notebooks/14_inference_pipeline.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Standard Library Imports\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "# Third-party imports\n",
    "import pandas as pd\n",
    "\n",
    "# Local module imports\n",
    "from src.inference import load_batch_of_features_from_store, load_model_from_registry, get_model_predictions\n",
    "from src.feature_store_api import get_feature_store\n",
    "import src.config as config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "\n",
      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/111769\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "FROM-TO DATA:\n",
      " 2023-09-06 13:00:00 2023-10-04 12:00:00 \n",
      "\n",
      "\n",
      "\n",
      "Fetching data from 2023-09-06 13:00:00 to 2023-10-04 12:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DeprecationWarning: ssl.PROTOCOL_TLS is deprecated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-04 10:40:37,970 INFO: USE `taxi_demand_api_featurestore`\n",
      "2023-10-04 10:40:38,903 INFO: SELECT `fg0`.`pickup_hour` `pickup_hour`, `fg0`.`rides` `rides`, `fg0`.`pickup_location_id` `pickup_location_id`\n",
      "FROM `taxi_demand_api_featurestore`.`time_series_hourly_feature_group_1` `fg0`\n",
      "WHERE `fg0`.`pickup_hour` >= TIMESTAMP '2023-09-05 01:00:00.000' AND `fg0`.`pickup_hour` < TIMESTAMP '2023-10-05 12:00:00.000'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "178080 178080\n",
      "          pickup_hour  rides  pickup_location_id\n",
      "0 2023-09-24 19:00:00     14                  41\n",
      "2 2023-09-18 23:00:00     44                 233\n",
      "3 2023-09-24 22:00:00      0                 171\n",
      "4 2023-09-18 10:00:00     20                 158\n",
      "5 2023-09-11 13:00:00    167                 239\n",
      "2023-09-06 13:00:00 2023-10-04 12:00:00\n"
     ]
    }
   ],
   "source": [
    "current_date = pd.to_datetime(datetime.utcnow()).floor('H')\n",
    "\n",
    "features = load_batch_of_features_from_store(current_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "     rides_previous_672_hour  rides_previous_671_hour  \\\n",
       "134                      2.0                      4.0   \n",
       "212                      0.0                      0.0   \n",
       "40                       0.0                      0.0   \n",
       "220                      6.0                      4.0   \n",
       "69                       0.0                      0.0   \n",
       "\n",
       "     rides_previous_670_hour  rides_previous_669_hour  \\\n",
       "134                      1.0                      0.0   \n",
       "212                      0.0                      0.0   \n",
       "40                       0.0                      0.0   \n",
       "220                      3.0                      3.0   \n",
       "69                       0.0                      1.0   \n",
       "\n",
       "     rides_previous_668_hour  rides_previous_667_hour  \\\n",
       "134                      2.0                      1.0   \n",
       "212                      0.0                      0.0   \n",
       "40                       0.0                      0.0   \n",
       "220                      4.0                     10.0   \n",
       "69                       0.0                      0.0   \n",
       "\n",
       "     rides_previous_666_hour  rides_previous_665_hour  \\\n",
       "134                      1.0                      0.0   \n",
       "212                      0.0                      0.0   \n",
       "40                       0.0                      0.0   \n",
       "220                      5.0                      4.0   \n",
       "69                       0.0                      1.0   \n",
       "\n",
       "     rides_previous_664_hour  rides_previous_663_hour  ...  \\\n",
       "134                      0.0                      0.0  ...   \n",
       "212                      0.0                      0.0  ...   \n",
       "40                       0.0                      0.0  ...   \n",
       "220                      6.0                      7.0  ...   \n",
       "69                       0.0                      0.0  ...   \n",
       "\n",
       "     rides_previous_8_hour  rides_previous_7_hour  rides_previous_6_hour  \\\n",
       "134                    0.0                    0.0                    1.0   \n",
       "212                    0.0                    0.0                    0.0   \n",
       "40                     0.0                    0.0                    0.0   \n",
       "220                    1.0                    3.0                    3.0   \n",
       "69                     0.0                    0.0                    0.0   \n",
       "\n",
       "     rides_previous_5_hour  rides_previous_4_hour  rides_previous_3_hour  \\\n",
       "134                    0.0                    0.0                    1.0   \n",
       "212                    0.0                    0.0                    0.0   \n",
       "40                     0.0                    0.0                    1.0   \n",
       "220                   11.0                    7.0                    8.0   \n",
       "69                     1.0                    0.0                    0.0   \n",
       "\n",
       "     rides_previous_2_hour  rides_previous_1_hour         pickup_hour  \\\n",
       "134                    3.0                    1.0 2023-10-04 13:00:00   \n",
