SpaceTitianicV0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, TensorDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Solam Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
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       "      <th>8688</th>\n",
       "      <td>9276_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/98/P</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>41.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6819.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Gravior Noxnuther</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>9278_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/1499/S</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Kurta Mondalley</td>\n",
       "      <td>False</td>\n",
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       "      <td>1872.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fayey Connon</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8691</th>\n",
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       "      <td>353.0</td>\n",
       "      <td>3235.0</td>\n",
       "      <td>Celeon Hontichre</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>9280_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>E/608/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>44.0</td>\n",
       "      <td>False</td>\n",
       "      <td>126.0</td>\n",
       "      <td>4688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Propsh Hontichre</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8693 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
       "0        0001_01     Europa     False     B/0/P    TRAPPIST-1e  39.0  False   \n",
       "1        0002_01      Earth     False     F/0/S    TRAPPIST-1e  24.0  False   \n",
       "2        0003_01     Europa     False     A/0/S    TRAPPIST-1e  58.0   True   \n",
       "3        0003_02     Europa     False     A/0/S    TRAPPIST-1e  33.0  False   \n",
       "4        0004_01      Earth     False     F/1/S    TRAPPIST-1e  16.0  False   \n",
       "...          ...        ...       ...       ...            ...   ...    ...   \n",
       "8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n",
       "8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n",
       "8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n",
       "8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n",
       "8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0             0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1           109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2            43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3             0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4           303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "...           ...        ...           ...     ...     ...                ...   \n",
       "8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n",
       "8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n",
       "8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n",
       "8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n",
       "8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n",
       "\n",
       "      Transported  \n",
       "0           False  \n",
       "1            True  \n",
       "2           False  \n",
       "3           False  \n",
       "4            True  \n",
       "...           ...  \n",
       "8688        False  \n",
       "8689        False  \n",
       "8690         True  \n",
       "8691        False  \n",
       "8692         True  \n",
       "\n",
       "[8693 rows x 14 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = pd.read_csv(\"data/train.csv\")\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>CryoSleep</th>\n",
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       "      <th>Transported</th>\n",
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       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
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       "      <td>0002_01</td>\n",
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       "    <tr>\n",
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       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
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       "      <td>TRAPPIST-1e</td>\n",
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       "      <td>True</td>\n",
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       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003_02</td>\n",
