notebooks/13_model_training_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from typing import Optional, List\n",
    "from datetime import date, timedelta\n",
    "import joblib\n",
    "\n",
    "from pytz import timezone\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import KFold, TimeSeriesSplit\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import plotly.express as px \n",
    "import optuna\n",
    "import hopsworks\n",
    "\n",
    "from src.paths import MODELS_DIR\n",
    "from src.data import transform_ts_data_into_features_and_target\n",
    "from src.data_split import train_test_split\n",
    "from src.model import get_pipeline\n",
    "import src.config as config\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# conect to hopsworks feature store\n",
    "project = hopsworks.login(\n",
    "    # Hopsworks project name\n",
    "    project=config.HOPSWORKS_PROJECT_NAME,\n",
    "    # Hopsworks feature store name\n",
    "    api_key_value=config.HOPSWORKS_API_KEY,\n",
    ")\n",
    "\n",
    "feature_store = project.get_feature_store()\n",
    "\n",
    "# connect to feature group\n",
    "feature_group = feature_store.get_feature_group(\n",
    "    name=config.FEATURE_GROUP_NAME,\n",
    "    version=config.FEATURE_GROUP_VERSION\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature view already exists. Skipping creation.\n"
     ]
    }
   ],
   "source": [
    "# create feature view if does not exist yet\n",
    "try:\n",
    "    feature_store.create_feature_view(\n",
    "        name=config.FEATURE_VIEW_NAME,\n",
    "        version=config.FEATURE_VIEW_VERSION,\n",
    "        query=feature_group.select_all()\n",
    "    )\n",
    "except:\n",
    "    print('Feature view already exists. Skipping creation.')\n",
    "\n",
    "# get feature view\n",
    "feature_view = feature_store.get_feature_view(\n",
    "    name=config.FEATURE_VIEW_NAME,\n",
    "    version=config.FEATURE_VIEW_VERSION\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DeprecationWarning: ssl.PROTOCOL_TLS is deprecated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-02 08:53:43,950 INFO: USE `taxi_demand_api_featurestore`\n",
      "2023-10-02 08:53:44,951 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 '1970-01-01 12:16:40.000' AND `fg0`.`pickup_hour` < TIMESTAMP '2023-10-02 11:53:41.000'\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",
      "VersionWarning: Incremented version to `2`.\n"
     ]
    }
   ],
   "source": [
    "ts_data, _ = feature_view.training_data(\n",
    "    description=\"Time-series hourly taxi rides\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                pickup_hour  rides  pickup_location_id\n",
      "2818921 2022-01-01 00:00:00      0                   1\n",
      "1222681 2022-01-01 01:00:00      0                   1\n",
      "2552147 2022-01-01 02:00:00      0                   1\n",
      "280548  2022-01-01 03:00:00      0                   1\n",
      "3194290 2022-01-01 04:00:00      1                   1\n",
      "3360201 2022-01-01 05:00:00      1                   1\n",
      "1460822 2022-01-01 06:00:00      0                   1\n",
      "395374  2022-01-01 07:00:00      2                   1\n",
      "2156465 2022-01-01 08:00:00      0                   1\n",
      "1615181 2022-01-01 09:00:00      0                   1\n"
     ]
    }
   ],
   "source": [
    "print(ts_data.head(10))\n",
    "\n",
    "ts_data.sort_values(by=['pickup_location_id', 'pickup_hour'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ts(ts_data: pd.DataFrame, locations: Optional[List[int]] = None):\n",
    "    \"\"\"\n",
    "    Plot time-series data.\n",
    "\n",
    "    Args:\n",
    "    - ts_data: pandas DataFrame containing the time-series data to plot.\n",
    "    - locations: optional list of integers representing the pickup location IDs to plot.\n",
    "\n",
    "    Returns:\n",
    "    - None\n",
    "    \"\"\"\n",
    "    ts_data_to_plot = ts_data[ts_data.pickup_location_id.isin(locations)] if locations else ts_data\n",
    "\n",
    "    fig = px.line(\n",
    "        ts_data_to_plot,\n",
    "        x=\"pickup_hour\",\n",
    "        y=\"rides\",\n",
    "        color='pickup_location_id',\n",
    "        template='none',\n",
    "    )\n",
    "\n",
    "    fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "pickup_location_id=43<br>pickup_hour=%{x}<br>rides=%{y}<extra></extra>",
         "legendgroup": "43",
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          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
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         "mode": "lines",
         "name": "43",
         "showlegend": true,
