save_custom_transformed_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_transform(x, scale):\n",
    "    new_x = np.empty(x.shape, dtype=(np.float32))\n",
    "    for i in range(x.shape[0]):\n",
    "        temp = np.copy(x[i]).astype(np.float32)\n",
    "        temp[(temp > 0) & (temp < 200)] = scale\n",
    "        temp[temp >= 200] = 1\n",
    "        new_x[i][:][:] = temp.astype(np.float32)\n",
    "\n",
    "    return new_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pd.read_csv('MNIST_CSV/mnist_test.csv', header=None).iloc[:, 1:785].to_numpy()\n",
    "y_test = pd.read_csv('MNIST_CSV/mnist_test.csv', header=None).iloc[:, 1].to_numpy()\n",
    "x_test_to_one = custom_transform(x_test.reshape(x_test.shape[0], 28, 28), 0.75).reshape(-1, 784)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b>The following code blocks load the augmented MNIST samples, transforms the dataset and the test set using custom transformation, and stores the 2 datasets as 2 .feather files</b></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_augmented = pd.read_feather('augmented_MNIST.feather')\n",
    "df_augmented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_augmented.rename(columns={col: int(col) for col in df_augmented.columns}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
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    "y_aug = df_augmented[0].to_numpy()\n",
    "x_train_aug = df_augmented.iloc[:, 1:785].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_aug_to_one = custom_transform(df_augmented.iloc[:, 1:785].to_numpy(), 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "    <tr>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>600000 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1    2    3    4    5    6    7    8    9    ...  775  776  777  \\\n",
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       "3         1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4         9  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "...     ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "599995    8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "599996    3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "599997    5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "599998    6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "599999    8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "        778  779  780  781  782  783  784  \n",
       "0       0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1       0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2       0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3       0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4       0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...     ...  ...  ...  ...  ...  ...  ...  \n",
       "599995  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "599996  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "599997  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "599998  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "599999  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[600000 rows x 785 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x_aug_to_one = pd.DataFrame(x_train_aug_to_one).astype(np.float16)\n",
    "df_x_aug_to_one.rename(columns={col: (col + 1) for col in df_x_aug_to_one.columns}, inplace=True)\n",
    "\n",
    "df_aug_to_one = pd.concat([pd.Series(y_aug), df_x_aug_to_one], axis=1)\n",
    "df_aug_to_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aug_to_one.rename(columns={col: str(col) for col in df_aug_to_one.columns}, inplace=True)\n",
    "df_aug_to_one.to_feather('data_train_aug_to_one.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_to_one = x_test_to_one.astype(np.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9    ...  775  776  777  \\\n",
       "0       0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1       0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2       0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3       0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4       0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9995    0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9996    0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9997    0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9998    0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9999    0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "      778  779  780  781  782  783  784  \n",
       "0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1     0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2     0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4     0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  \n",
       "9995  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9996  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9997  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9998  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "9999  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[10000 rows x 785 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x_test_to_one = pd.DataFrame(x_test_to_one).astype(np.float16)\n",
    "df_x_test_to_one.rename(columns={col: (col + 1) for col in df_x_test_to_one.columns}, inplace=True)\n",
    "\n",
    "df_test_to_one = pd.concat([pd.Series(y_test), df_x_test_to_one], axis=1)\n",
    "df_test_to_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_to_one.rename(columns={col: str(col) for col in df_test_to_one.columns}, inplace=True)\n",
    "df_test_to_one.to_feather('data_test_to_one.feather')"
   ]
  }
 ],
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