       "      <td>Europa</td>\n",
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       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>1283.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3329.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/1/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>303.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Willy Santantines</td>\n",
       "      <td>True</td>\n",
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       "      <th>8688</th>\n",
       "      <td>9276_01</td>\n",
       "      <td>Europa</td>\n",
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       "      <td>A/98/P</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>41.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6819.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Gravior Noxnuther</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>9278_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>True</td>\n",
       "      <td>G/1499/S</td>\n",
       "      <td>PSO J318.5-22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Kurta Mondalley</td>\n",
       "      <td>False</td>\n",
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       "      <th>8690</th>\n",
       "      <td>9279_01</td>\n",
       "      <td>Earth</td>\n",
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       "      <td>TRAPPIST-1e</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1872.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fayey Connon</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>9280_01</td>\n",
       "      <td>Europa</td>\n",
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       "      <td>E/608/S</td>\n",
       "      <td>55 Cancri e</td>\n",
       "      <td>32.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>3235.0</td>\n",
       "      <td>Celeon Hontichre</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>9280_02</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>E/608/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>44.0</td>\n",
       "      <td>False</td>\n",
       "      <td>126.0</td>\n",
       "      <td>4688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Propsh Hontichre</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8693 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
       "0        0001_01     Europa     False     B/0/P    TRAPPIST-1e  39.0  False   \n",
       "1        0002_01      Earth     False     F/0/S    TRAPPIST-1e  24.0  False   \n",
       "2        0003_01     Europa     False     A/0/S    TRAPPIST-1e  58.0   True   \n",
       "3        0003_02     Europa     False     A/0/S    TRAPPIST-1e  33.0  False   \n",
       "4        0004_01      Earth     False     F/1/S    TRAPPIST-1e  16.0  False   \n",
       "...          ...        ...       ...       ...            ...   ...    ...   \n",
       "8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n",
       "8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n",
       "8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n",
       "8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n",
       "8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n",
       "\n",
       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
       "0             0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
       "1           109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
       "2            43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
       "3             0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
       "4           303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
       "...           ...        ...           ...     ...     ...                ...   \n",
       "8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n",
       "8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n",
       "8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n",
       "8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n",
       "8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n",
       "\n",
       "      Transported  \n",
       "0           False  \n",
       "1            True  \n",
       "2           False  \n",
       "3           False  \n",
       "4            True  \n",
       "...           ...  \n",
       "8688        False  \n",
       "8689        False  \n",
       "8690         True  \n",
       "8691        False  \n",
       "8692         True  \n",
       "\n",
       "[8693 rows x 14 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.fillna(0, inplace=True)\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0001_01', 'Europa', False, ..., 0.0, 'Maham Ofracculy', False],\n",
