KbDCgsLc3Jy+vTpM2TIkFmzZr3++uux+rhx45YuXXq2Xw0AAGhKngEAAPyX7du3z549e/78+SGEF198cdSoUStXrszIyLjwwguff/75EMJzzz137bXX/vjHPx49enTnzp0XLFgQQli5cmVaWtru3bv3798/bdq00aNHhxCWL1++Y8eOxYsX5+fnT5gw4dFHH23zbhsaGr7//e/fdtttscuXXnopLS0thPDOO+/MmDHj82ZVV1cXFRXNmzdv5MiRc+fOraqq+ryRt9xyS2O0k56efuONN8Y+RyKR4cOHN78EBAAAaFvyDAAA4L8MGzbs008/ja08GD9+/K5du377298+99xz8+fPf+SRR0II3/zmN99+++0jR4788pe/XLRo0Q9+8INDhw7NmDHj8OHDtbW1F1988a233lpSUhJCyM7ODiHce++948ePnzRp0qhRo9q82+Li4g8//PCSSy5pWjx8+HBNTU0zeUZKSspbb71VWlr64IMP/uxnP3vsscda8qw333xz6tSpjZf9+/dft25dqzsHAAC+KHkGAADwRy688MLYh5SUlG7duo0aNSopKemqq67at29fCKFTp06dOnUaO3ZsUlLS7bff3qdPn9deey0ajTZOT0pK+uw958yZc/3117d5q7t37+7du3ckEmlazM3NXbx48UnFz7rgggsefvjhRx55ZMWKFad90J49e5KSkpq+Qv/+/Xfv3t26tgEAgFaQZwAAAKcXjUYbGho+W7/mmmvef//9ZiaeNlc4E5WVlXV1dScVO3TocMUVV7TwDhkZGQcOHGh+THV19aJFi3JycpoWe/fuXVpa2vJWAQCAMyTPAAAAWq+oqGjQoEHNDDirecbgwYOPHj1aW1vbtDh27NiW36Gmpmbo0KHNj8nJyZk/f37Hjh1DCNXV1bFiaWlpenr6F+wXAABoPXkGAADwR6qqqhr/qX1dXV1sWUZ1dXV9fX3jmNgBGyUlJQcPHpw8eXIIIT09fceOHSGEnTt3lpeXhxCi0WhqamppaemJEycKCgq2bNnS5q0OGDCgY8eOhw4daqyUlJTE2mi0b9++p59+uqamprFSXFxcVFQU+5yXl/fAAw80fnVSNBJCWLp06eDBg8vLy4uKit544401a9bE6gcPHmz5KhAAAODMyTMAAID/8tZbb23cuPHXv/71e++998Ybb+zfvz8/P//o0aMrV64sLy/Pz8+PDVu+fPmSJUuysrJefPHFrl27hhCys7MzMzMnTJjQs2fPrl27vvrqqyGEOXPmTJ8+PT8/f+HChS05puKLikajd99999tvv91YWb9+/ezZs5tGL2vXrs3Kyor1E7N169YhQ4bMnDnzvvvuu/TSSydOnBirHz58eOHChSGEZcuWffTRRyGEgoKC7OzsSZMmDRw4cODAgTfffPOYMWNig9etW5eVldXmbwQAAHyeyCn3wAUAAE4rEonk5uZmZGTEu5HTaPM+u3fvXlBQMGjQoJPO4q6oqOjatWttbW3TI8ErKyu7dOlSVVWVkpLyhfaemjJlSgghLy+v+WHl5eVz587Nzc1trBw/frxz585Nx7z88sv9+/cfPnx4Y6WsrKyqqiotLa3l/TRVXFx8//33nzahWb169dSpU/3mAgCANmF9BgAA8MU0NDTU19f36dPnpHwitlCjaZgRQujSpUsIITU19SwdpNGtW7d58+Y1jRZOCjOOHj167NixpmFGCKFHjx6tDjNKSkoef/zxZ555pnXTAQCA1pFnAAAAX0Bubm5FRcWqVauKi4vj3ct/GjZs2E033fTJJ5+c8ttevXpNnz69DR/38ccfP/HEEykpKW14TwAA4LSSTj8EAADg/zdu3LjYcd+pqanx7uW/9O3b95w9a9iwYefsWQAAQCN5BgAA8AXE9o8CAAA4x+w3BQAAZ8sLL7zQp0+f7t2733HHHd/+9rfHjBnz/PPPNzP+xRdfvOiiizp06PDUU0+dOHGisZ6fn3/ppZdGIpHHHnussrIyhPDhhx9Omzbtq1/96mmPpAYAAGgf5BkAAHC2TJ48+brrrvva1772k5/8ZPny5f/wD/8wffr09evXf97422677cYbbxw4cOBdd93VsWPHxvr48eNvueWW3r17f+9734stj7jkkksef/zxiRMnzpo161y8CQAAQLzJMwAA4Czq3Llz4+exY8dGo9H/+I//aH58p06dTllveqsQQrdu3bp27dpWfQIAACQ4eQYAAJwjv/vd7+rq6m6++ebGyquvvpqVlXX99dc/9dRTcWwMAAAg8TkPHAAAzq7y8vKCgoIdO3asWLHihz/84dVXXx2rf/DBBy+99NJPf/rTTz/9NC0tbcyYMV/72tfOUg8bN248S3c+e/bv3x9CWL16dbwbab3z8T92AABIWPIMAAA4u6qrq99///1777131apVM2bMaKw//fTTn3766SOPPBJCuPrqqzdt2nT28ozFixcvXrz4LN38rJo6dWq8WwAAABKCPAMAAM6unj17/t3f/V1hYeEdd9xxzTXXXHrppbF6UVHR+PHjv/Od74QQ/vf//t/N36RLly7V1dVNK59++umFF17Ywh5yc3MzMjK+cOtxNWXKlBBCXl5evBtpvdWrV8tjAACgrTg/AwAAzoVFixZddNFFU6dOrampiVVGjBjxm9/8pnHABx98cMqJBw4c2Lp164gRIw4cOFBRUdFY371797Bhw85qzwAAAIlDngEAAGfRsWPHKisrQwidOnV67rnntm7d+r3vfS/21V/8xV+sWLHiX/7lX2pqav7f//t/27ZtCyFUVlY2NDQ0Tq+vr/9f/+t/9e3b9xvf+EanTp3WrFnT+FV+fv6VV155bt8GAAAgbuw3BQAAZ0teXt7GjRtLS0t/8pOfZGVljRw58oc//OE//uM/pqamzp8//8orr8zMzLzjjjt+9KMfzZw584c//OGaNWt+9atfffLJJ3/1V3+VlJR07NixzZs3d+7cuXfv3iGEtWvXZmVlrV+//k/+5E/279+fmZnZt2/feL8iAADAORJp+i9/AQAALReJRM78XIqysrLOnTunpKS0cPzBgwcjkUhaWlrLH9EmfZ577eb8DL+5AACgTVifAQAA8dSjR48vNP6rX/3qWeoEAAAgkTk/AwAAAAAASHTyDAAAAAAAINHJMwAAAAAAgEQnzwAAAAAAABKdPAMAADi/1dbWLlmypLKyMj8/f8OGDQ8//PB11133wQcfND/rqquuikQikUhk5MiRjcW1a9eOGDFi+/btjZUVK1YMHTo0OTl53Lhxe/bsCSFs2bJlw4YNZ+ldAACAzyPPAAAAvoDt27efNipoq1ktUVdXl52dPXny5I4dO+bk5IwePXrBggVpaWlLly5tZtamTZsyMzN37dq1a9eu9evXx4oHDhzo27fvtm3bGocVFhZu3rx5w4YN27ZtKywsfPLJJ0MIV1xxRUVFRV5e3tl4HQAA4PPIMwAAgJYqKyubNm1aZWXlOZjVQqtWrRo4cGBaWlo0Gu3WrduRI0dCCJdddtnOnTubmfXUU09VVFQcP3580KBBPXv2jBUvuuii4cOHNx1WWFiYk5PTp0+fIUOGzJo16/XXX4/Vx40bt3Tp0rP0RgAAwCnJMwAAgLB58+a77rrroYceGj9+/JYtW0IIK1euTEtL27179/79+6dNmzZ69OgQwvLly3fs2LF48eL8/Pznnnvu2muv/fGPfzx69OjOnTsvWLCgJbNCCBMmTHj00UfbpO2Ghobvf//7t912W+zypZdeSktLCyG88847M2bM+LxZ1dXVRUVF8+bNGzly5Ny5c6uqqj5v5C233JKcnBz7nJ6efuONN8Y+RyKR4cOHN78EBAAAaFvyDAAA+LI7ePDg9OnTH3300YceeigzM/Omm24qLS2dMWPG4cOHa2trL7744ltvvbWkpCSEkJ2dHUK49957x48f/81vfvPtt98+cuTIL3/5y0WLFv3gBz84dOjQaWeFECZNmjRq1Kg26by4uPjDDz+85JJLmhYPHz5cU1PTTJ6RkpLy1ltvlZaWPvjggz/72c8ee+yxljzrzTffnDp1auNl//79161b1+rOAQCAL0qeAQAAX3bLli0bNGhQ165dQwjjxo0rKyvLzc2NRqONA5KSkj47q1OnTp06dRo7dmxSUtLtt9/ep0+f11577bSzQghz5sy5/vrr26Tz3bt39+7dOxKJNC3m5uYuXrz4pOJnXXDBBQ8//PAjjzyyYsWK0z5oz549SUlJTdvu37//7t27W9c2AADQCvIMAAD4stu7d2+HDv/506Bz587