EDA_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For classification...\n",
    "using titanic dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/lightgbm/__init__.py:48: UserWarning:\n",
      "\n",
      "Starting from version 2.2.1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8.3.3) compiler.\n",
      "This means that in case of installing LightGBM from PyPI via the ``pip install lightgbm`` command, you don't need to install the gcc compiler anymore.\n",
      "Instead of that, you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler.\n",
      "You can install the OpenMP library by the following command: ``brew install libomp``.\n",
      "\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libraries imported\n"
     ]
    }
   ],
   "source": [
    "# basics\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import gc\n",
    "import json\n",
    "import time\n",
    "import datetime\n",
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "\n",
    "# visualize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "from matplotlib_venn import venn2\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "import missingno as msno\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "# sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# custom EDA functions\n",
    "mypath = os.getcwd()\n",
    "sys.path.append(mypath + '/code/') \n",
    "from utils import reduce_mem_usage, plot_target\n",
    "from categorical_encoder import label_encoding\n",
    "from feature_converter import categorize_features\n",
    "from catplot import CategoricalVisualizer\n",
    "from denseplot import DenseVisualizer\n",
    "\n",
    "# custom modeling functions\n",
    "sys.path.append(mypath + '/models/') \n",
    "from run_models import RunModel\n",
    "from lgb_param_models import lgb_model\n",
    "from xgb_param_models import xgb_model\n",
    "from catb_param_models import catb_model\n",
    "from lin_param_models import lin_model\n",
    "from nn_param_models import nn_model\n",
    "\n",
    "# stats\n",
    "from scipy import stats\n",
    "from sklearn.metrics import cohen_kappa_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, GroupKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "import scipy as sp\n",
    "from functools import partial\n",
    "from collections import Counter\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"libraries imported\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EDA_houseprice.ipynb README.md            inputs\n",
      "EDA_titanic.ipynb    code                 models\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.DS_Store', 'atma2020', 'house-prices-advanced-regression-techniques', 'titanic']\n"
     ]
    }
   ],
   "source": [
    "# check path\n",
    "datapath = \"inputs/\"\n",
    "files = os.listdir(datapath)\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test.csv', 'train.csv', 'gender_submission.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datapath = datapath + 'titanic'\n",
    "os.listdir(datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.020323991775512695  seconds to load data\n"
     ]
    }
   ],
   "source": [
    "# import data\n",
    "time0 = time.time()\n",
    "\n",
    "train = pd.read_csv(datapath + '/train.csv')\n",
    "test = pd.read_csv(datapath + '/test.csv')\n",
    "submission = pd.read_csv(datapath + '/gender_submission.csv')\n",
    "\n",
    "time1 = time.time()\n",
    "print(time1 - time0, \" seconds to load data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check train\n",
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418, 11)\n"
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       "      <th>Pclass</th>\n",
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       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check test\n",
    "print(test.shape)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418, 2)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>894</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check submission file\n",
    "print(submission.shape)\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c478e9490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data?\n",
    "import missingno as msno\n",
    "msno.matrix(df=train, figsize=(20,14), color=(0.5,0,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hist\n",
    "target = \"Survived\"\n",
    "plot_target(train, target, objective=\"classification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 features.\n",
      "There are 3 dense features.\n",
      "There are 8 categorical features.\n"
     ]
    }
   ],
   "source": [
    "# roughly categorize features\n",
    "features, dense_features, cat_features = categorize_features(train, target, cat_threshold=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dense features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature name = PassengerId\n",
      "feature name = Age\n",
      "feature name = Fare\n"
     ]
    }
   ],
   "source": [
    "vd = DenseVisualizer(train=train, test=test, features=dense_features, target=target)\n",
    "chkd = vd.column_checker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col3 {\n",
       "            background-color:  #dbdaeb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col4 {\n",
       "            background-color:  #cccfe5;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col5 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col6 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col7 {\n",
       "            background-color:  #63a2cb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col8 {\n",
       "            background-color:  #adc1dd;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col9 {\n",
       "            background-color:  #fef6fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col10 {\n",
       "            background-color:  #fef6fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col11 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_d33f4f50_6209_11ea_b47b_acde48001122row2_col12 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_d33f4f50_6209_11ea_b47b_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >features</th>        <th class=\"col_heading level0 col1\" >train_nans</th>        <th class=\"col_heading level0 col2\" >test_nans</th>        <th class=\"col_heading level0 col3\" >train_nunique</th>        <th class=\"col_heading level0 col4\" >test_nunique</th>        <th class=\"col_heading level0 col5\" >train_min</th>        <th class=\"col_heading level0 col6\" >test_min</th>        <th class=\"col_heading level0 col7\" >train_max</th>        <th class=\"col_heading level0 col8\" >test_max</th>        <th class=\"col_heading level0 col9\" >train_mean</th>        <th class=\"col_heading level0 col10\" >test_mean</th>        <th class=\"col_heading level0 col11\" >train_skew</th>        <th class=\"col_heading level0 col12\" >test_skew</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d33f4f50_6209_11ea_b47b_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col0\" class=\"data row0 col0\" >PassengerId</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col3\" class=\"data row0 col3\" >891</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col4\" class=\"data row0 col4\" >418</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col5\" class=\"data row0 col5\" >1</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col6\" class=\"data row0 col6\" >892</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col7\" class=\"data row0 col7\" >891</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col8\" class=\"data row0 col8\" >1309</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col9\" class=\"data row0 col9\" >446</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col10\" class=\"data row0 col10\" >1100.5</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row0_col12\" class=\"data row0 col12\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d33f4f50_6209_11ea_b47b_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col0\" class=\"data row1 col0\" >Age</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col1\" class=\"data row1 col1\" >177</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col2\" class=\"data row1 col2\" >86</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col3\" class=\"data row1 col3\" >88</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col4\" class=\"data row1 col4\" >79</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col5\" class=\"data row1 col5\" >0.42</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col6\" class=\"data row1 col6\" >0.17</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col7\" class=\"data row1 col7\" >80</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col8\" class=\"data row1 col8\" >76</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col9\" class=\"data row1 col9\" >29.6991</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col10\" class=\"data row1 col10\" >30.2726</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col11\" class=\"data row1 col11\" >0.389108</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row1_col12\" class=\"data row1 col12\" >0.457361</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d33f4f50_6209_11ea_b47b_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col0\" class=\"data row2 col0\" >Fare</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col3\" class=\"data row2 col3\" >248</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col4\" class=\"data row2 col4\" >169</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col7\" class=\"data row2 col7\" >512.329</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col8\" class=\"data row2 col8\" >512.329</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col9\" class=\"data row2 col9\" >32.2042</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col10\" class=\"data row2 col10\" >35.6272</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col11\" class=\"data row2 col11\" >4.78732</td>\n",