       "       ['0002_01', 'Earth', False, ..., 44.0, 'Juanna Vines', True],\n",
       "       ['0003_01', 'Europa', False, ..., 49.0, 'Altark Susent', False],\n",
       "       ...,\n",
       "       ['9279_01', 'Earth', False, ..., 0.0, 'Fayey Connon', True],\n",
       "       ['9280_01', 'Europa', False, ..., 3235.0, 'Celeon Hontichre',\n",
       "        False],\n",
       "       ['9280_02', 'Europa', False, ..., 12.0, 'Propsh Hontichre', True]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = training_data.to_numpy()\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B', '0', 'P']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[:, 3][0].split('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaow\\AppData\\Local\\Temp\\ipykernel_39768\\1914284128.py:9: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  new_seperated_column = np.hstack(map(split_3rd, training_data[:, 3])).reshape(training_data.shape[0], 3)\n"
     ]
    }
   ],
   "source": [
    "def split_3rd(x):\n",
    "    if x == 0:\n",
    "        return [0, 0, 0]\n",
    "    else:\n",
    "        a, b, c = x.split('/')\n",
    "        return a, int(b), c\n",
    "\n",
    "\n",
    "new_seperated_column = np.hstack(map(split_3rd, training_data[:, 3])).reshape(training_data.shape[0], 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = np.delete(training_data, 3, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0001_01', 'Europa', False, ..., 'B', '0', 'P'],\n",
       "       ['0002_01', 'Earth', False, ..., 'F', '0', 'S'],\n",
       "       ['0003_01', 'Europa', False, ..., 'A', '0', 'S'],\n",
       "       ...,\n",
       "       ['9279_01', 'Earth', False, ..., 'G', '1500', 'S'],\n",
       "       ['9280_01', 'Europa', False, ..., 'E', '608', 'S'],\n",
       "       ['9280_02', 'Europa', False, ..., 'E', '608', 'S']], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = np.hstack((training_data, new_seperated_column))\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_mapping(column_number):\n",
    "    classes = {}\n",
    "    index = 0\n",
    "    for id in training_data[:, column_number]:\n",
    "        if id not in classes:\n",
    "            classes[id] = index\n",
    "            index += 1\n",
    "    return classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_planet = class_mapping(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0001_01', 0, False, ..., 'B', '0', 'P'],\n",
       "       ['0002_01', 1, False, ..., 'F', '0', 'S'],\n",
       "       ['0003_01', 0, False, ..., 'A', '0', 'S'],\n",
       "       ...,\n",
       "       ['9279_01', 1, False, ..., 'G', '1500', 'S'],\n",
       "       ['9280_01', 0, False, ..., 'E', '608', 'S'],\n",
       "       ['9280_02', 0, False, ..., 'E', '608', 'S']], dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data[:, 1] = np.vectorize(map_planet.get)(training_data[:, 1])\n",
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_with_mapped_value(column_number, map_name, data_set):\n",
    "    data_set[:, column_number] = np.vectorize(map_name.get)(data_set[:, column_number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{False: 0, True: 1}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TF_map = class_mapping(2)\n",
    "TF_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TRAPPIST-1e': 0, 'PSO J318.5-22': 1, '55 Cancri e': 2, 0: 3}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "destination_map = class_mapping(3)\n",
    "destination_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B': 0, 'F': 1, 'A': 2, 'G': 3, '0': 4, 'E': 5, 'D': 6, 'C': 7, 'T': 8}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin0 = class_mapping(13)\n",
    "cabin0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'P': 0, 'S': 1, '0': 2}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin2 = class_mapping(15)\n",
    "cabin2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_mapped_value(2, TF_map, training_data)\n",
    "replace_with_mapped_value(3, destination_map, training_data)\n",
    "replace_with_mapped_value(5, TF_map, training_data)\n",
    "replace_with_mapped_value(13, cabin0, training_data)\n",
    "replace_with_mapped_value(15, cabin2, training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0001_01', 0, 0, ..., 0, '0', 0],\n",
       "       ['0002_01', 1, 0, ..., 1, '0', 1],\n",
       "       ['0003_01', 0, 0, ..., 2, '0', 1],\n",
       "       ...,\n",
       "       ['9279_01', 1, 0, ..., 3, '1500', 1],\n",
       "       ['9280_01', 0, 0, ..., 5, '608', 1],\n",