Dhg37/e9/38z4U0YF11xzzfvvv/9FZ52hysrKurq6k4odOnS44oorWniHjIyMAwcOND+murp60aJFOTk5TYu9e/cuLS1teasAAMAZkmcAAMCXXb9+/TZv3tx4mZyc3K9fv2bGnzKZKCoqGjRo0BeddYYGDx589OjR2trapsWxY8e2/A41NTVDhw5tfkxOTs78+fM7duwYQqiuro4VS0tL09PTv2C/AABA68kzAADgy27atGllZWXvvvtuCKGmpqawsDB2wnZ6evqOHTtCCDt37iwvLw8hRKPR1NTU0tLSEydOxOZWVlaGEEpKSg4ePDh58uSWzCooKIgdOX7mBgwY0LFjx0OHDjVWSkpKYk9vtG/fvqeffrqmpqaxUlxcXFRUFPucl5f3wAMPNH51UjQSQli6dOngwYPLy8uLioreeOONNWvWxOoHDx5s+SoQAADgzMkzAADgy+7yyy9ftmzZnXfe+corr8yZM+fZZ5+Nrc/Izs7OzMycMGFCz549u3bt+uqrr4YQ5syZM3369Pz8/Njc5cuXL1myJCsr68UXX4ydwHHaWQsXLmzJkRUtEY1G77777rfffruxsn79+tmzZ9fX1zdW1q5dm5WVFWsjZuvWrUOGDJk5c+Z999136aWXTpw4MVY/fPjwwoULQwjLli376KOPQggFBQXZ2dmTJk0aOHDgwIEDb7755jFjxsQGr1u3Lisrq03eAgAAaIlIQ0NDvHsAAIDzUiQSyc3NzcjIiHcjp9HCPquqqvbu3TtgwICUlJTGYkVFRdeuXWtra5se7l1ZWdmlS5cQQvfu3QsKCgYNGnTSodzNz6qqqkpJSTnt9lNTpkwJIeTl5TU/rLy8fO7cubm5uY2V48ePd+7cuemYl19+uX///sOHD2+slJWVVVVVpaWlNX/zz1NcXHz//fefNpVZvXr11KlT/eYCAIA2YX0GAAAQQgipqamDBw9uGmaEEGJLLprGEiGEWCwRQmhoaKivr+/Tp89J4UTzs1JTU9vwLI1u3brNmzevabRwUphx9OjRY8eONQ0zQgg9evRodZhRUlLy+OOPP/PMM62bDgAAtI48AwAAaI3c3NyKiopVq1YVFxfHt5Nhw4bddNNNn3zyySm/7dWr1/Tp09vwcR9//PETTzxxUvADAACcbUmnHwIAAPAZ48aNix33nZqaGu9eQt++fc/Zs4YNG3bOngUAADSSZwAAAK3RuH8UAADAOWC/KQAAAAAAINHJMwAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh0kYaGhnj3AAAA56VIJHL11VdffPHF8W7kNF544YXzos+TbNq0KYRw9dVXx7uR1tu/f/+mTZv85gIAgDYhzwAAgFaaMmVKvFtILL/73e9CCCNHjox3I4klLy8v3i0AAEB7IM8AAADaRkZGRghh9erV8W4EAABoh5yfAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJTp4BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJTp4BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJLineDQAAAOer48ePV1VVNV5WV1eHED755JPGSmpqaufOnePQGQAA0O5EGhoa4t0DAABwXvrJT35y5513NjNgyZIld9xxxznrBwAAaMfkGQAAQCsdOXLkoosuqqurO+W30Wj0wIEDvXv3PsddAQAA7ZLzMwAAgFbq3bv3mDFjotHoZ7+KRqM33XSTMAMAAGgr8gwAAKD1vvWtb51yzXdDQ8O3vvWtc98PAADQXtlvCgAAaL3y8vLevXs3PRU8JiUl5ciRI927d49LVwAAQPtjfQYAANB63bp1Gz9+fHJyctNiUlLSxIkThRkAAEAbkmcAAABnZObMmbW1tU0rdXV1M2fOjFc/AABAu2S/KQAA4IxUV1f36tWrvLy8sdK1a9ejR4+mpqbGsSsAAKCdsT4DAAA4IykpKVOmTElJSYldJicnT506VZgBAAC0LXkGAABwpmbMmFFdXR37XFNTM2PGjPj2AwAAtD/2mwIAAM5UfX39V7/61SNHjoQQevXqdfDgwWg0Gu+