         "type": "scattergl",
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         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "title": {
          "text": "pickup_location_id"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "scatter": [
           {
            "type": "scatter"
           }
          ]
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "pickup_hour"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "rides"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ts(ts_data, locations=[43])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 265/265 [37:29<00:00,  8.49s/it]\n"
     ]
    }
   ],
   "source": [
    "features, targets = transform_ts_data_into_features_and_target(\n",
    "    ts_data=ts_data,\n",
    "    input_seq_len=24*28,\n",
    "    step_size=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features_and_targets.shape=(3682175, 675)\n"
     ]
    }
   ],
   "source": [
    "features_and_targets = features.copy()\n",
    "\n",
    "features_and_targets['target'] = targets\n",
    "\n",
    "print(f'{features_and_targets.shape=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rides_previous_672_hour</th>\n",
       "      <th>rides_previous_671_hour</th>\n",
       "      <th>rides_previous_670_hour</th>\n",
       "      <th>rides_previous_669_hour</th>\n",
       "      <th>rides_previous_668_hour</th>\n",
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       "      <th>rides_previous_664_hour</th>\n",
       "      <th>rides_previous_663_hour</th>\n",
       "      <th>...</th>\n",
       "      <th>rides_previous_7_hour</th>\n",
       "      <th>rides_previous_6_hour</th>\n",
       "      <th>rides_previous_5_hour</th>\n",
       "      <th>rides_previous_4_hour</th>\n",
       "      <th>rides_previous_3_hour</th>\n",
       "      <th>rides_previous_2_hour</th>\n",
       "      <th>rides_previous_1_hour</th>\n",
       "      <th>pickup_hour</th>\n",
       "      <th>pickup_location_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</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>2022-01-29 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</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>2022-01-29 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</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>2022-01-29 05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</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>2022-01-29 06:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</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>2022-01-29 07:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</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>2022-01-29 08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</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>2022-01-29 09:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 675 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   rides_previous_672_hour  rides_previous_671_hour  rides_previous_670_hour  \\\n",
       "0                      0.0                      0.0                      0.0   \n",
       "1                      0.0                      0.0                      0.0   \n",
       "2                      0.0                      0.0                      1.0   \n",
       "3                      0.0                      1.0                      1.0   \n",
       "4                      1.0                      1.0                      0.0   \n",
       "5                      1.0                      0.0                      2.0   \n",
       "6                      0.0                      2.0                      0.0   \n",
       "7                      2.0                      0.0                      0.0   \n",
       "8                      0.0                      0.0                      1.0   \n",
       "9                      0.0                      1.0                      2.0   \n",
       "\n",
       "   rides_previous_669_hour  rides_previous_668_hour  rides_previous_667_hour  \\\n",
       "0                      0.0                      1.0                      1.0   \n",
       "1                      1.0                      1.0                      0.0   \n",
       "2                      1.0                      0.0                      2.0   \n",
       "3                      0.0                      2.0                      0.0   \n",
       "4                      2.0                      0.0                      0.0   \n",
       "5                      0.0                      0.0                      1.0   \n",
       "6                      0.0                      1.0                      2.0   \n",
       "7                      1.0                      2.0                      1.0   \n",