       "       ['9280_02', 0, 0, ..., 5, '608', 1]], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.shuffle(training_data)\n",
    "training_data, test_data = training_data[:int(len(training_data) * 0.8)], training_data[int(len(training_data) * 0.8):]\n",
    "\n",
    "\n",
    "# x=np.delete(training_data,[0,1,2],1).astype(float)\n",
    "# y=training_data[:,[1]].astype(float)\n",
    "# tensor_x=torch.Tensor(x)\n",
    "# tensor_y=torch.Tensor(y)\n",
    "# training_data=TensorDataset(tensor_x,tensor_y)\n",
    "def to_tensor(np_array, removed_columns, target_column):\n",
    "    removed_columns.append(target_column)\n",
    "    x = np.delete(np_array, removed_columns, axis=1).astype(float)\n",
    "    y = np_array[:, target_column].astype(int)\n",
    "    y_with_zeros = []\n",
    "    for class_element in y:\n",
    "        y_with_zeros.append(np.zeros(len(TF_map)))\n",
    "        y_with_zeros[-1][TF_map[class_element]] = 1\n",
    "    y = y_with_zeros\n",
    "    tensor_x = torch.Tensor(x)\n",
    "    tensor_y = torch.Tensor(y)\n",
    "    tensor = TensorDataset(tensor_x, tensor_y)\n",
    "    return tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 13])\n",
      "torch.Size([128, 2]) torch.float32\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "\n",
    "# Create data loaders.\n",
    "train_dataloader = DataLoader(to_tensor(training_data, [0, 11], 12), batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(to_tensor(test_data, [0, 11], 12), batch_size=batch_size)\n",
    "\n",
    "for X, y in train_dataloader:\n",
    "    print(X.shape)\n",
    "    print(y.shape, y.dtype)\n",
    "    break\n",
    "\n",
    "# # Display sample data\n",
    "# figure = plt.figure(figsize=(10, 8))\n",
    "# cols, rows = 5, 5\n",
    "# for i in range(1, cols * rows + 1):\n",
    "#     idx = torch.randint(len(test_data), size=(1,)).item()\n",
    "#     img, label = test_data[idx]\n",
    "#     figure.add_subplot(rows, cols, i)\n",
    "#     plt.title(label)\n",
    "#     plt.axis(\"off\")\n",
    "#     plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda device\n",
      "NeuralNetwork(\n",
      "  (linear_relu_stack): Sequential(\n",
      "    (0): Linear(in_features=13, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (5): ReLU()\n",
      "    (6): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (7): Sigmoid()\n",
      "    (8): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (9): Sigmoid()\n",
      "    (10): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (11): Sigmoid()\n",
      "    (12): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (13): ReLU()\n",
      "    (14): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (15): ReLU()\n",
      "    (16): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (17): ReLU()\n",
      "    (18): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (19): Sigmoid()\n",
      "    (20): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (21): Sigmoid()\n",
      "    (22): Linear(in_features=512, out_features=2, bias=True)\n",
      "    (23): Sigmoid()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Get cpu or gpu device for training.\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using {} device\".format(device))\n",
    "layer_size = 512\n",
    "input_size = 13\n",
    "\n",
    "\n",
    "# Define model\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            nn.Linear(input_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(layer_size, layer_size),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(layer_size, len(TF_map)),\n",
    "            nn.Sigmoid(),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        return logits\n",
    "\n",
    "\n",
    "model = NeuralNetwork().to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss_fn = nn.L1Loss()\n",
    "loss_fn = nn.MSELoss()\n",
    "learning_rate = 1e-5\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    size = len(dataloader.dataset)\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        # y=y.type(torch.float)\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # Compute prediction error\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        # Backpropagation\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if batch % 8 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataloader, model):\n",
    "    size = len(dataloader.dataset)\n",
    "    model.eval()\n",
    "    test_loss, correct = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            pred = model(X)\n",