mAACAdsX6DAAA4Ex16NBhxowZKSkpycnJM2fOFGYAAABtTp4BAAC0genTp1dXV9tsCgAAOEuS4t0AAL/iWOoAACAASURBVACcqdWrV8e7BUJDQ0PPnj1DCHv37v39738f73YIGRkZ8W4BAADakvMzAAA470UikXi3AAnHbz0AANoZ+00BANAe5ObmNhBv77333nvvvdd4eZ7+XSZPnjx58uR4d3FGcnNz4/2/SAAAaHv2mwIAANrG1772tXi3AAAAtFvWZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJTp4BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAACeGGG2740Y9+FO8uvoDa2tolS5ZUVlbm5+dv2LDh4Ycfvu666z744IPmZ1111VWRSCQSiYwcObKxuHbt2hEjRmzfvr2xsmLFiqFDhyYnJ48bN27Pnj0hhC1btmzYsOEsvQsAACQ+eQYAAJAQfvGLX/z93/99m9xq+/btp80VzlBdXV12dvbkyZM7duyYk5MzevToBQsWpKWlLV26tJlZmzZtyszM3LVr165du9avXx8rHjhwoG/fvtu2bWscVlhYuHnz5g0bNmzbtq2wsPDJJ58MIVxxxRUVFRV5eXln9b0AACBhyTMAAICE8JWvfCU5OfnM71NWVjZt2rTKysozv1UzVq1aNXDgwLS0tGg02q1btyNHjoQQLrvssp07dzYz66mnnqqoqDh+/PigQYN69uwZK1500UXDhw9vOqywsDAnJ6dPnz5DhgyZNWvW66+/HquPGzdu6dKlZ/vVAAAgMckzAACA+Nu+ffvs2bPnz58fQnjxxRdHjRq1cuXKjIyMCy+88Pnnnw8hPPfcc9dee+2Pf/zj0aNHd+7cecGCBSGElStXpqWl7d69e//+/dOmTRs9enQIYfny5Tt27Fi8eHF+fv6ECRMeffTRNu+2oaHh+9///m233Ra7fOmll9LS0kII77zzzowZMz5vVnV1dVFR0bx580aOHDl37tyqqqrPG3nLLbc0Rjvp6ek33nhj7HMkEhk+fHjzS0AAAKC9kmcAAADxN2zYsE8//TS28mD8+PG7du367W9/+9xzz82fP/+RRx4JIXzzm998++23jxw58stf/nLRokU/+MEPDh06NGPGjMOHD9fW1l588cW33nprSUlJCCE7OzuEcO+9944fP37SpEmjRo1q826Li4s//PDDSy65pGnx8OHDNTU1zeQZKSkpb731Vmlp6YMPPvizn/3ssccea8mz3nzzzalTpzZe9u/ff926da3uHAAAzl/yDAAAICFceOGFsQ8pKSndunUbNWpUUlLSVVddtW/fvhBCp06dOnXqNHbs2KSkpNtvv71Pnz6vvfZaNBptnJ6UlPTZe86ZM+f6669v81Z3797du3fvSCTStJibm7t48eKTip91wQUXPPzww4888siKFStO+6A9e/YkJSU1fYX+/fvv3r27dW0DAMB5TZ4BAAAkrmg02tDQ8Nn6Nddc8/777zcz8bS5wpmorKysq6s7qdihQ4crrriihXfIyMg4cOBA82Oqq6sXLVqUk5PTtNi7d+/S0tKWtwoAAO2GPAMAADj/FBUVDRo0qJkBZzXPGDx48NGjR2tra5sWx44d2/I71NTUDB06tPkxOTk58+fP79ixYwihuro6ViwtLU1PT/+C/QIAQHsgzwAAABJCVVVV4z+1r6uriy3LqK6urq+vbxwTO2CjpKTk4MGDkydPDiGkp6fv2LEjhLBz587y8vIQQjQaTU1NLS0tPXHiREFBwZYtW9q81QEDBnTs2PHQoUONlZKSklgbjfbt2/f000/X1NQ0VoqLi4uKimKf8/LyHnjggcavTopGQghLly4dPHhweXl5UVHRG2+8sWbNmlj94MGDLV8FAgAA7Yk8AwAAiL+33npr48aNv/71r99777033nhj//79+fn5R48eXblyZXl5eX5+fmzY8uXLlyxZkpWV9eKLL3bt2jWEkJ2dnZmZOWHChJ49e3bt2vXVV18NIcyZM2f69On5+fkLFy5syTEVX1Q0Gr377rvffvvtxsr69etnz57dNHpZu3ZtVlZWrJ+YrVu3DhkyZObMmffdd9