       "                        <td id=\"T_d33f4f50_6209_11ea_b47b_acde48001122row2_col12\" class=\"data row2 col12\" >3.68721</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1166812d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chkd.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vd.plot_bars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vd.plot_vs_target()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature name = Pclass\n",
      "feature name = Name\n",
      "feature name = Sex\n",
      "feature name = SibSp\n",
      "feature name = Parch\n",
      "feature name = Ticket\n",
      "feature name = Cabin\n",
      "feature name = Embarked\n"
     ]
    }
   ],
   "source": [
    "# EDA for categorical features\n",
    "vc = CategoricalVisualizer(train=train, test=test, features=cat_features, target=target)\n",
    "chkc = vc.column_checker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #e4e1ef;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row6_col5 {\n",
       "            background-color:  #e0deed;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row7_col1 {\n",
       "            background-color:  #d9d8ea;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row7_col2 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row7_col3 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row7_col4 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_d4276024_6209_11ea_b47b_acde48001122row7_col5 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_d4276024_6209_11ea_b47b_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >features</th>        <th class=\"col_heading level0 col1\" >overlap</th>        <th class=\"col_heading level0 col2\" >train_nans</th>        <th class=\"col_heading level0 col3\" >test_nans</th>        <th class=\"col_heading level0 col4\" >train_nunique</th>        <th class=\"col_heading level0 col5\" >test_nunique</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col0\" class=\"data row0 col0\" >Pclass</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col4\" class=\"data row0 col4\" >3</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row0_col5\" class=\"data row0 col5\" >3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col0\" class=\"data row1 col0\" >Name</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col1\" class=\"data row1 col1\" >0.681714</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col2\" class=\"data row1 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col3\" class=\"data row1 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col4\" class=\"data row1 col4\" >891</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row1_col5\" class=\"data row1 col5\" >418</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col0\" class=\"data row2 col0\" >Sex</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col1\" class=\"data row2 col1\" >1</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col4\" class=\"data row2 col4\" >2</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row2_col5\" class=\"data row2 col5\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col0\" class=\"data row3 col0\" >SibSp</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col4\" class=\"data row3 col4\" >7</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row3_col5\" class=\"data row3 col5\" >7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col0\" class=\"data row4 col0\" >Parch</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col1\" class=\"data row4 col1\" >0.875</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col4\" class=\"data row4 col4\" >7</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row4_col5\" class=\"data row4 col5\" >8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col0\" class=\"data row5 col0\" >Ticket</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col1\" class=\"data row5 col1\" >0.733046</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col3\" class=\"data row5 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col4\" class=\"data row5 col4\" >681</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row5_col5\" class=\"data row5 col5\" >363</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col0\" class=\"data row6 col0\" >Cabin</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col1\" class=\"data row6 col1\" >0.786096</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col2\" class=\"data row6 col2\" >687</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col3\" class=\"data row6 col3\" >327</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col4\" class=\"data row6 col4\" >147</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row6_col5\" class=\"data row6 col5\" >76</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d4276024_6209_11ea_b47b_acde48001122level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col0\" class=\"data row7 col0\" >Embarked</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col1\" class=\"data row7 col1\" >0.75</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col2\" class=\"data row7 col2\" >2</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col3\" class=\"data row7 col3\" >0</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col4\" class=\"data row7 col4\" >3</td>\n",
       "                        <td id=\"T_d4276024_6209_11ea_b47b_acde48001122row7_col5\" class=\"data row7 col5\" >3</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c48612fd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chkc.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Sex', 'SibSp']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove categorical features which have different values between train and test\n",
    "cat_features = [c for c in cat_features if chkc.loc[chkc[\"features\"] == c, \"overlap\"].iloc[0] == 1]\n",
    "print(len(cat_features))\n",
    "cat_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vc = CategoricalVisualizer(train=train, test=test, features=cat_features, target=target)\n",
    "vc.plot_bars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vc.plot_venn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit LGB and Get Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding object features\n",
    "train, test, cat_features = label_encoding(train, test, cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[target].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = dense_features + cat_features\n",
    "target in features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[361]\ttraining's auc: 0.922247\tvalid_1's auc: 0.841897\n",
      "Partial score of fold 0 is: 0.841897233201581\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[656]\ttraining's auc: 0.943858\tvalid_1's auc: 0.852437\n",
      "Partial score of fold 1 is: 0.852437417654809\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[656]\ttraining's auc: 0.943767\tvalid_1's auc: 0.856885\n",
      "Partial score of fold 2 is: 0.8568850267379678\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[29]\ttraining's auc: 0.845572\tvalid_1's auc: 0.897193\n",
      "Partial score of fold 3 is: 0.8971925133689839\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Early stopping, best iteration is:\n",
      "[412]\ttraining's auc: 0.91887\tvalid_1's auc: 0.899622\n",
      "Partial score of fold 4 is: 0.8996222342147868\n",
      "Our oof loss score is:  0.8562058607356278\n"
     ]
    }
   ],
   "source": [
    "# fit LGB\n",
    "lgbm = RunModel(train, test, target, features, categoricals=cat_features, n_splits=5,\n",
    "                model=\"lgb\", task=\"binary\", cv_method=\"StratifiedKFold\", seed=1220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "lgb_fi_df = lgbm.plot_feature_importance(rank_range=[1,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6889004\ttest: 0.6889782\tbest: 0.6889782 (0)\ttotal: 60.7ms\tremaining: 2m 1s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.4191179496\n",
      "bestIteration = 359\n",
      "\n",
      "Shrink model to first 360 iterations.\n",
      "Partial score of fold 0 is: 0.7851778656126482\n",
      "0:\tlearn: 0.6889004\ttest: 0.6889782\tbest: 0.6889782 (0)\ttotal: 594us\tremaining: 1.19s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.4263881975\n",
      "bestIteration = 140\n",
      "\n",
      "Shrink model to first 141 iterations.\n",
      "Partial score of fold 1 is: 0.7905797101449274\n",