       "212                    0.0                    0.0 2023-10-04 13:00:00   \n",
       "40                     0.0                    0.0 2023-10-04 13:00:00   \n",
       "220                    4.0                    2.0 2023-10-04 13:00:00   \n",
       "69                     0.0                    0.0 2023-10-04 13:00:00   \n",
       "\n",
       "     pickup_location_id  \n",
       "134                   1  \n",
       "212                   2  \n",
       "40                    3  \n",
       "220                   4  \n",
       "69                    5  \n",
       "\n",
       "[5 rows x 674 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection closed.\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "\n",
      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/111769\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "Downloading file ... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n",
      "DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n",
      "DeprecationWarning: np.find_common_type is deprecated.  Please use `np.result_type` or `np.promote_types`.\n",
      "See https://numpy.org/devdocs/release/1.25.0-notes.html and the docs for more information.  (Deprecated NumPy 1.25)\n"
     ]
    }
   ],
   "source": [
    "model = load_model_from_registry()\n",
    "predictions = get_model_predictions(model, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "</table>\n",
       "<p>265 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pickup_location_id  predicted_demand         pickup_hour\n",
       "0                     1               2.0 2023-10-04 13:00:00\n",
       "1                     2               0.0 2023-10-04 13:00:00\n",
       "2                     3               0.0 2023-10-04 13:00:00\n",
       "3                     4               4.0 2023-10-04 13:00:00\n",
       "4                     5               0.0 2023-10-04 13:00:00\n",
       "..                  ...               ...                 ...\n",
       "260                 261              40.0 2023-10-04 13:00:00\n",
       "261                 262              96.0 2023-10-04 13:00:00\n",
       "262                 263             116.0 2023-10-04 13:00:00\n",
       "263                 264              90.0 2023-10-04 13:00:00\n",
       "264                 265              10.0 2023-10-04 13:00:00\n",
       "\n",
       "[265 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions['pickup_hour'] = current_date\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection closed.\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n",
      "\n",
      "Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/111769\n",
      "Connected. Call `.close()` to terminate connection gracefully.\n"
     ]
    }
   ],
   "source": [
    "feature_group = get_feature_store().get_or_create_feature_group(\n",
    "    name=config.FEATURE_GROUP_MODEL_PREDICTIONS,\n",
    "    version=1,\n",
    "    description=\"Predictions generated by the production model\",\n",
    "    primary_key=[\"pickup_location_id\", \"pickup_hour\"],\n",
    "    event_time=\"pickup_hour\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8ac42eda04b4d9c9d18b243d7de6d1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Uploading Dataframe: 0.00% |          | Rows 0/265 | Elapsed Time: 00:00 | Remaining Time: ?"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching job: model_predictions_feature_group_1_offline_fg_backfill\n",
      "Job started successfully, you can follow the progress at \n",
      "https://c.app.hopsworks.ai/p/111769/jobs/named/model_predictions_feature_group_1_offline_fg_backfill/executions\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<hsfs.core.job.Job at 0x14be2d870>, None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_group.insert(predictions, write_options={\"wait_for_job\": False})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_location_id</th>\n",
       "      <th>predicted_demand</th>\n",
       "      <th>pickup_hour</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>263</th>\n",
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       "      <td>90.0</td>\n",
       "      <td>2023-10-04 13:00:00</td>\n",
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       "    <tr>\n",
       "      <th>264</th>\n",
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       "</table>\n",
       "<p>265 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "     pickup_location_id  predicted_demand         pickup_hour\n",
       "0                     1               2.0 2023-10-04 13:00:00\n",
       "1                     2               0.0 2023-10-04 13:00:00\n",
       "2                     3               0.0 2023-10-04 13:00:00\n",
       "3                     4               4.0 2023-10-04 13:00:00\n",
       "4                     5               0.0 2023-10-04 13:00:00\n",
       "..                  ...               ...                 ...\n",
       "260                 261              40.0 2023-10-04 13:00:00\n",
       "261                 262              96.0 2023-10-04 13:00:00\n",
       "262                 263             116.0 2023-10-04 13:00:00\n",
       "263                 264              90.0 2023-10-04 13:00:00\n",
       "264                 265              10.0 2023-10-04 13:00:00\n",
       "\n",
       "[265 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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