    "            # print(pred.argmax(1).shape,y.shape)\n",
    "            test_loss += loss_fn(pred, y).item()\n",
    "            correct += (pred.argmax(1) == y.argmax(1)).type(torch.float).sum().item()\n",
    "    test_loss /= size\n",
    "    correct /= size\n",
    "    try:\n",
    "        if correct>0.81:\n",
    "            torch.save(model,'model/m3')\n",
    "        elif correct>0.805:\n",
    "            torch.save(model,'model/m2')\n",
    "        elif correct>0.80:\n",
    "            torch.save(model,'model/m1')\n",
    "            print('save success')\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "\n",
    "    print(f\"Test Error: \\n Accuracy: {(100 * correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 0.256395  [    0/ 6954]\n",
      "loss: 0.268650  [ 1024/ 6954]\n",
      "loss: 0.255308  [ 2048/ 6954]\n",
      "loss: 0.247280  [ 3072/ 6954]\n",
      "loss: 0.244419  [ 4096/ 6954]\n",
      "loss: 0.268394  [ 5120/ 6954]\n",
      "loss: 0.249724  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 50.5%, Avg loss: 0.002018 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.250016  [    0/ 6954]\n",
      "loss: 0.249838  [ 1024/ 6954]\n",
      "loss: 0.252416  [ 2048/ 6954]\n",
      "loss: 0.251577  [ 3072/ 6954]\n",
      "loss: 0.248714  [ 4096/ 6954]\n",
      "loss: 0.251386  [ 5120/ 6954]\n",
      "loss: 0.249815  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 50.5%, Avg loss: 0.002012 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.250037  [    0/ 6954]\n",
      "loss: 0.249820  [ 1024/ 6954]\n",
      "loss: 0.250218  [ 2048/ 6954]\n",
      "loss: 0.250369  [ 3072/ 6954]\n",
      "loss: 0.249665  [ 4096/ 6954]\n",
      "loss: 0.249621  [ 5120/ 6954]\n",
      "loss: 0.250367  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 50.5%, Avg loss: 0.002012 \n",
      "\n",
      "Epoch 4\n",
      "-------------------------------\n",
      "loss: 0.249821  [    0/ 6954]\n",
      "loss: 0.250141  [ 1024/ 6954]\n",
      "loss: 0.250112  [ 2048/ 6954]\n",
      "loss: 0.249824  [ 3072/ 6954]\n",
      "loss: 0.249940  [ 4096/ 6954]\n",
      "loss: 0.249953  [ 5120/ 6954]\n",
      "loss: 0.249690  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 50.5%, Avg loss: 0.002011 \n",
      "\n",
      "Epoch 5\n",
      "-------------------------------\n",
      "loss: 0.250295  [    0/ 6954]\n",
      "loss: 0.249812  [ 1024/ 6954]\n",
      "loss: 0.249942  [ 2048/ 6954]\n",
      "loss: 0.249858  [ 3072/ 6954]\n",
      "loss: 0.250178  [ 4096/ 6954]\n",
      "loss: 0.249998  [ 5120/ 6954]\n",
      "loss: 0.250829  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 50.5%, Avg loss: 0.002008 \n",
      "\n",
      "Epoch 6\n",
      "-------------------------------\n",
      "loss: 0.249969  [    0/ 6954]\n",
      "loss: 0.249332  [ 1024/ 6954]\n",
      "loss: 0.249292  [ 2048/ 6954]\n",
      "loss: 0.248752  [ 3072/ 6954]\n",
      "loss: 0.248853  [ 4096/ 6954]\n",
      "loss: 0.247898  [ 5120/ 6954]\n",
      "loss: 0.247822  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 78.4%, Avg loss: 0.001984 \n",
      "\n",
      "Epoch 7\n",
      "-------------------------------\n",
      "loss: 0.246579  [    0/ 6954]\n",
      "loss: 0.245779  [ 1024/ 6954]\n",
      "loss: 0.244759  [ 2048/ 6954]\n",
      "loss: 0.243082  [ 3072/ 6954]\n",
      "loss: 0.240305  [ 4096/ 6954]\n",
      "loss: 0.242116  [ 5120/ 6954]\n",
      "loss: 0.234702  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.6%, Avg loss: 0.001842 \n",
      "\n",
      "Epoch 8\n",
      "-------------------------------\n",
      "loss: 0.231668  [    0/ 6954]\n",
      "loss: 0.221872  [ 1024/ 6954]\n",
      "loss: 0.224105  [ 2048/ 6954]\n",
      "loss: 0.219221  [ 3072/ 6954]\n",
      "loss: 0.216834  [ 4096/ 6954]\n",
      "loss: 0.216967  [ 5120/ 6954]\n",
      "loss: 0.208016  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.9%, Avg loss: 0.001561 \n",
      "\n",
      "Epoch 9\n",
      "-------------------------------\n",
      "loss: 0.205827  [    0/ 6954]\n",
      "loss: 0.194143  [ 1024/ 6954]\n",
      "loss: 0.185681  [ 2048/ 6954]\n",
      "loss: 0.177150  [ 3072/ 6954]\n",
      "loss: 0.172396  [ 4096/ 6954]\n",
      "loss: 0.169761  [ 5120/ 6954]\n",
      "loss: 0.176679  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.8%, Avg loss: 0.001345 \n",
      "\n",
      "Epoch 10\n",
      "-------------------------------\n",
      "loss: 0.198349  [    0/ 6954]\n",
      "loss: 0.176136  [ 1024/ 6954]\n",
      "loss: 0.162874  [ 2048/ 6954]\n",