      "0:\tlearn: 0.6808422\ttest: 0.6813158\tbest: 0.6813158 (0)\ttotal: 2.31ms\tremaining: 4.61s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.4336102322\n",
      "bestIteration = 182\n",
      "\n",
      "Shrink model to first 183 iterations.\n",
      "Partial score of fold 2 is: 0.7780748663101604\n",
      "0:\tlearn: 0.6799074\ttest: 0.6770930\tbest: 0.6770930 (0)\ttotal: 2.98ms\tremaining: 5.96s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.3970788701\n",
      "bestIteration = 102\n",
      "\n",
      "Shrink model to first 103 iterations.\n",
      "Partial score of fold 3 is: 0.822192513368984\n",
      "0:\tlearn: 0.6808088\ttest: 0.6812647\tbest: 0.6812647 (0)\ttotal: 809us\tremaining: 1.62s\n",
      "Stopped by overfitting detector  (100 iterations wait)\n",
      "\n",
      "bestTest = 0.3866524493\n",
      "bestIteration = 567\n",
      "\n",
      "Shrink model to first 568 iterations.\n",
      "Partial score of fold 4 is: 0.8300728548300054\n",
      "Our oof loss score is:  0.8011456236218962\n"
     ]
    }
   ],
   "source": [
    "# fit CB\n",
    "lgbm = RunModel(train, test, target, features, categoricals=cat_features, n_splits=5,\n",
    "                model=\"catb\", task=\"binary\", cv_method=\"StratifiedKFold\", seed=1220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.54585\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "Stopping. Best iteration:\n",
      "[28]\tvalidation_0-auc:0.828854\n",
      "\n",
      "Partial score of fold 0 is: 0.7181159420289857\n",
      "[0]\tvalidation_0-auc:0.560013\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "Stopping. Best iteration:\n",
      "[3]\tvalidation_0-auc:0.829974\n",
      "\n",
      "Partial score of fold 1 is: 0.6983530961791832\n",
      "[0]\tvalidation_0-auc:0.571925\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "Stopping. Best iteration:\n",
      "[229]\tvalidation_0-auc:0.838102\n",
      "\n",
      "Partial score of fold 2 is: 0.7693850267379679\n",
      "[0]\tvalidation_0-auc:0.504947\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "Stopping. Best iteration:\n",
      "[83]\tvalidation_0-auc:0.892647\n",
      "\n",
      "Partial score of fold 3 is: 0.7798128342245989\n",
      "[0]\tvalidation_0-auc:0.521924\n",
      "Will train until validation_0-auc hasn't improved in 100 rounds.\n",
      "Stopping. Best iteration:\n",
      "[408]\tvalidation_0-auc:0.87075\n",
      "\n",
      "Partial score of fold 4 is: 0.8236643281165676\n",
      "Our oof loss score is:  0.7574777106701179\n"
     ]
    }
   ],
   "source": [
    "# fit XGB\n",
    "lgbm = RunModel(train, test, target, features, categoricals=cat_features, n_splits=5,\n",
    "                model=\"xgb\", task=\"binary\", cv_method=\"StratifiedKFold\", seed=1220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial score of fold 0 is: 0.7415678524374176\n",
      "Partial score of fold 1 is: 0.765151515151515\n",
      "Partial score of fold 2 is: 0.7648395721925134\n",
      "Partial score of fold 3 is: 0.8152406417112299\n",
      "Partial score of fold 4 is: 0.7850782514840798\n",
      "Our oof loss score is:  0.7742066915923689\n"
     ]
    }
   ],
   "source": [
    "# fit Linear\n",
    "lgbm = RunModel(train, test, target, features, categoricals=cat_features, n_splits=5,\n",
    "                model=\"linear\", task=\"binary\", cv_method=\"StratifiedKFold\", scaler=\"Standard\", seed=1220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 712 samples, validate on 179 samples\n",
      "Epoch 1/80\n",
      "712/712 [==============================] - ETA: 6s - loss: 0.775 - 2s 2ms/step - loss: 0.7336 - val_loss: 0.6958\n",
      "Epoch 2/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.719 - 0s 88us/step - loss: 0.7007 - val_loss: 0.6874\n",
      "Epoch 3/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.715 - 0s 81us/step - loss: 0.7006 - val_loss: 0.6758\n",
      "Epoch 4/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.747 - 0s 80us/step - loss: 0.6793 - val_loss: 0.6646\n",
      "Epoch 5/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.708 - 0s 82us/step - loss: 0.6686 - val_loss: 0.6585\n",
      "Epoch 6/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.652 - 0s 83us/step - loss: 0.6612 - val_loss: 0.6534\n",
      "Epoch 7/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.636 - 0s 85us/step - loss: 0.6413 - val_loss: 0.6497\n",
      "Epoch 8/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.581 - 0s 86us/step - loss: 0.6408 - val_loss: 0.6455\n",
      "Epoch 9/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.690 - 0s 85us/step - loss: 0.6431 - val_loss: 0.6412\n",
      "Epoch 10/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.690 - 0s 85us/step - loss: 0.6332 - val_loss: 0.6378\n",
      "Epoch 11/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.658 - 0s 84us/step - loss: 0.6303 - val_loss: 0.6344\n",
      "Epoch 12/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.614 - 0s 85us/step - loss: 0.6377 - val_loss: 0.6304\n",
      "Epoch 13/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.675 - 0s 86us/step - loss: 0.6395 - val_loss: 0.6266\n",
      "Epoch 14/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.629 - 0s 86us/step - loss: 0.6290 - val_loss: 0.6230\n",
      "Epoch 15/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.640 - 0s 85us/step - loss: 0.6158 - val_loss: 0.6193\n",
      "Epoch 16/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.616 - 0s 86us/step - loss: 0.6165 - val_loss: 0.6159\n",
      "Epoch 17/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.619 - 0s 86us/step - loss: 0.6037 - val_loss: 0.6128\n",
      "Epoch 18/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.587 - 0s 86us/step - loss: 0.6038 - val_loss: 0.6089\n",
      "Epoch 19/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.615 - 0s 88us/step - loss: 0.5933 - val_loss: 0.6052\n",
      "Epoch 20/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.563 - 0s 90us/step - loss: 0.5991 - val_loss: 0.6018\n",
      "Epoch 21/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.590 - 0s 88us/step - loss: 0.6030 - val_loss: 0.5987\n",
      "Epoch 22/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.562 - 0s 90us/step - loss: 0.5957 - val_loss: 0.5942\n",
      "Epoch 23/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.570 - 0s 91us/step - loss: 0.5981 - val_loss: 0.5906\n",
      "Epoch 24/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.608 - 0s 89us/step - loss: 0.5839 - val_loss: 0.5869\n",
      "Epoch 25/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.548 - 0s 88us/step - loss: 0.5716 - val_loss: 0.5844\n",
      "Epoch 26/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.553 - 0s 89us/step - loss: 0.5728 - val_loss: 0.5806\n",
      "Epoch 27/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.547 - 0s 91us/step - loss: 0.5688 - val_loss: 0.5765\n",
      "Epoch 28/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.563 - 0s 88us/step - loss: 0.5609 - val_loss: 0.5721\n",
      "Epoch 29/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.527 - 0s 92us/step - loss: 0.5558 - val_loss: 0.5682\n",
      "Epoch 30/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.593 - 0s 89us/step - loss: 0.5507 - val_loss: 0.5643\n",
      "Epoch 31/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.595 - 0s 90us/step - loss: 0.5454 - val_loss: 0.5620\n",
      "Epoch 32/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.518 - 0s 94us/step - loss: 0.5481 - val_loss: 0.5565\n",
      "Epoch 33/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.503 - 0s 90us/step - loss: 0.5332 - val_loss: 0.5533\n",
      "Epoch 34/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.540 - 0s 90us/step - loss: 0.5332 - val_loss: 0.5509\n",
      "Epoch 35/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.555 - 0s 95us/step - loss: 0.5276 - val_loss: 0.5467\n",
      "Epoch 36/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.469 - 0s 90us/step - loss: 0.5260 - val_loss: 0.5437\n",
      "Epoch 37/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.525 - 0s 90us/step - loss: 0.5142 - val_loss: 0.5422\n",
      "Epoch 38/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.596 - 0s 91us/step - loss: 0.5224 - val_loss: 0.5394\n",
      "Epoch 39/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.518 - 0s 92us/step - loss: 0.5141 - val_loss: 0.5372\n",
      "Epoch 40/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.443 - 0s 89us/step - loss: 0.5055 - val_loss: 0.5346\n",
      "Epoch 41/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.501 - 0s 90us/step - loss: 0.5098 - val_loss: 0.5330\n",
      "Epoch 42/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.548 - 0s 93us/step - loss: 0.4959 - val_loss: 0.5312\n",
      "Epoch 43/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.562 - 0s 100us/step - loss: 0.5135 - val_loss: 0.5296\n",
      "Epoch 44/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.475 - 0s 97us/step - loss: 0.5014 - val_loss: 0.5277\n",
      "Epoch 45/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.568 - 0s 100us/step - loss: 0.4881 - val_loss: 0.5260\n",
