Dataset Split for Dear and Downstream.ipynb

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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
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       "     image_id  5_o_Clock_Shadow  Arched_Eyebrows  Attractive  Bags_Under_Eyes  \\\n",
       "0  000001.jpg                -1                1           1               -1   \n",
       "1  000002.jpg                -1               -1          -1                1   \n",
       "2  000003.jpg                -1               -1          -1               -1   \n",
       "3  000004.jpg                -1               -1           1               -1   \n",
       "4  000005.jpg                -1                1           1               -1   \n",
       "\n",
       "   Bald  Bangs  Big_Lips  Big_Nose  Black_Hair  ...  Sideburns  Smiling  \\\n",
       "0    -1     -1        -1        -1          -1  ...         -1        1   \n",
       "1    -1     -1        -1         1          -1  ...         -1        1   \n",
       "2    -1     -1         1        -1          -1  ...         -1       -1   \n",
       "3    -1     -1        -1        -1          -1  ...         -1       -1   \n",
       "4    -1     -1         1        -1          -1  ...         -1       -1   \n",
       "\n",
       "   Straight_Hair  Wavy_Hair  Wearing_Earrings  Wearing_Hat  Wearing_Lipstick  \\\n",
       "0              1         -1                 1           -1                 1   \n",
       "1             -1         -1                -1           -1                -1   \n",
       "2             -1          1                -1           -1                -1   \n",
       "3              1         -1                 1           -1                 1   \n",
       "4             -1         -1                -1           -1                 1   \n",
       "\n",
       "   Wearing_Necklace  Wearing_Necktie  Young  \n",
       "0                -1               -1      1  \n",
       "1                -1               -1      1  \n",
       "2                -1               -1      1  \n",
       "3                 1               -1      1  \n",
       "4                -1               -1      1  \n",
       "\n",
       "[5 rows x 41 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder = \"dataset/celebA/\"\n",
    "img_folder = folder+'img_align_celeba/img_align_celeba/'\n",
    "\n",
    "df = pd.read_csv(folder + \"attr.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4b85da3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of imgs :  202599\n"
     ]
    }
   ],
   "source": [
    "print('Total number of imgs : ', len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0a4f7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dear, downstream = train_test_split(df, test_size = 0.2, random_state = 42)\n",
    "#print(f\"# dear : {len(dear)} # downstream : {len(downstream)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34188af",
   "metadata": {},
   "source": [
    "### Dear dataset split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15b83210",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# dear_train : 162079 # dear_val : 20260 # dear_test : 20260\n"
     ]
    }
   ],
   "source": [
    "dear_train, dear_test = train_test_split(df, test_size = 0.2, random_state = 42)\n",
    "dear_test, dear_val = train_test_split(dear_test, test_size = 0.5, random_state = 42)\n",
    "print(f\"# dear_train : {len(dear_train)} # dear_val : {len(dear_val)} # dear_test : {len(dear_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25cbe374",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_dir = 'dataset/celebA/'\n",
    "\n",
    "dear_train.to_csv(f'{folder_dir}dear_train.csv', index=False)\n",
    "dear_test.to_csv(f'{folder_dir}dear_test.csv', index=False)\n",
    "dear_val.to_csv(f'{folder_dir}dear_val.csv', index=False)"
   ]
  }
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