       "8                      2.0                      1.0                      5.0   \n",
       "9                      1.0                      5.0                      5.0   \n",
       "\n",
       "   rides_previous_666_hour  rides_previous_665_hour  rides_previous_664_hour  \\\n",
       "0                      0.0                      2.0                      0.0   \n",
       "1                      2.0                      0.0                      0.0   \n",
       "2                      0.0                      0.0                      1.0   \n",
       "3                      0.0                      1.0                      2.0   \n",
       "4                      1.0                      2.0                      1.0   \n",
       "5                      2.0                      1.0                      5.0   \n",
       "6                      1.0                      5.0                      5.0   \n",
       "7                      5.0                      5.0                     13.0   \n",
       "8                      5.0                     13.0                      3.0   \n",
       "9                     13.0                      3.0                      3.0   \n",
       "\n",
       "   rides_previous_663_hour  ...  rides_previous_7_hour  rides_previous_6_hour  \\\n",
       "0                      0.0  ...                    2.0                    0.0   \n",
       "1                      1.0  ...                    0.0                    1.0   \n",
       "2                      2.0  ...                    1.0                    0.0   \n",
       "3                      1.0  ...                    0.0                    0.0   \n",
       "4                      5.0  ...                    0.0                    0.0   \n",
       "5                      5.0  ...                    0.0                    0.0   \n",
       "6                     13.0  ...                    0.0                    0.0   \n",
       "7                      3.0  ...                    0.0                    0.0   \n",
       "8                      3.0  ...                    0.0                    0.0   \n",
       "9                      1.0  ...                    0.0                    0.0   \n",
       "\n",
       "   rides_previous_5_hour  rides_previous_4_hour  rides_previous_3_hour  \\\n",
       "0                    1.0                    0.0                    0.0   \n",
       "1                    0.0                    0.0                    0.0   \n",
       "2                    0.0                    0.0                    0.0   \n",
       "3                    0.0                    0.0                    0.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "5                    0.0                    0.0                    0.0   \n",
       "6                    0.0                    0.0                    0.0   \n",
       "7                    0.0                    0.0                    0.0   \n",
       "8                    0.0                    0.0                    0.0   \n",
       "9                    0.0                    0.0                    0.0   \n",
       "\n",
       "   rides_previous_2_hour  rides_previous_1_hour         pickup_hour  \\\n",
       "0                    0.0                    0.0 2022-01-29 00:00:00   \n",
       "1                    0.0                    0.0 2022-01-29 01:00:00   \n",
       "2                    0.0                    0.0 2022-01-29 02:00:00   \n",
       "3                    0.0                    0.0 2022-01-29 03:00:00   \n",
       "4                    0.0                    0.0 2022-01-29 04:00:00   \n",
       "5                    0.0                    0.0 2022-01-29 05:00:00   \n",
       "6                    0.0                    0.0 2022-01-29 06:00:00   \n",
       "7                    0.0                    0.0 2022-01-29 07:00:00   \n",
       "8                    0.0                    0.0 2022-01-29 08:00:00   \n",
       "9                    0.0                    0.0 2022-01-29 09:00:00   \n",
       "\n",
       "   pickup_location_id  target  \n",
       "0                   1     0.0  \n",
       "1                   1     0.0  \n",
       "2                   1     0.0  \n",
       "3                   1     0.0  \n",
       "4                   1     0.0  \n",
       "5                   1     0.0  \n",
       "6                   1     0.0  \n",
       "7                   1     0.0  \n",
       "8                   1     0.0  \n",
       "9                   1     0.0  \n",
       "\n",
       "[10 rows x 675 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_and_targets.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape=(2715720, 674)\n",