      "Epoch 46/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.521 - 0s 94us/step - loss: 0.4970 - val_loss: 0.5255\n",
      "Epoch 47/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.398 - 0s 91us/step - loss: 0.4889 - val_loss: 0.5234\n",
      "Epoch 48/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.518 - 0s 92us/step - loss: 0.4894 - val_loss: 0.5228\n",
      "Epoch 49/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.560 - 0s 95us/step - loss: 0.4813 - val_loss: 0.5216\n",
      "Epoch 50/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.499 - 0s 89us/step - loss: 0.4817 - val_loss: 0.5218\n",
      "Epoch 51/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.470 - 0s 89us/step - loss: 0.4826 - val_loss: 0.5216\n",
      "Epoch 52/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.478 - 0s 91us/step - loss: 0.4972 - val_loss: 0.5175\n",
      "Epoch 53/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.489 - 0s 91us/step - loss: 0.4875 - val_loss: 0.5154\n",
      "Epoch 54/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.510 - 0s 91us/step - loss: 0.4740 - val_loss: 0.5138\n",
      "Epoch 55/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.479 - 0s 91us/step - loss: 0.4792 - val_loss: 0.5135\n",
      "Epoch 56/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.516 - 0s 92us/step - loss: 0.4732 - val_loss: 0.5120\n",
      "Epoch 57/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.553 - 0s 91us/step - loss: 0.4754 - val_loss: 0.5153\n",
      "Epoch 58/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.494 - 0s 91us/step - loss: 0.4717 - val_loss: 0.5097\n",
      "Epoch 59/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.409 - 0s 92us/step - loss: 0.4594 - val_loss: 0.5085\n",
      "Epoch 60/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.585 - 0s 92us/step - loss: 0.4624 - val_loss: 0.5075\n",
      "Epoch 61/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.481 - 0s 92us/step - loss: 0.4675 - val_loss: 0.5087\n",
      "Epoch 62/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.495 - 0s 93us/step - loss: 0.4605 - val_loss: 0.5058\n",
      "Epoch 63/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.481 - 0s 91us/step - loss: 0.4685 - val_loss: 0.5033\n",
      "Epoch 64/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.487 - 0s 92us/step - loss: 0.4649 - val_loss: 0.5024\n",
      "Epoch 65/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.429 - 0s 93us/step - loss: 0.4617 - val_loss: 0.5014\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 66/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.434 - 0s 91us/step - loss: 0.4664 - val_loss: 0.4995\n",
      "Epoch 67/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.455 - 0s 91us/step - loss: 0.4583 - val_loss: 0.4984\n",
      "Epoch 68/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.405 - 0s 91us/step - loss: 0.4644 - val_loss: 0.4987\n",
      "Epoch 69/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.440 - 0s 92us/step - loss: 0.4572 - val_loss: 0.4972\n",
      "Epoch 70/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.438 - 0s 91us/step - loss: 0.4588 - val_loss: 0.4962\n",
      "Epoch 71/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.477 - 0s 92us/step - loss: 0.4542 - val_loss: 0.4951\n",
      "Epoch 72/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.419 - 0s 91us/step - loss: 0.4540 - val_loss: 0.4954\n",
      "Epoch 73/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.440 - 0s 93us/step - loss: 0.4507 - val_loss: 0.4952\n",
      "Epoch 74/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.472 - 0s 93us/step - loss: 0.4530 - val_loss: 0.4935\n",
      "Epoch 75/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.440 - 0s 91us/step - loss: 0.4545 - val_loss: 0.4950\n",
      "Epoch 76/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.449 - 0s 90us/step - loss: 0.4516 - val_loss: 0.4935\n",
      "Epoch 77/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.447 - 0s 92us/step - loss: 0.4538 - val_loss: 0.4915\n",
      "Epoch 78/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.452 - 0s 92us/step - loss: 0.4524 - val_loss: 0.4901\n",
      "Epoch 79/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.430 - 0s 93us/step - loss: 0.4469 - val_loss: 0.4903\n",
      "Epoch 80/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.445 - 0s 92us/step - loss: 0.4414 - val_loss: 0.4893\n",
      "Partial score of fold 0 is: 0.8185770750988143\n",
      "Train on 712 samples, validate on 179 samples\n",
      "Epoch 1/80\n",
      "712/712 [==============================] - ETA: 6s - loss: 0.888 - 2s 2ms/step - loss: 0.8241 - val_loss: 0.7035\n",
      "Epoch 2/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.742 - 0s 82us/step - loss: 0.7052 - val_loss: 0.6524\n",
      "Epoch 3/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.697 - 0s 90us/step - loss: 0.6809 - val_loss: 0.6542\n",
      "Epoch 4/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.615 - 0s 92us/step - loss: 0.6607 - val_loss: 0.6500\n",
      "Epoch 5/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.638 - 0s 95us/step - loss: 0.6531 - val_loss: 0.6354\n",
      "Epoch 6/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.659 - 0s 91us/step - loss: 0.6555 - val_loss: 0.6246\n",
      "Epoch 7/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.619 - 0s 93us/step - loss: 0.6409 - val_loss: 0.6180\n",
      "Epoch 8/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.633 - 0s 95us/step - loss: 0.6356 - val_loss: 0.6119\n",
      "Epoch 9/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.631 - 0s 94us/step - loss: 0.6238 - val_loss: 0.6062\n",
      "Epoch 10/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.631 - 0s 94us/step - loss: 0.6241 - val_loss: 0.6013\n",
      "Epoch 11/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.619 - ETA: 0s - loss: 0.626 - 0s 122us/step - loss: 0.6212 - val_loss: 0.5966\n",
      "Epoch 12/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.610 - 0s 111us/step - loss: 0.6053 - val_loss: 0.5900\n",
      "Epoch 13/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.629 - 0s 114us/step - loss: 0.5961 - val_loss: 0.5834\n",
      "Epoch 14/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.548 - 0s 112us/step - loss: 0.5970 - val_loss: 0.5786\n",
      "Epoch 15/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.575 - 0s 110us/step - loss: 0.5861 - val_loss: 0.5750\n",
      "Epoch 16/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.590 - ETA: 0s - loss: 0.573 - 0s 125us/step - loss: 0.5777 - val_loss: 0.5694\n",
      "Epoch 17/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.556 - 0s 109us/step - loss: 0.5637 - val_loss: 0.5639\n",
      "Epoch 18/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.588 - 0s 105us/step - loss: 0.5705 - val_loss: 0.5587\n",
      "Epoch 19/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.565 - 0s 99us/step - loss: 0.5653 - val_loss: 0.5540\n",
      "Epoch 20/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.536 - 0s 97us/step - loss: 0.5585 - val_loss: 0.5529\n",
      "Epoch 21/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.626 - 0s 96us/step - loss: 0.5548 - val_loss: 0.5495\n",
      "Epoch 22/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.518 - 0s 99us/step - loss: 0.5422 - val_loss: 0.5412\n",
      "Epoch 23/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.558 - 0s 98us/step - loss: 0.5462 - val_loss: 0.5387\n",
      "Epoch 24/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.464 - 0s 99us/step - loss: 0.5317 - val_loss: 0.5346\n",
      "Epoch 25/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.512 - 0s 95us/step - loss: 0.5334 - val_loss: 0.5300\n",
      "Epoch 26/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.552 - 0s 97us/step - loss: 0.5381 - val_loss: 0.5253\n",
      "Epoch 27/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.453 - 0s 96us/step - loss: 0.5199 - val_loss: 0.5241\n",
      "Epoch 28/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.480 - 0s 99us/step - loss: 0.5281 - val_loss: 0.5210\n",
      "Epoch 29/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.529 - 0s 99us/step - loss: 0.5197 - val_loss: 0.5176\n",
      "Epoch 30/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.429 - 0s 97us/step - loss: 0.5099 - val_loss: 0.5183\n",
      "Epoch 31/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.479 - 0s 95us/step - loss: 0.5030 - val_loss: 0.5152\n",
      "Epoch 32/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.562 - 0s 98us/step - loss: 0.5023 - val_loss: 0.5125\n",
      "Epoch 33/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.502 - 0s 95us/step - loss: 0.5107 - val_loss: 0.5111\n",
      "Epoch 34/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.488 - 0s 96us/step - loss: 0.4989 - val_loss: 0.5091\n",
      "Epoch 35/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.467 - 0s 94us/step - loss: 0.4925 - val_loss: 0.5058\n",
      "Epoch 36/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.497 - 0s 95us/step - loss: 0.5023 - val_loss: 0.5049\n",