      "loss: 0.175759  [ 3072/ 6954]\n",
      "loss: 0.176635  [ 4096/ 6954]\n",
      "loss: 0.167854  [ 5120/ 6954]\n",
      "loss: 0.180175  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.9%, Avg loss: 0.001259 \n",
      "\n",
      "Epoch 11\n",
      "-------------------------------\n",
      "loss: 0.155686  [    0/ 6954]\n",
      "loss: 0.133383  [ 1024/ 6954]\n",
      "loss: 0.163658  [ 2048/ 6954]\n",
      "loss: 0.182702  [ 3072/ 6954]\n",
      "loss: 0.199059  [ 4096/ 6954]\n",
      "loss: 0.189428  [ 5120/ 6954]\n",
      "loss: 0.136068  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.1%, Avg loss: 0.001225 \n",
      "\n",
      "Epoch 12\n",
      "-------------------------------\n",
      "loss: 0.150502  [    0/ 6954]\n",
      "loss: 0.127563  [ 1024/ 6954]\n",
      "loss: 0.145029  [ 2048/ 6954]\n",
      "loss: 0.146939  [ 3072/ 6954]\n",
      "loss: 0.190207  [ 4096/ 6954]\n",
      "loss: 0.149215  [ 5120/ 6954]\n",
      "loss: 0.137874  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.9%, Avg loss: 0.001208 \n",
      "\n",
      "Epoch 13\n",
      "-------------------------------\n",
      "loss: 0.120931  [    0/ 6954]\n",
      "loss: 0.130052  [ 1024/ 6954]\n",
      "loss: 0.181210  [ 2048/ 6954]\n",
      "loss: 0.137216  [ 3072/ 6954]\n",
      "loss: 0.154264  [ 4096/ 6954]\n",
      "loss: 0.159858  [ 5120/ 6954]\n",
      "loss: 0.200269  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001196 \n",
      "\n",
      "Epoch 14\n",
      "-------------------------------\n",
      "loss: 0.185102  [    0/ 6954]\n",
      "loss: 0.166755  [ 1024/ 6954]\n",
      "loss: 0.190509  [ 2048/ 6954]\n",
      "loss: 0.156006  [ 3072/ 6954]\n",
      "loss: 0.152659  [ 4096/ 6954]\n",
      "loss: 0.209933  [ 5120/ 6954]\n",
      "loss: 0.138641  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001187 \n",
      "\n",
      "Epoch 15\n",
      "-------------------------------\n",
      "loss: 0.136563  [    0/ 6954]\n",
      "loss: 0.150876  [ 1024/ 6954]\n",
      "loss: 0.163365  [ 2048/ 6954]\n",
      "loss: 0.170552  [ 3072/ 6954]\n",
      "loss: 0.145427  [ 4096/ 6954]\n",
      "loss: 0.126147  [ 5120/ 6954]\n",
      "loss: 0.170806  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001180 \n",
      "\n",
      "Epoch 16\n",
      "-------------------------------\n",
      "loss: 0.183674  [    0/ 6954]\n",
      "loss: 0.191004  [ 1024/ 6954]\n",
      "loss: 0.160853  [ 2048/ 6954]\n",
      "loss: 0.127459  [ 3072/ 6954]\n",
      "loss: 0.169597  [ 4096/ 6954]\n",
      "loss: 0.162727  [ 5120/ 6954]\n",
      "loss: 0.160614  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.1%, Avg loss: 0.001175 \n",
      "\n",
      "Epoch 17\n",
      "-------------------------------\n",
      "loss: 0.190322  [    0/ 6954]\n",
      "loss: 0.179349  [ 1024/ 6954]\n",
      "loss: 0.142318  [ 2048/ 6954]\n",
      "loss: 0.144170  [ 3072/ 6954]\n",
      "loss: 0.171505  [ 4096/ 6954]\n",
      "loss: 0.155195  [ 5120/ 6954]\n",
      "loss: 0.136107  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.2%, Avg loss: 0.001172 \n",
      "\n",
      "Epoch 18\n",
      "-------------------------------\n",
      "loss: 0.154351  [    0/ 6954]\n",
      "loss: 0.150612  [ 1024/ 6954]\n",
      "loss: 0.199124  [ 2048/ 6954]\n",
      "loss: 0.132828  [ 3072/ 6954]\n",
      "loss: 0.150887  [ 4096/ 6954]\n",
      "loss: 0.140933  [ 5120/ 6954]\n",
      "loss: 0.181744  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.2%, Avg loss: 0.001170 \n",
      "\n",
      "Epoch 19\n",
      "-------------------------------\n",
      "loss: 0.160745  [    0/ 6954]\n",
      "loss: 0.164015  [ 1024/ 6954]\n",
      "loss: 0.140107  [ 2048/ 6954]\n",
      "loss: 0.149625  [ 3072/ 6954]\n",
      "loss: 0.119772  [ 4096/ 6954]\n",
      "loss: 0.202937  [ 5120/ 6954]\n",
      "loss: 0.165226  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.2%, Avg loss: 0.001192 \n",
      "\n",
      "Epoch 20\n",
      "-------------------------------\n",
      "loss: 0.149391  [    0/ 6954]\n",
      "loss: 0.172414  [ 1024/ 6954]\n",
      "loss: 0.123146  [ 2048/ 6954]\n",
      "loss: 0.157492  [ 3072/ 6954]\n",
      "loss: 0.173239  [ 4096/ 6954]\n",
      "loss: 0.175370  [ 5120/ 6954]\n",
      "loss: 0.142029  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001168 \n",
      "\n",
      "Epoch 21\n",
      "-------------------------------\n",
      "loss: 0.149312  [    0/ 6954]\n",
      "loss: 0.172889  [ 1024/ 6954]\n",
      "loss: 0.171514  [ 2048/ 6954]\n",
