notebooks/3-Training Pipeline.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "daa869cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Feature Generartor\n",
    "import feature_generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "001d2f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the Feature Generator Class\n",
    "feature_generator_ = feature_generator.feature_creator(fpath_data_input='data/synthetic_txn_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f5f6b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stratified Train/Test Data\n",
    "X_train, X_test, y_train, y_test = feature_generator_.get_data_for_training()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e3c63fa",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature0</th>\n",
       "      <th>Feature1</th>\n",
       "      <th>Feature2</th>\n",
       "      <th>Feature3</th>\n",
       "      <th>Feature4</th>\n",
       "      <th>Feature5</th>\n",
       "      <th>Feature6</th>\n",
       "      <th>Feature7</th>\n",
       "      <th>Feature8</th>\n",
       "      <th>Feature9</th>\n",
       "      <th>...</th>\n",
       "      <th>Feature375</th>\n",
       "      <th>Feature376</th>\n",
       "      <th>Feature377</th>\n",
       "      <th>Feature378</th>\n",
       "      <th>Feature379</th>\n",
       "      <th>Feature380</th>\n",
       "      <th>Feature381</th>\n",
       "      <th>Feature382</th>\n",
       "      <th>Feature383</th>\n",
       "      <th>User_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>-0.061607</td>\n",
       "      <td>0.021109</td>\n",
       "      <td>-0.029507</td>\n",
       "      <td>0.050608</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>0.005973</td>\n",
       "      <td>0.040485</td>\n",
       "      <td>0.028323</td>\n",
       "      <td>-0.034021</td>\n",
       "      <td>0.046917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055946</td>\n",
       "      <td>-0.067546</td>\n",
       "      <td>-0.101885</td>\n",
       "      <td>-0.006615</td>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.066049</td>\n",
       "      <td>-0.001069</td>\n",
       "      <td>-0.073724</td>\n",
       "      <td>-0.023892</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>-0.025332</td>\n",
       "      <td>-0.051232</td>\n",
       "      <td>-0.100755</td>\n",
       "      <td>0.055770</td>\n",
       "      <td>-0.009053</td>\n",
       "      <td>-0.016233</td>\n",
       "      <td>0.007624</td>\n",
       "      <td>0.025452</td>\n",
       "      <td>-0.021775</td>\n",
       "      <td>-0.015170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064789</td>\n",
       "      <td>-0.051988</td>\n",
       "      <td>-0.059050</td>\n",
       "      <td>-0.063797</td>\n",
       "      <td>0.078680</td>\n",
       "      <td>0.077889</td>\n",
       "      <td>-0.029503</td>\n",
       "      <td>-0.051294</td>\n",
       "      <td>-0.037061</td>\n",
       "      <td>14</td>\n",
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       "      <td>-0.021021</td>\n",
       "      <td>0.007008</td>\n",
       "      <td>-0.022461</td>\n",
       "      <td>0.002843</td>\n",
       "      <td>0.021604</td>\n",
       "      <td>0.028168</td>\n",
       "      <td>0.084881</td>\n",
       "      <td>-0.046810</td>\n",
       "      <td>-0.008587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019474</td>\n",
       "      <td>-0.091984</td>\n",
       "      <td>-0.030931</td>\n",
       "      <td>-0.078524</td>\n",
       "      <td>0.062954</td>\n",
       "      <td>-0.054538</td>\n",
       "      <td>-0.083758</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>-0.049912</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>-0.014338</td>\n",
       "      <td>0.036519</td>\n",
       "      <td>-0.030587</td>\n",
       "      <td>-0.043091</td>\n",
       "      <td>-0.097023</td>\n",
       "      <td>0.038005</td>\n",
       "      <td>-0.047036</td>\n",
       "      <td>-0.017092</td>\n",
       "      <td>-0.057033</td>\n",
       "      <td>0.007419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151159</td>\n",
       "      <td>-0.095012</td>\n",
       "      <td>-0.085966</td>\n",
       "      <td>-0.076053</td>\n",
       "      <td>0.035824</td>\n",
       "      <td>0.032255</td>\n",
       "      <td>-0.076523</td>\n",
       "      <td>-0.043340</td>\n",
       "      <td>-0.072152</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.020997</td>\n",
       "      <td>-0.052240</td>\n",
       "      <td>-0.077261</td>\n",
       "      <td>-0.066575</td>\n",
       "      <td>0.055326</td>\n",
       "      <td>0.087589</td>\n",
       "      <td>-0.029689</td>\n",
       "      <td>0.046233</td>\n",
       "      <td>-0.017779</td>\n",
       "      <td>-0.011467</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066864</td>\n",
       "      <td>-0.018092</td>\n",
       "      <td>-0.051249</td>\n",
       "      <td>-0.065001</td>\n",
       "      <td>0.036361</td>\n",
       "      <td>0.145354</td>\n",
       "      <td>0.001424</td>\n",
       "      <td>-0.049648</td>\n",
       "      <td>-0.069986</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 385 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature0  Feature1  Feature2  Feature3  Feature4  Feature5  Feature6  \\\n",
       "466 -0.061607  0.021109 -0.029507  0.050608  0.001134  0.005973  0.040485   \n",
       "129 -0.025332 -0.051232 -0.100755  0.055770 -0.009053 -0.016233  0.007624   \n",
       "940 -0.013159 -0.021021  0.007008 -0.022461  0.002843  0.021604  0.028168   \n",
       "730 -0.014338  0.036519 -0.030587 -0.043091 -0.097023  0.038005 -0.047036   \n",
       "210  0.020997 -0.052240 -0.077261 -0.066575  0.055326  0.087589 -0.029689   \n",
       "\n",
       "     Feature7  Feature8  Feature9  ...  Feature375  Feature376  Feature377  \\\n",
       "466  0.028323 -0.034021  0.046917  ...    0.055946   -0.067546   -0.101885   \n",
       "129  0.025452 -0.021775 -0.015170  ...    0.064789   -0.051988   -0.059050   \n",
       "940  0.084881 -0.046810 -0.008587  ...    0.019474   -0.091984   -0.030931   \n",
       "730 -0.017092 -0.057033  0.007419  ...    0.151159   -0.095012   -0.085966   \n",
       "210  0.046233 -0.017779 -0.011467  ...    0.066864   -0.018092   -0.051249   \n",
       "\n",
       "     Feature378  Feature379  Feature380  Feature381  Feature382  Feature383  \\\n",
       "466   -0.006615    0.073394    0.066049   -0.001069   -0.073724   -0.023892   \n",
       "129   -0.063797    0.078680    0.077889   -0.029503   -0.051294   -0.037061   \n",
       "940   -0.078524    0.062954   -0.054538   -0.083758    0.003162   -0.049912   \n",
       "730   -0.076053    0.035824    0.032255   -0.076523   -0.043340   -0.072152   \n",
       "210   -0.065001    0.036361    0.145354    0.001424   -0.049648   -0.069986   \n",
       "\n",
       "     User_id  \n",
       "466       13  \n",
       "129       14  \n",
       "940       12  \n",
       "730       14  \n",
       "210        1  \n",
       "\n",
       "[5 rows x 385 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa6887e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd0b96b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the path to the synthetic data\n",
    "feature_generator_ = feature_generator.feature_creator(fpath_data_input='data/synthetic_txn_data.csv')\n",
    "# Pass the feature creator to the trainer and train the model\n",
    "task = trainer.Trainer(feature_creator = feature_generator_)\n",
    "# Call to train the model and save assests to the fpath_output\n",
    "task.train()"
   ]
  }
 ],
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