      "Epoch 37/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.456 - 0s 95us/step - loss: 0.4958 - val_loss: 0.5089\n",
      "Epoch 38/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.453 - 0s 95us/step - loss: 0.4802 - val_loss: 0.5074\n",
      "Epoch 39/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.539 - 0s 95us/step - loss: 0.4847 - val_loss: 0.4992\n",
      "Epoch 40/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.477 - 0s 95us/step - loss: 0.4905 - val_loss: 0.5019\n",
      "Epoch 41/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.470 - 0s 96us/step - loss: 0.4837 - val_loss: 0.5058\n",
      "Epoch 42/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.530 - 0s 94us/step - loss: 0.4944 - val_loss: 0.4986\n",
      "Epoch 43/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.529 - 0s 95us/step - loss: 0.4885 - val_loss: 0.4995\n",
      "Epoch 44/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.506 - 0s 94us/step - loss: 0.4903 - val_loss: 0.5004\n",
      "Epoch 45/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.465 - 0s 95us/step - loss: 0.4751 - val_loss: 0.5013\n",
      "Epoch 46/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.457 - 0s 94us/step - loss: 0.4858 - val_loss: 0.4973\n",
      "Epoch 47/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.375 - 0s 96us/step - loss: 0.4720 - val_loss: 0.4950\n",
      "Epoch 48/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.367 - 0s 95us/step - loss: 0.4802 - val_loss: 0.4982\n",
      "Epoch 49/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.465 - 0s 97us/step - loss: 0.4670 - val_loss: 0.4956\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.496 - 0s 96us/step - loss: 0.4705 - val_loss: 0.4943\n",
      "Epoch 51/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.515 - 0s 99us/step - loss: 0.4779 - val_loss: 0.4933\n",
      "Epoch 52/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.462 - 0s 96us/step - loss: 0.4714 - val_loss: 0.4934\n",
      "Epoch 53/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.544 - 0s 96us/step - loss: 0.4607 - val_loss: 0.4950\n",
      "Epoch 54/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.425 - 0s 97us/step - loss: 0.4820 - val_loss: 0.4911\n",
      "Epoch 55/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.408 - 0s 95us/step - loss: 0.4611 - val_loss: 0.4907\n",
      "Epoch 56/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.463 - 0s 96us/step - loss: 0.4706 - val_loss: 0.4858\n",
      "Epoch 57/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.454 - 0s 96us/step - loss: 0.4666 - val_loss: 0.4896\n",
      "Epoch 58/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.544 - 0s 95us/step - loss: 0.4719 - val_loss: 0.4941\n",
      "Epoch 59/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.432 - 0s 97us/step - loss: 0.4650 - val_loss: 0.4874\n",
      "Epoch 60/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.407 - 0s 96us/step - loss: 0.4656 - val_loss: 0.4808\n",
      "Epoch 61/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.541 - 0s 95us/step - loss: 0.4743 - val_loss: 0.4851\n",
      "Epoch 62/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.478 - 0s 97us/step - loss: 0.4689 - val_loss: 0.4945\n",
      "Epoch 63/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.516 - 0s 94us/step - loss: 0.4599 - val_loss: 0.4875\n",
      "Epoch 64/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.523 - 0s 96us/step - loss: 0.4629 - val_loss: 0.4801\n",
      "Epoch 65/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.402 - 0s 97us/step - loss: 0.4663 - val_loss: 0.4808\n",
      "Epoch 66/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.457 - 0s 95us/step - loss: 0.4624 - val_loss: 0.4848\n",
      "Epoch 67/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.469 - 0s 96us/step - loss: 0.4639 - val_loss: 0.4861\n",
      "Epoch 68/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.449 - 0s 96us/step - loss: 0.4642 - val_loss: 0.4782\n",
      "Epoch 69/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.461 - 0s 95us/step - loss: 0.4585 - val_loss: 0.4768\n",
      "Epoch 70/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.409 - 0s 97us/step - loss: 0.4627 - val_loss: 0.4833\n",
      "Epoch 71/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.516 - 0s 97us/step - loss: 0.4628 - val_loss: 0.4787\n",
      "Epoch 72/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.490 - 0s 95us/step - loss: 0.4606 - val_loss: 0.4793\n",
      "Epoch 73/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.343 - 0s 96us/step - loss: 0.4531 - val_loss: 0.4751\n",
      "Epoch 74/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.427 - 0s 97us/step - loss: 0.4545 - val_loss: 0.4791\n",
      "Epoch 75/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.451 - 0s 97us/step - loss: 0.4500 - val_loss: 0.4801\n",
      "Epoch 76/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.458 - 0s 96us/step - loss: 0.4541 - val_loss: 0.4731\n",
      "Epoch 77/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.438 - 0s 96us/step - loss: 0.4483 - val_loss: 0.4752\n",
      "Epoch 78/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.515 - 0s 98us/step - loss: 0.4442 - val_loss: 0.4759\n",
      "Epoch 79/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.427 - 0s 97us/step - loss: 0.4573 - val_loss: 0.4740\n",
      "Epoch 80/80\n",
      "712/712 [==============================] - ETA: 0s - loss: 0.445 - 0s 97us/step - loss: 0.4450 - val_loss: 0.4726\n",
      "Partial score of fold 1 is: 0.8353096179183136\n",
      "Train on 713 samples, validate on 178 samples\n",
      "Epoch 1/80\n",
      "713/713 [==============================] - ETA: 7s - loss: 0.735 - 2s 3ms/step - loss: 0.6989 - val_loss: 0.6601\n",
      "Epoch 2/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.659 - 0s 84us/step - loss: 0.6627 - val_loss: 0.6404\n",
      "Epoch 3/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.688 - 0s 92us/step - loss: 0.6491 - val_loss: 0.6271\n",
      "Epoch 4/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.634 - 0s 95us/step - loss: 0.6452 - val_loss: 0.6140\n",
      "Epoch 5/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.668 - 0s 97us/step - loss: 0.6358 - val_loss: 0.6027\n",
      "Epoch 6/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.642 - 0s 99us/step - loss: 0.6209 - val_loss: 0.5935\n",
      "Epoch 7/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.583 - 0s 97us/step - loss: 0.6224 - val_loss: 0.5861\n",
      "Epoch 8/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.630 - 0s 98us/step - loss: 0.6093 - val_loss: 0.5780\n",
      "Epoch 9/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.577 - 0s 96us/step - loss: 0.5998 - val_loss: 0.5705\n",
      "Epoch 10/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.623 - 0s 98us/step - loss: 0.5982 - val_loss: 0.5637\n",
      "Epoch 11/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.590 - 0s 99us/step - loss: 0.5973 - val_loss: 0.5575\n",
      "Epoch 12/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.611 - 0s 97us/step - loss: 0.5884 - val_loss: 0.5509\n",
      "Epoch 13/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.586 - 0s 98us/step - loss: 0.5850 - val_loss: 0.5461\n",
      "Epoch 14/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.643 - 0s 97us/step - loss: 0.5838 - val_loss: 0.5402\n",
      "Epoch 15/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.578 - 0s 97us/step - loss: 0.5626 - val_loss: 0.5343\n",
      "Epoch 16/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.557 - 0s 96us/step - loss: 0.5512 - val_loss: 0.5294\n",
      "Epoch 17/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.543 - 0s 98us/step - loss: 0.5605 - val_loss: 0.5238\n",
      "Epoch 18/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.560 - 0s 98us/step - loss: 0.5442 - val_loss: 0.5198\n",
      "Epoch 19/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.517 - 0s 97us/step - loss: 0.5336 - val_loss: 0.5145\n",
      "Epoch 20/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.563 - 0s 98us/step - loss: 0.5391 - val_loss: 0.5101\n",
      "Epoch 21/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.505 - 0s 98us/step - loss: 0.5281 - val_loss: 0.5066\n",
      "Epoch 22/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.519 - 0s 96us/step - loss: 0.5190 - val_loss: 0.5032\n",
      "Epoch 23/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.570 - 0s 100us/step - loss: 0.5199 - val_loss: 0.5000\n",
      "Epoch 24/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.484 - 0s 103us/step - loss: 0.5249 - val_loss: 0.4956\n",
      "Epoch 25/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.473 - 0s 103us/step - loss: 0.5153 - val_loss: 0.4938\n",
      "Epoch 26/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.508 - 0s 102us/step - loss: 0.5148 - val_loss: 0.4899\n",
      "Epoch 27/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.445 - 0s 103us/step - loss: 0.5063 - val_loss: 0.4889\n",
      "Epoch 28/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.499 - 0s 104us/step - loss: 0.4942 - val_loss: 0.4854\n",