      "y_train.shape=(2715720,)\n",
      "X_test.shape=(966455, 674)\n",
      "y_test.shape=(966455,)\n"
     ]
    }
   ],
   "source": [
    "# set training date from january 2022 up until april 2023\n",
    "# and test date from april 2023 up until july 2023\n",
    "training_end_date = pd.to_datetime(date(2023, 4, 1))\n",
    "\n",
    "X_train, y_train, X_test, y_test = train_test_split(\n",
    "    df=features_and_targets,\n",
    "    cutoff_date=training_end_date,\n",
    "    target_col_name='target'\n",
    ")\n",
    "\n",
    "# print shapes\n",
    "print(f'{X_train.shape=}')\n",
    "print(f'{y_train.shape=}')\n",
    "print(f'{X_test.shape=}')\n",
    "print(f'{y_test.shape=}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_objective(X_train, y_train):\n",
    "    def objective(trial: optuna.trial.Trial) -> float:\n",
    "        \"\"\"\n",
    "        Train a model with a set of hyperparameters and compute and validation error based on a TimeSeriesSplit.\n",
    "        \"\"\"\n",
    "        # define hyperparameters to tune\n",
    "        hyperparams = {\n",
    "            \"metric\": \"mae\",\n",
    "            \"verbose\": -1,\n",
    "            \"num_leaves\": trial.suggest_int(\"num_leaves\", 2, 256),\n",
    "            \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.2, 1.0),\n",
    "            \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.2, 1.0),\n",
    "            \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 3, 100),    \n",
    "        }\n",
    "\n",
    "        tss = TimeSeriesSplit(n_splits=2)\n",
    "        scores = []\n",
    "        for train_index, val_index in tss.split(X_train):\n",
    "            X_train_, X_val_ = X_train.iloc[train_index], X_train.iloc[val_index]\n",
    "            y_train_, y_val_ = y_train.iloc[train_index], y_train.iloc[val_index]\n",
    "\n",
    "            # create pipeline\n",
    "            pipeline = get_pipeline(**hyperparams)\n",
    "            pipeline.fit(X_train_, y_train_)\n",
    "\n",
    "            # evaluate the model\n",
    "            y_pred = pipeline.predict(X_val_)\n",
    "            mae = mean_absolute_error(y_val_, y_pred)\n",
    "            scores.append(mae)\n",
    "\n",
    "        # return the mean scores\n",
    "        return np.array(scores).mean()\n",
    "\n",
    "    return objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-10-02 22:26:28,224] A new study created in memory with name: no-name-8483684e-b0d9-468f-97cc-bf13e8e98c4e\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] bagging_fraction is set=0.9008727736790323, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9008727736790323\n",
      "[LightGBM] [Warning] feature_fraction is set=0.32910402197251526, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.32910402197251526\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.9008727736790323, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9008727736790323\n",
      "[LightGBM] [Warning] feature_fraction is set=0.32910402197251526, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.32910402197251526\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2023-10-02 22:31:20,853] Trial 0 finished with value: 3.220451304802626 and parameters: {'num_leaves': 135, 'feature_fraction': 0.32910402197251526, 'bagging_fraction': 0.9008727736790323, 'min_child_samples': 8}. Best is trial 0 with value: 3.220451304802626.\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study(direction=\"minimize\")\n",
    "study.optimize(create_objective(X_train, y_train), n_trials=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_params={'num_leaves': 135, 'feature_fraction': 0.32910402197251526, 'bagging_fraction': 0.9008727736790323, 'min_child_samples': 8}\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.9008727736790323, subsample=1.0 will be ignored. Current value: bagging_fraction=0.9008727736790323\n",
      "[LightGBM] [Warning] feature_fraction is set=0.32910402197251526, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.32910402197251526\n"
     ]
    }
   ],
   "source": [
    "best_params = study.best_trial.params\n",
    "print(f'{best_params=}')\n",
    "\n",
    "pipeline = get_pipeline(**best_params)\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "predictions = pipeline.predict(X_test)\n",
    "test_mae = mean_absolute_error(y_test, predictions)\n",
    "print(f'{test_mae=:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(pipeline, MODELS_DIR / 'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from schema import Schema\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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