+ll146ceLEWP3w4cMLFy4MISxbtuyjjz4KIRQUFGRnZ0+aNGngwIEDBw68+eabx4wZExu8bt26rKysNn8jAABIfJFT7kULAADnkUgkkpubm5GREe9G+CNt/nfp3r17QUHBoEGDTjqLu6KiomvXrrW1tU2PBK+srOzSpUtVVVVKSsoX2ntqypQpIYS8vLzmh5WXl8+dOzc3N7excvz48c6dOzcd8/LLL/fv33/48OGNlbKysqqqqrS0tJb301RxcfH9999/2oRm9erVU6dO9VsPAIB2xvoMAADg/NDQ0FBfX9+nT5+T8onYQo2mYUYIoUuXLiGE1NTUs3SQRrdu3ebNm9c0WjgpzDh69OixY8eahhkhhB49erQ6zCgpKXn88cefeeaZ1k0HAIDznTwDAAA4D+Tm5lZUVKxataq4uDjevfynYcOG3XTTTZ988skpv+3Vq9f06dPb8HEff/zxE088kZKS0ob3BACA80jS6YcAAADE27hx42LHfaempsa7l//St2/fc/asYcOGnbNnAQBAApJnAAAA54HY/lEAAMCXlv2mAABo/9atW/enf/qnkUgkKyvrzjvv/Ku/+quHHnroxIkTrbvb8uXLo9Hojh07mhYLCgr69+8/YMCATz/9NFb58MMPMzMze/fu/eyzz57pC3yO3/zmN+np6ffee2/s8oYbbvjRj37UuludyVwAAIBzwPoMAADav7Fjx27duvV3v/vd0qVLO3ToUFZWNmLEiE8++eT//J//83lTtm/f3rlz5//23/7bZ7/69re//dd//dcnFW+55ZasrKwFCxbMnTs3Ly8vhHDJJZc8+uijycnJ3/3ud9v2dRpde+21o0ePbrz8xS9+ETsZu4WavuMXnQsAAHCOWZ8BAMCXQmy3okgkEkLo0aPH1VdfvX379s8bXFZWNm3atMrKys8b0KHDKf6PdCQSmTdv3gsvvLB06dJY5YILLujevfuZtt6saDQae6kQwle+8pXk5OQWTjzpHb/QXAAAgHNPngEAwJdOZWXlb3/724yMjMbKP//zP+fm5v7N3/zN/PnzQwjLly/fsWPH4sWL8/PzQwi/+93v5syZ89hjj02cOLGsrCw25Z133pk4cWLPnj2fe+65xvt85zvfmTVr1j333LNly5bw/8cnMZs3b77rrrseeuih8ePHx75ds2bNmDFjVq1aNXTo0HvvvfdXv/rVX/7lX65cuTIrK6t///5LlizZtGnT5MmT+/fv/9prr52yz6a2b98+e/bsWP2dd96ZMGHCY4899uijj15++eVPPvlk8+/YdO4pW33xxRdHjRq1cuXKjIyMCy+88Pnnn2/LvwcAAEALRBoaGuLdAwAAnJFIJJKbm9s0n/ispUuX3nHHHXfdddenn3767//+79OmTVuwYEFqamoIYf/+/f/9v//3srKyP/zhDxdccEFlZWWHDh2SkpJ27NgxZMiQkpKSW265ZePGjR06dLjlllv+x//4H//wD//QqVOn+fPnf+9733vqqaeWLVu2devWEMI//dM/zZgxo0+fPldeeWV9ff0777wTQpg3b97ixYsPHjw4atSorVu3du3adfXq1Xfcccfu3btramr69euXnZ09e/bs8vLyr3/964MHDx4zZsxPf/rTNWvWZGZmPvvsszNmzHjggQe2bNlSUFDw2T6Tk5O/853v9OnTZ+HChSGEv/zLv/yTP/mTRYsW/cd//Ed6enp6enpOTs4TTzzx/vvvf/rpp828Y9O5p2y1a9euffv2zczMfPzxx2OvvG3btpb8Xa6++uqLL764Tf7K58ymTZtCCFdffXW8G2m9/fv3b9q0yW89AADaGeszAAD4EnnkkUeys7P/9m//9v/+3//7r//6r7Fiv379fv3rX4cQNm7cWFdXt2/fvqZTnnnmmauuuiq2wdTq1av/9m//Nlb/n//zf0aj0W984xt79+5tOr5Lly6rV6/et2/f7bff3lhctmzZoEGDYgdUjBs3rqysLDc3Ny0trVevXuPHjx8xYsSf/dmfpaam9u7de/To0dFodOzYsVVVVTfeeGMkErnhhhs++uij0/YZQrjwwgtjH/7sz/4sPT39wIEDCxYsWLRoUbdu3Vo+95StpqSkdOvWbdSoUUlJSVddddVnpwMAAJxtzgMHAOBLpEuXLl//+te//vWv/+EPf7j//vu//e1vd+vWLRKJHDt27P777587d24kEqmrq4sNju0WtWPHjsYVBp89MTspKalxfKNhw4YtXrw4Kyvr61//eqyyd+/exiM3OnfuPGzYsN///vchhNgiic/22fTUjeTk5D/84Q+xfk7Z5+e59957r7766qlTpzYzt+mOWM232igajbb8X/y/5557ml83k4CmTJkSQogd6n6eWr16dezvDgAA7Yk8AwCAL6OLL764oqJi165dV1555Xvvvffd7353x44dJ0ULjYeHFxQU/NM//VOseODAgYsuuujzbtv4D/r/+q//esOGDf/4j//4N3/zNyGEfv36/eIXv2gclpyc3K9fvy/a8+f1eUpvvvnmCy+8ENsVqra2dteuXc28Y1Nt0ioAAECbs98UAABfChUVFSGE+vr62OXLL7/cp0+fK664IoTw5ptvlpeXNzQ0vP/++3V1dTU1NdFoNDU1tbS09MSJE7feeuuWLVv+9V//9cSJE2vWrNm+fXt9fX1tbW11dXUIobq6uqamJhZjHDt2rPG08BDC008/nZ6eHvs8bdq0srKyd999N4RQU1NTWFh42223xfqpqalpnFJfXx+7VWVlZWxk054/22cIoa6urnGxRVVVVayr2traO++889577x00aFAI4bnnnmv+HZvO/bxW6+rqYr1VV1c3tgQAAHDOyDMAAGj/1q9f//Of/zyEMHPmzO9+97ujR4+urKz8zW9+E1us8Bd/8RcdOnS49tpr33zzzWuuueaee+4pKSmZM2fO9OnT8/Pz//zP//zBBx+88847e/To8c477/z5n//5v/3bv9XW1v7bv/3bp59+umrVqpqamtzc3J///Oc///nPH3roocLCwthDu3XrlpubGzty/PLLL1+2bNmdd975yiuvzJkz59lnn+3Xr9+qVasOHTr09NNPf/jhhyGETZs2vfvuu6+88kpJSclPf/rTEMK//Mu/lJSUPP/88x9++OHrr7/+2T4LCgr+/d//fcOGDTt37nzrrbc2btz461//+r333vvJT36yc+fOjh07/vM//3NWVlZBQUHz79h07ilbfeONN/bv35+fn3/06NGVK1eWl5fn5+fH7c8JAAB8KUVavvUtAAAkpkgkkpubeybnNNTW1kYikWg0WldX16FDh9guTJWVlV26dIkNOHHiRH19fefOnb/onSsqKhpP3aiqqtq7d++AAQNSUlLasM8zmdv0HZs681ZDW/xd4qLdnJ/htx4AAO2M8zMAACA0nioRjUYbi03/QX/Hjh1bd+emR4inpqYOHjy4dfeJOWWfZzL3lGFGaItWAQAA2pb9pgAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh08gwAAIDWqK2tXbJkSWVlZX5+/oYNGx5++OHrrrvugw8+aH7WVVddFYlEIpHIyJEjG4tr164dMWLE9u3bGysrVqwYOnRocnLyuHHj9uzZE0LYsmXLhg0bztK7AABA4pNnAAAA54Ht27efNipoq1ktUVdXl52dPXny5I4dO+bk5IwePXrBggVpaWlLly5tZtamTZsyMzN37dq1a9eu9evXx4oHDhzo27fvtm3bGocVFhZu3rx5w4YN27ZtKywsfPLJJ0MIV1xxRUVFRV5e3tl4HQAASHzyDAAAINGVlZVNmzatsrLyHMxqoVWrVg0cODAtLS0ajXbr1u3IkSMhhMsuu2znzp3NzHrqqacqKiqOHz8+aNCgnj17xooXXXTR8OHDmw4rLCzMycnp06fPkCFDZs2a9frrr8fq48aNW7p06Vl6IwAASHDyDAAAIG42b9581113PfTQQ+PHj9+yZUsIYeXKlWlpabt3796/f/+0adNGjx4dQli+fPmOHTsWL16cn5//3HPPXXvttT/+8Y9Hjx7duXPnBQsWtGRWCGHChAmPPvpom7Td0NDw/e9//7bbbotdvvTSS2lpaSGEd955Z8aMGZ83q7q6uqioaN68eSNHjpw7d25VVdXnjbzllluSk5Njn9PT02+88cbY50gkMnz48OaXgAAAQHslzwAAAOLj4MGD06dPf/TRRx966KHMzMybbrqptLR0xowZhw8frq2tvfjii2+99daSkpIQQnZ2dgjh3nvvHT9+/De/+c233377yJEjv/zlLxctWvSDH/zg0KFDp50VQpg0adKoUaPapPPi4uIPP/zwkksuaVo8fPhwTU1NM3lGSkrKW2+9VVpa+uCDD/7sZz977LHHWvKsN998c+rUqY2X/fv3X7duXas7BwCA85c8AwAAiI9ly5YNGjSoa9euIYRx48aVlZXl5uZGo9HGAUlJSZ+d1alTp06dOo0dOzYpKen222/v06fPa6+9dtpZIYQ5c+Zcf/31bdL57t27e/fuHYlEmhZzc3MXL158UvGzLrjggocffviRRx5ZsWLFaR+0Z8+epKSkpm33799/9+7drWsbAADOa/IMAAAgPvbu3duhw3/+JOncufOwYcN+//vfNzP+lFHBNddc8/7773/RWWeosrKyrq7upGKHDh2uuOKKFt4hIyPjwIEDzY+prq5etGhRTk5O02Lv3r1LS0tb3ioAALQb8gwAACA++vXrt3nz5sbL5OTkfv36NTP+lMlEUVHRoEGDvuisMzR48OCjR4/W1tY2LY4dO7bld6ipqRk6dGjzY3JycubPn9+xY8cQQnV1daxYWlqanp7+BfsFAID2QJ4BAADEx7Rp08rKyt59990QQk1NTWFhYeyE7fT09B07doQQdu7cWV5eHkKIRqOpqamlpaUnTpyIza2srAwhlJSUHDx4cPLkyS2ZVVBQEDty/MwNGDCgY8eOhw4daqyUlJTEnt5o3759Tz/9dE1NTWOluLi4qKgo9jkvL++BBx5o/OqkaCSEsHTp0sGDB5eXlxcVFb3xxhtr1qyJ1Q8ePNjyVSAAANCeyDMAAID4uPzyy5ctW3bnnXe+8sorc+bMefbZZ2PrM7KzszMzMydMmNCzZ8+uXbu++uqrIYQ5c+ZMnz49Pz8/Nnf58uVLlizJysp68cUXYydwnHbWwoULW3JkRUtEo9G777777bffbqysX79+9uzZ9fX1jZW1a9dmZWXF2ojZunXrkCFDZs6ced9991166aUTJ06M1Q8fPrxw4cIQwrJlyz766KMQQkFBQXZ29qRJkwYOHDhw4MCbb755zJgxscHr1q3Lyspqk7cAAIDzS6ShoSHePQAAwBmJRCK5ubkZGRnxboQ/0sK/S1VV1d69ewcMGJCSktJYrKio6Nq1a21tbdPDvSsrK7t06RJC6N69e0FBwaBBg046lLv5WVVVVSkpKafdfmrKlCkhhLy8vOaHlZeXz507Nzc3t7Fy/Pjxzp07Nx3z8ssv9+/ff/jw4Y2VsrKyqqqqtLS05m/+eYqLi++///7TpjKrV6+eOnWq33oAALQz1mcAAADxlJqaOnjw4KZhRgghtuSiaSwRQojFEiGEhoaG+vr6Pn36nBROND8rNTW1Dc/S6Nat27x585pGCyeFGUePHj127FjTMCOE0KNHj1aHGSUlJY8//vgzzzzTuukAAHC+k2cAAADnk9zc3IqKilWrVhUXF8e3k2HDht10002ffPLJKb/t1avX9OnT2/BxH3/88RNPPHFS8AMAAF8eSacfAgAAkDDGjRsXO+47NTU13r2Evn37nrNnDRs27Jw9CwAAEpA8AwAAOJ807h8FAAB8qdhvCgAAAAAASHTyDAAAAAAAINHJMwAAAAAAgEQnzwAAAAAAABKdPAMAAAAAAEh0kYaGhnj3AAAAZyQSicS7BUg4fusBANDOJMW7AQAAOFO5ubnxboEQQsjJyQkh3HPPPfFuBAAAaIeszwAAANpGRkZGCGH16tXxbgQAAGiHnJ8BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJTp4BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIlOngEAAAAAACQ6eQYAAAAAAJDo5BkAAAAAAECik2cAAAAAAACJTp4BAAAAAAAkOnkGAAAAAACQ6OQZAAAAAABAopNnAAAAAAAAiU6eAQAAAAAAJDp5BgAAAAAAkOjkGQAAAAAAQKKTZwAAAAAAAIkuKd4NAAAA56u33npr69atjZd79uwJITz99NONlREjRnzjG9+IQ2cAAEC7E2loaIh3DwAAwHlp7dq148ePj0ajHTp0CCHEflxEIpEQQn19fV1dXX5+/rhx4+