      "Epoch 29/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.487 - 0s 105us/step - loss: 0.4956 - val_loss: 0.4838\n",
      "Epoch 30/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.478 - 0s 106us/step - loss: 0.4918 - val_loss: 0.4815\n",
      "Epoch 31/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.551 - 0s 100us/step - loss: 0.4912 - val_loss: 0.4801\n",
      "Epoch 32/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.487 - 0s 99us/step - loss: 0.4812 - val_loss: 0.4791\n",
      "Epoch 33/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.493 - 0s 101us/step - loss: 0.4869 - val_loss: 0.4771\n",
      "Epoch 34/80\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - ETA: 0s - loss: 0.424 - 0s 108us/step - loss: 0.4878 - val_loss: 0.4763\n",
      "Epoch 35/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.409 - 0s 97us/step - loss: 0.4874 - val_loss: 0.4747\n",
      "Epoch 36/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.361 - 0s 97us/step - loss: 0.4765 - val_loss: 0.4742\n",
      "Epoch 37/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.514 - 0s 100us/step - loss: 0.4806 - val_loss: 0.4732\n",
      "Epoch 38/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.496 - 0s 97us/step - loss: 0.4880 - val_loss: 0.4733\n",
      "Epoch 39/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.478 - 0s 98us/step - loss: 0.4844 - val_loss: 0.4716\n",
      "Epoch 40/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.509 - 0s 97us/step - loss: 0.4894 - val_loss: 0.4711\n",
      "Epoch 41/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.485 - 0s 99us/step - loss: 0.4705 - val_loss: 0.4699\n",
      "Epoch 42/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.523 - 0s 98us/step - loss: 0.4753 - val_loss: 0.4706\n",
      "Epoch 43/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.547 - 0s 98us/step - loss: 0.4814 - val_loss: 0.4688\n",
      "Epoch 44/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.439 - 0s 97us/step - loss: 0.4695 - val_loss: 0.4678\n",
      "Epoch 45/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.483 - 0s 97us/step - loss: 0.4720 - val_loss: 0.4669\n",
      "Epoch 46/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.431 - 0s 99us/step - loss: 0.4659 - val_loss: 0.4656\n",
      "Epoch 47/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.417 - 0s 97us/step - loss: 0.4807 - val_loss: 0.4662\n",
      "Epoch 48/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.400 - 0s 98us/step - loss: 0.4709 - val_loss: 0.4647\n",
      "Epoch 49/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.481 - 0s 97us/step - loss: 0.4713 - val_loss: 0.4636\n",
      "Epoch 50/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.467 - 0s 98us/step - loss: 0.4638 - val_loss: 0.4627\n",
      "Epoch 51/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.523 - 0s 98us/step - loss: 0.4651 - val_loss: 0.4620\n",
      "Epoch 52/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.420 - 0s 98us/step - loss: 0.4687 - val_loss: 0.4611\n",
      "Epoch 53/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.607 - 0s 98us/step - loss: 0.4634 - val_loss: 0.4610\n",
      "Epoch 54/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.467 - 0s 98us/step - loss: 0.4683 - val_loss: 0.4605\n",
      "Epoch 55/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.438 - 0s 99us/step - loss: 0.4561 - val_loss: 0.4597\n",
      "Epoch 56/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.447 - 0s 98us/step - loss: 0.4646 - val_loss: 0.4586\n",
      "Epoch 57/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.499 - 0s 98us/step - loss: 0.4653 - val_loss: 0.4590\n",
      "Epoch 58/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.447 - 0s 98us/step - loss: 0.4506 - val_loss: 0.4594\n",
      "Epoch 59/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.502 - 0s 97us/step - loss: 0.4660 - val_loss: 0.4569\n",
      "Epoch 60/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.485 - 0s 98us/step - loss: 0.4633 - val_loss: 0.4564\n",
      "Epoch 61/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.480 - 0s 103us/step - loss: 0.4696 - val_loss: 0.4557\n",
      "Epoch 62/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.407 - 0s 100us/step - loss: 0.4615 - val_loss: 0.4590\n",
      "Epoch 63/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.481 - 0s 100us/step - loss: 0.4611 - val_loss: 0.4551\n",
      "Epoch 64/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.432 - 0s 100us/step - loss: 0.4535 - val_loss: 0.4545\n",
      "Epoch 65/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.412 - 0s 101us/step - loss: 0.4588 - val_loss: 0.4534\n",
      "Epoch 66/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.371 - 0s 99us/step - loss: 0.4565 - val_loss: 0.4573\n",
      "Epoch 67/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.526 - 0s 104us/step - loss: 0.4555 - val_loss: 0.4530\n",
      "Epoch 68/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.512 - 0s 100us/step - loss: 0.4545 - val_loss: 0.4527\n",
      "Epoch 69/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.499 - 0s 101us/step - loss: 0.4599 - val_loss: 0.4546\n",
      "Epoch 70/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.382 - 0s 102us/step - loss: 0.4535 - val_loss: 0.4527\n",
      "Epoch 71/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.436 - 0s 101us/step - loss: 0.4481 - val_loss: 0.4524\n",
      "Epoch 72/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.421 - 0s 102us/step - loss: 0.4473 - val_loss: 0.4512\n",
      "Epoch 73/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.419 - 0s 101us/step - loss: 0.4423 - val_loss: 0.4512\n",
      "Epoch 74/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.518 - 0s 101us/step - loss: 0.4454 - val_loss: 0.4501\n",
      "Epoch 75/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.393 - 0s 104us/step - loss: 0.4471 - val_loss: 0.4496\n",
      "Epoch 76/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.424 - 0s 99us/step - loss: 0.4480 - val_loss: 0.4499\n",
      "Epoch 77/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.410 - 0s 101us/step - loss: 0.4502 - val_loss: 0.4490\n",
      "Epoch 78/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.511 - 0s 101us/step - loss: 0.4514 - val_loss: 0.4490\n",
      "Epoch 79/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.506 - 0s 100us/step - loss: 0.4477 - val_loss: 0.4486\n",
      "Epoch 80/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.417 - 0s 103us/step - loss: 0.4486 - val_loss: 0.4487\n",
      "Partial score of fold 2 is: 0.8410427807486632\n",
      "Train on 713 samples, validate on 178 samples\n",
      "Epoch 1/80\n",
      "713/713 [==============================] - ETA: 7s - loss: 0.715 - 2s 3ms/step - loss: 0.7090 - val_loss: 0.6277\n",
      "Epoch 2/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.751 - 0s 88us/step - loss: 0.6880 - val_loss: 0.6188\n",
      "Epoch 3/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.641 - 0s 98us/step - loss: 0.6592 - val_loss: 0.6122\n",
      "Epoch 4/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.660 - 0s 100us/step - loss: 0.6668 - val_loss: 0.6080\n",
      "Epoch 5/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.631 - 0s 102us/step - loss: 0.6401 - val_loss: 0.6047\n",
      "Epoch 6/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.652 - 0s 101us/step - loss: 0.6393 - val_loss: 0.6016\n",
      "Epoch 7/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.627 - 0s 101us/step - loss: 0.6364 - val_loss: 0.5994\n",
      "Epoch 8/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.607 - 0s 103us/step - loss: 0.6189 - val_loss: 0.5961\n",
      "Epoch 9/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.544 - 0s 100us/step - loss: 0.6117 - val_loss: 0.5922\n",
      "Epoch 10/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.667 - 0s 100us/step - loss: 0.6199 - val_loss: 0.5878\n",
      "Epoch 11/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.588 - 0s 101us/step - loss: 0.6019 - val_loss: 0.5830\n",
      "Epoch 12/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.642 - 0s 101us/step - loss: 0.6065 - val_loss: 0.5773\n",
      "Epoch 13/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.586 - 0s 102us/step - loss: 0.6030 - val_loss: 0.5722\n",
      "Epoch 14/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.608 - 0s 101us/step - loss: 0.5846 - val_loss: 0.5659\n",
      "Epoch 15/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.580 - 0s 102us/step - loss: 0.5961 - val_loss: 0.5605\n",
      "Epoch 16/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.531 - 0s 101us/step - loss: 0.5919 - val_loss: 0.5544\n",
      "Epoch 17/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.571 - 0s 101us/step - loss: 0.5938 - val_loss: 0.5491\n",
      "Epoch 18/80\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713/713 [==============================] - ETA: 0s - loss: 0.552 - 0s 101us/step - loss: 0.5747 - val_loss: 0.5431\n",
      "Epoch 19/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.562 - 0s 101us/step - loss: 0.5736 - val_loss: 0.5375\n",