      "loss: 0.158317  [ 3072/ 6954]\n",
      "loss: 0.154087  [ 4096/ 6954]\n",
      "loss: 0.181241  [ 5120/ 6954]\n",
      "loss: 0.147331  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.3%, Avg loss: 0.001161 \n",
      "\n",
      "Epoch 22\n",
      "-------------------------------\n",
      "loss: 0.178045  [    0/ 6954]\n",
      "loss: 0.144362  [ 1024/ 6954]\n",
      "loss: 0.131694  [ 2048/ 6954]\n",
      "loss: 0.159211  [ 3072/ 6954]\n",
      "loss: 0.126022  [ 4096/ 6954]\n",
      "loss: 0.151209  [ 5120/ 6954]\n",
      "loss: 0.181426  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001159 \n",
      "\n",
      "Epoch 23\n",
      "-------------------------------\n",
      "loss: 0.146195  [    0/ 6954]\n",
      "loss: 0.179625  [ 1024/ 6954]\n",
      "loss: 0.178902  [ 2048/ 6954]\n",
      "loss: 0.148646  [ 3072/ 6954]\n",
      "loss: 0.161825  [ 4096/ 6954]\n",
      "loss: 0.176747  [ 5120/ 6954]\n",
      "loss: 0.216191  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001162 \n",
      "\n",
      "Epoch 24\n",
      "-------------------------------\n",
      "loss: 0.171828  [    0/ 6954]\n",
      "loss: 0.124721  [ 1024/ 6954]\n",
      "loss: 0.155184  [ 2048/ 6954]\n",
      "loss: 0.151944  [ 3072/ 6954]\n",
      "loss: 0.172196  [ 4096/ 6954]\n",
      "loss: 0.179463  [ 5120/ 6954]\n",
      "loss: 0.160730  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.3%, Avg loss: 0.001160 \n",
      "\n",
      "Epoch 25\n",
      "-------------------------------\n",
      "loss: 0.164226  [    0/ 6954]\n",
      "loss: 0.191741  [ 1024/ 6954]\n",
      "loss: 0.168038  [ 2048/ 6954]\n",
      "loss: 0.170298  [ 3072/ 6954]\n",
      "loss: 0.125910  [ 4096/ 6954]\n",
      "loss: 0.183443  [ 5120/ 6954]\n",
      "loss: 0.167041  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 80.9%, Avg loss: 0.001158 \n",
      "\n",
      "Epoch 26\n",
      "-------------------------------\n",
      "loss: 0.135636  [    0/ 6954]\n",
      "loss: 0.190411  [ 1024/ 6954]\n",
      "loss: 0.184667  [ 2048/ 6954]\n",
      "loss: 0.131923  [ 3072/ 6954]\n",
      "loss: 0.140211  [ 4096/ 6954]\n",
      "loss: 0.133908  [ 5120/ 6954]\n",
      "loss: 0.172075  [ 6144/ 6954]\n",
      "Test Error: \n",
      " Accuracy: 81.0%, Avg loss: 0.001157 \n",
      "\n",
      "Epoch 27\n",
      "-------------------------------\n",
      "loss: 0.187831  [    0/ 6954]\n",
      "loss: 0.173646  [ 1024/ 6954]\n",
      "loss: 0.136760  [ 2048/ 6954]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 1000\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t + 1}\\n-------------------------------\")\n",
    "    train(train_dataloader, model, loss_fn, optimizer)\n",
    "    test(test_dataloader, model)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, 'model/m0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test_data = pd.read_csv(\"data/test.csv\")\n",
    "final_test_data.fillna(0, inplace=True)\n",
    "final_test_data = final_test_data.to_numpy()\n",
    "new_seperated_column = np.hstack(map(split_3rd, final_test_data[:, 3])).reshape(final_test_data.shape[0], 3)\n",
    "final_test_data = np.delete(final_test_data, 3, axis=1)\n",
    "final_test_data = np.hstack((final_test_data, new_seperated_column))\n",
    "final_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_with_mapped_value(1, map_planet, final_test_data)\n",
    "replace_with_mapped_value(2, TF_map, final_test_data)\n",
    "replace_with_mapped_value(3, destination_map, final_test_data)\n",
    "replace_with_mapped_value(5, TF_map, final_test_data)\n",
    "replace_with_mapped_value(12, cabin0, final_test_data)\n",
    "replace_with_mapped_value(14, cabin2, final_test_data)\n",
    "final_test_data = np.delete(final_test_data, 11, axis=1)\n",
    "final_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test_data[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load('model/m2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dct = {v: k for k, v in TF_map.items()}\n",
    "dct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "for line in final_test_data:\n",
    "    current_line = torch.Tensor(line[1:].astype(float)).to(device)\n",
    "    pred = model(current_line)\n",
    "    result.append([line[0], dct[pred.argmax().item()]])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = [['PassengerId', 'Transported']] + result\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"result.csv\", result, delimiter=\",\", fmt='%s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
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