LcJQAA0C7IMwAAgFaqqanp1avXsWPHTvlt9+7djxw5kpKSco67AgAA2iXnZwAAAK2UnJw8ffr0UyYWzXwFAADQCvIMAACg9aZPn15dXf3Zek1NzYwZM859PwAAQHtlvykAAKD16uvr+/bte+jQoZPqvXv3PnjwYOxcDQAAgDPn1wUAANB6HTp0yMzMPGlfqZSUlO985zvCDAAAoA35gQEAAJyRz245VV1dPX369Hj1AwAAtEv2mwIAAM7UwIEDi4qKGi8HDBjwwQcfxLEfAACg/bE+AwAAOFPf+ta3kpOTY59TUlK+/e1vx7cfAACg/bE+AwAAOFNFRUUDBw5svNy1a9egQYPi2A8AAND+WJ8BAACcqcsuu2zEiBGRSCQSiYwYMUKYAQAAtDl5BgAA0AZmzZoVjUaj0eisWbPi3QsAANAO2W8KAABoAx9//HH//v0bGhr27dvXr1+/eLcDAAC0N/IMAABojUWLFm3cuDHeXSSWX/3qVyGEG264Ic59JJhrrrnm7/7u7+LdBQAAnPfsNwUAAK2xcePGTZs2xbuLFtm/f/8LL7xwDh6Unp5+ySWXtOENX3jhhf3797fhDc+9TZs2yb0AAKBNJMW7AQAAOF9dffXVeXl58e7i9FavXj116tRz0GppaWkI4cILL2yrG0YikXvuuScjI6OtbnjuTZkyJd4tAABAOyHPAAAA2kYbJhkAAAAnsd8UAAAAAACQ6OQZAAAAAABAopNnAAAAAPx/7N17VJTXvf/xPczAcFcRmHoBq0cQ4wE1yxgJUeJRcowBb0FuBhupDdaQHpvGNFbRmAtGTYCjobQeEwMUlwPRHNcYotFIm2PrLRqUisKgxgrhjihgZGCY3x9Pf9OJIqAMzEDerz+6Zr7P3s98t6ZrOXzYzwYAANaOPAMAAAAAAAAAAFg78gwAAAAAAAAAAGDtyDMAAAAAAAAAAIC1I88AAAAAAAAAAADWjjwDAAAAwA889dRT7777rqW7eABtbW1paWnNzc1CCI1Gk5+fv3HjxunTp1++fLmTWVOnTpXJZDKZbPLkycbigQMHJk6cWFhYaKxkZmZOmDDB1tY2NDT0ypUrQoiCgoL8/PxeWw0AAACAjpFnAAAAAPiBTz/99De/+Y1ZblVYWNh5qNBzer0+ISEhPDzcyclJr9enpKQEBwdv2LBBpVKlp6ffb9aJEydiY2OLi4uLi4uPHDkiFSsqKoYPH37+/HnjMK1We+rUqfz8/PPnz2u12m3btgkhJk2a1NTUlJub26vrAgAAAHAX8gwAAAAAPzBkyBBbW9ue36ehoSEqKkraNtF7srOzfXx8VCqVEEIul7u4uNTU1Aghxo4de/HixfvN2r59e1NT0+3bt319fYcOHSoVhw0bFhAQYDpMq9WmpKR4enqOHz9+6dKlX3zxhVQPDQ1NT0/v7aUBAAAAMEWeAQAAAOBfCgsLly1blpiYKITYu3dvUFBQVlZWRESEm5vbnj17hBC7d+9+4oknPvjgg+DgYEdHxw0bNgghsrKyVCpVSUlJWVlZVFRUcHCwECIjI6OoqCg1NVWj0Qgh5s2bt2nTJvN2azAY3nzzzeeee85Y2b9/v5RtnDlzJiYmpsNZOp2utLR03bp1kydPXr58eUtLy/3uP3fuXGO04+3tPXPmTOm1TCYLCAjoZP8HAAAAALMjzwAAAADwL/7+/jdv3pR2HoSFhRUXF589e3b37t2JiYlJSUlCiIULF54+fbqmpubLL79MTk5+6623qqqqYmJiqqur29raRo4c+eyzz9bV1QkhEhIShBCrV68OCwsTQixYsCAoKMi83ZaXl1+7dm3UqFF31aurq1tbW++XZ9jZ2Z08ebK+vn79+vUfffTR5s2bu/NZX331VWRkpPGtl5fX4cOHH7pzAAAAAA+KPAMAAADAD7i5uUkv7OzsXFx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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='./data/model.png', show_shapes=True, show_layer_names=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}