      "Epoch 20/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.529 - 0s 100us/step - loss: 0.5755 - val_loss: 0.5308\n",
      "Epoch 21/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.601 - 0s 101us/step - loss: 0.5693 - val_loss: 0.5253\n",
      "Epoch 22/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.554 - 0s 99us/step - loss: 0.5678 - val_loss: 0.5186\n",
      "Epoch 23/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.521 - 0s 100us/step - loss: 0.5520 - val_loss: 0.5125\n",
      "Epoch 24/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.526 - 0s 100us/step - loss: 0.5533 - val_loss: 0.5072\n",
      "Epoch 25/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.511 - 0s 100us/step - loss: 0.5473 - val_loss: 0.5013\n",
      "Epoch 26/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.532 - 0s 101us/step - loss: 0.5407 - val_loss: 0.4949\n",
      "Epoch 27/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.545 - 0s 101us/step - loss: 0.5413 - val_loss: 0.4891\n",
      "Epoch 28/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.486 - 0s 97us/step - loss: 0.5351 - val_loss: 0.4838\n",
      "Epoch 29/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.568 - 0s 100us/step - loss: 0.5308 - val_loss: 0.4790\n",
      "Epoch 30/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.558 - 0s 100us/step - loss: 0.5366 - val_loss: 0.4736\n",
      "Epoch 31/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.544 - 0s 105us/step - loss: 0.5236 - val_loss: 0.4691\n",
      "Epoch 32/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.566 - 0s 108us/step - loss: 0.5175 - val_loss: 0.4646\n",
      "Epoch 33/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.530 - 0s 111us/step - loss: 0.5213 - val_loss: 0.4596\n",
      "Epoch 34/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.482 - 0s 106us/step - loss: 0.5179 - val_loss: 0.4549\n",
      "Epoch 35/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.473 - 0s 103us/step - loss: 0.5106 - val_loss: 0.4510\n",
      "Epoch 36/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.639 - 0s 103us/step - loss: 0.5200 - val_loss: 0.4497\n",
      "Epoch 37/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.500 - 0s 106us/step - loss: 0.5038 - val_loss: 0.4459\n",
      "Epoch 38/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.490 - 0s 101us/step - loss: 0.5051 - val_loss: 0.4440\n",
      "Epoch 39/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.425 - 0s 100us/step - loss: 0.5040 - val_loss: 0.4426\n",
      "Epoch 40/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.502 - 0s 100us/step - loss: 0.4983 - val_loss: 0.4412\n",
      "Epoch 41/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.577 - 0s 101us/step - loss: 0.4932 - val_loss: 0.4396\n",
      "Epoch 42/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.517 - 0s 102us/step - loss: 0.5072 - val_loss: 0.4367\n",
      "Epoch 43/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.461 - 0s 99us/step - loss: 0.4999 - val_loss: 0.4349\n",
      "Epoch 44/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.505 - 0s 101us/step - loss: 0.4897 - val_loss: 0.4338\n",
      "Epoch 45/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.527 - 0s 101us/step - loss: 0.4888 - val_loss: 0.4309\n",
      "Epoch 46/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.448 - 0s 100us/step - loss: 0.4746 - val_loss: 0.4297\n",
      "Epoch 47/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.491 - 0s 101us/step - loss: 0.4922 - val_loss: 0.4269\n",
      "Epoch 48/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.532 - 0s 103us/step - loss: 0.4758 - val_loss: 0.4268\n",
      "Epoch 49/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.585 - 0s 101us/step - loss: 0.4806 - val_loss: 0.4261\n",
      "Epoch 50/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.453 - 0s 100us/step - loss: 0.4890 - val_loss: 0.4232\n",
      "Epoch 51/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.558 - 0s 102us/step - loss: 0.4900 - val_loss: 0.4244\n",
      "Epoch 52/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.397 - 0s 101us/step - loss: 0.4823 - val_loss: 0.4243\n",
      "Epoch 53/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.454 - 0s 100us/step - loss: 0.4789 - val_loss: 0.4211\n",
      "Epoch 54/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.566 - 0s 100us/step - loss: 0.4807 - val_loss: 0.4229\n",
      "Epoch 55/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.431 - 0s 101us/step - loss: 0.4752 - val_loss: 0.4214\n",
      "Epoch 56/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.408 - 0s 101us/step - loss: 0.4764 - val_loss: 0.4205\n",
      "Epoch 57/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.483 - 0s 101us/step - loss: 0.4711 - val_loss: 0.4234\n",
      "Epoch 58/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.444 - 0s 101us/step - loss: 0.4762 - val_loss: 0.4194\n",
      "Epoch 59/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.498 - 0s 100us/step - loss: 0.4709 - val_loss: 0.4173\n",
      "Epoch 60/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.405 - 0s 99us/step - loss: 0.4705 - val_loss: 0.4175\n",
      "Epoch 61/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.400 - 0s 100us/step - loss: 0.4688 - val_loss: 0.4184\n",
      "Epoch 62/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.477 - 0s 101us/step - loss: 0.4701 - val_loss: 0.4153\n",
      "Epoch 63/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.508 - 0s 99us/step - loss: 0.4725 - val_loss: 0.4150\n",
      "Epoch 64/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.398 - 0s 102us/step - loss: 0.4678 - val_loss: 0.4176\n",
      "Epoch 65/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.507 - 0s 103us/step - loss: 0.4570 - val_loss: 0.4146\n",
      "Epoch 66/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.437 - 0s 103us/step - loss: 0.4688 - val_loss: 0.4123\n",
      "Epoch 67/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.530 - 0s 105us/step - loss: 0.4685 - val_loss: 0.4129\n",
      "Epoch 68/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.401 - 0s 103us/step - loss: 0.4645 - val_loss: 0.4112\n",
      "Epoch 69/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.439 - 0s 103us/step - loss: 0.4670 - val_loss: 0.4125\n",
      "Epoch 70/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.487 - 0s 102us/step - loss: 0.4577 - val_loss: 0.4148\n",
      "Epoch 71/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.441 - 0s 103us/step - loss: 0.4716 - val_loss: 0.4103\n",
      "Epoch 72/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.490 - 0s 104us/step - loss: 0.4664 - val_loss: 0.4123\n",
      "Epoch 73/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.454 - 0s 104us/step - loss: 0.4689 - val_loss: 0.4105\n",
      "Epoch 74/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.466 - 0s 105us/step - loss: 0.4610 - val_loss: 0.4094\n",
      "Epoch 75/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.497 - 0s 107us/step - loss: 0.4651 - val_loss: 0.4098\n",
      "Epoch 76/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.456 - 0s 108us/step - loss: 0.4692 - val_loss: 0.4088\n",
      "Epoch 77/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.514 - 0s 110us/step - loss: 0.4630 - val_loss: 0.4123\n",
      "Epoch 78/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.409 - 0s 109us/step - loss: 0.4547 - val_loss: 0.4092\n",
      "Epoch 79/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.365 - 0s 110us/step - loss: 0.4605 - val_loss: 0.4083\n",
      "Epoch 80/80\n",
      "713/713 [==============================] - ETA: 0s - loss: 0.427 - 0s 109us/step - loss: 0.4571 - val_loss: 0.4077\n",
      "Partial score of fold 3 is: 0.8811497326203209\n",
      "Train on 714 samples, validate on 177 samples\n",
      "Epoch 1/80\n",
      "714/714 [==============================] - ETA: 7s - loss: 0.724 - 2s 3ms/step - loss: 0.7151 - val_loss: 0.6991\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.734 - 0s 87us/step - loss: 0.7055 - val_loss: 0.6905\n",
      "Epoch 3/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.692 - 0s 93us/step - loss: 0.6890 - val_loss: 0.6808\n",
      "Epoch 4/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.653 - 0s 96us/step - loss: 0.6799 - val_loss: 0.6744\n",
      "Epoch 5/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.705 - 0s 98us/step - loss: 0.6738 - val_loss: 0.6671\n",
      "Epoch 6/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.623 - 0s 100us/step - loss: 0.6781 - val_loss: 0.6617\n",
      "Epoch 7/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.681 - 0s 99us/step - loss: 0.6573 - val_loss: 0.6532\n",
      "Epoch 8/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.651 - 0s 101us/step - loss: 0.6495 - val_loss: 0.6485\n",
      "Epoch 9/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.646 - 0s 101us/step - loss: 0.6533 - val_loss: 0.6436\n",
      "Epoch 10/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.659 - 0s 100us/step - loss: 0.6461 - val_loss: 0.6408\n",
      "Epoch 11/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.636 - 0s 97us/step - loss: 0.6368 - val_loss: 0.6341\n",
      "Epoch 12/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.672 - 0s 99us/step - loss: 0.6354 - val_loss: 0.6307\n",
      "Epoch 13/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.669 - 0s 100us/step - loss: 0.6357 - val_loss: 0.6262\n",
      "Epoch 14/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.643 - 0s 99us/step - loss: 0.6269 - val_loss: 0.6229\n",
      "Epoch 15/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.642 - 0s 99us/step - loss: 0.6307 - val_loss: 0.6189\n",
      "Epoch 16/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.633 - 0s 99us/step - loss: 0.6377 - val_loss: 0.6154\n",
      "Epoch 17/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.598 - 0s 100us/step - loss: 0.6247 - val_loss: 0.6112\n",
      "Epoch 18/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.650 - 0s 99us/step - loss: 0.6310 - val_loss: 0.6079\n",
      "Epoch 19/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.597 - 0s 98us/step - loss: 0.6176 - val_loss: 0.6043\n",
      "Epoch 20/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.663 - 0s 98us/step - loss: 0.6046 - val_loss: 0.6012\n",
      "Epoch 21/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.641 - 0s 98us/step - loss: 0.6132 - val_loss: 0.5974\n",
      "Epoch 22/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.576 - 0s 103us/step - loss: 0.6091 - val_loss: 0.5941\n",
      "Epoch 23/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.598 - 0s 104us/step - loss: 0.5984 - val_loss: 0.5896\n",
      "Epoch 24/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.612 - 0s 101us/step - loss: 0.5970 - val_loss: 0.5854\n",
      "Epoch 25/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.620 - 0s 100us/step - loss: 0.5922 - val_loss: 0.5819\n",
      "Epoch 26/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.600 - 0s 102us/step - loss: 0.5900 - val_loss: 0.5786\n",
      "Epoch 27/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.598 - 0s 102us/step - loss: 0.5964 - val_loss: 0.5746\n",
      "Epoch 28/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.580 - 0s 102us/step - loss: 0.5854 - val_loss: 0.5702\n",
      "Epoch 29/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.564 - 0s 103us/step - loss: 0.5861 - val_loss: 0.5695\n",
      "Epoch 30/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.580 - 0s 103us/step - loss: 0.5860 - val_loss: 0.5635\n",
      "Epoch 31/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.622 - 0s 104us/step - loss: 0.5841 - val_loss: 0.5592\n",
      "Epoch 32/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.621 - 0s 102us/step - loss: 0.5722 - val_loss: 0.5559\n",
      "Epoch 33/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.580 - 0s 103us/step - loss: 0.5633 - val_loss: 0.5514\n",
      "Epoch 34/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.585 - 0s 101us/step - loss: 0.5651 - val_loss: 0.5481\n",
      "Epoch 35/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.576 - 0s 107us/step - loss: 0.5576 - val_loss: 0.5458\n",
      "Epoch 36/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.498 - 0s 103us/step - loss: 0.5574 - val_loss: 0.5402\n",
      "Epoch 37/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.572 - 0s 103us/step - loss: 0.5610 - val_loss: 0.5360\n",
      "Epoch 38/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.572 - 0s 105us/step - loss: 0.5492 - val_loss: 0.5318\n",
      "Epoch 39/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.550 - 0s 103us/step - loss: 0.5333 - val_loss: 0.5292\n",
      "Epoch 40/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.495 - 0s 109us/step - loss: 0.5313 - val_loss: 0.5239\n",
      "Epoch 41/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.514 - 0s 111us/step - loss: 0.5266 - val_loss: 0.5199\n",
      "Epoch 42/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.479 - 0s 107us/step - loss: 0.5220 - val_loss: 0.5179\n",
      "Epoch 43/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.508 - 0s 112us/step - loss: 0.5311 - val_loss: 0.5151\n",
      "Epoch 44/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.460 - 0s 111us/step - loss: 0.5185 - val_loss: 0.5104\n",
      "Epoch 45/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.502 - 0s 110us/step - loss: 0.5247 - val_loss: 0.5073\n",
      "Epoch 46/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.464 - 0s 108us/step - loss: 0.5110 - val_loss: 0.5039\n",
      "Epoch 47/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.471 - 0s 108us/step - loss: 0.5108 - val_loss: 0.4998\n",
      "Epoch 48/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.535 - 0s 106us/step - loss: 0.4996 - val_loss: 0.4967\n",
      "Epoch 49/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.543 - 0s 103us/step - loss: 0.5035 - val_loss: 0.4966\n",
      "Epoch 50/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.491 - 0s 104us/step - loss: 0.4995 - val_loss: 0.4912\n",
      "Epoch 51/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.468 - 0s 104us/step - loss: 0.4968 - val_loss: 0.4877\n",
      "Epoch 52/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.476 - 0s 102us/step - loss: 0.5084 - val_loss: 0.4863\n",
      "Epoch 53/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.522 - 0s 104us/step - loss: 0.4901 - val_loss: 0.4867\n",
      "Epoch 54/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.470 - 0s 103us/step - loss: 0.4879 - val_loss: 0.4851\n",
      "Epoch 55/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.522 - 0s 104us/step - loss: 0.4914 - val_loss: 0.4828\n",
      "Epoch 56/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.466 - 0s 105us/step - loss: 0.4819 - val_loss: 0.4802\n",
      "Epoch 57/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.517 - 0s 103us/step - loss: 0.4886 - val_loss: 0.4788\n",
      "Epoch 58/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.563 - 0s 104us/step - loss: 0.4820 - val_loss: 0.4769\n",
      "Epoch 59/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.478 - 0s 104us/step - loss: 0.4840 - val_loss: 0.4762\n",
      "Epoch 60/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.474 - 0s 104us/step - loss: 0.4794 - val_loss: 0.4753\n",
      "Epoch 61/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.490 - 0s 104us/step - loss: 0.4765 - val_loss: 0.4737\n",
      "Epoch 62/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.425 - 0s 108us/step - loss: 0.4769 - val_loss: 0.4733\n",
      "Epoch 63/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.494 - 0s 109us/step - loss: 0.4832 - val_loss: 0.4721\n",
      "Epoch 64/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.454 - 0s 109us/step - loss: 0.4675 - val_loss: 0.4701\n",
      "Epoch 65/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.410 - 0s 109us/step - loss: 0.4684 - val_loss: 0.4690\n",
      "Epoch 66/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.537 - 0s 109us/step - loss: 0.4840 - val_loss: 0.4692\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.526 - 0s 109us/step - loss: 0.4810 - val_loss: 0.4682\n",
      "Epoch 68/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.449 - 0s 110us/step - loss: 0.4633 - val_loss: 0.4671\n",
      "Epoch 69/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.485 - 0s 108us/step - loss: 0.4656 - val_loss: 0.4647\n",
      "Epoch 70/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.442 - 0s 110us/step - loss: 0.4637 - val_loss: 0.4675\n",
      "Epoch 71/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.429 - 0s 110us/step - loss: 0.4614 - val_loss: 0.4627\n",
      "Epoch 72/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.449 - 0s 110us/step - loss: 0.4590 - val_loss: 0.4619\n",
      "Epoch 73/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.509 - 0s 109us/step - loss: 0.4755 - val_loss: 0.4641\n",
      "Epoch 74/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.427 - 0s 108us/step - loss: 0.4669 - val_loss: 0.4604\n",
      "Epoch 75/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.443 - 0s 110us/step - loss: 0.4669 - val_loss: 0.4593\n",
      "Epoch 76/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.431 - 0s 107us/step - loss: 0.4693 - val_loss: 0.4586\n",
      "Epoch 77/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.466 - 0s 112us/step - loss: 0.4579 - val_loss: 0.4570\n",
      "Epoch 78/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.403 - 0s 112us/step - loss: 0.4605 - val_loss: 0.4575\n",
      "Epoch 79/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.419 - 0s 111us/step - loss: 0.4639 - val_loss: 0.4573\n",
      "Epoch 80/80\n",
      "714/714 [==============================] - ETA: 0s - loss: 0.436 - 0s 112us/step - loss: 0.4687 - val_loss: 0.4547\n",
      "Partial score of fold 4 is: 0.8497031840259038\n",
      "Our oof loss score is:  0.8433888303028366\n"
     ]
    }
   ],
   "source": [
    "# fit NN\n",
    "lgbm = RunModel(train, test, target, features, categoricals=cat_features, n_splits=5,\n",
    "                model=\"nn\", task=\"binary\", cv_method=\"StratifiedKFold\", scaler=\"MinMax\", seed=1220)"
   ]
  }
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