model/01-train-and-deploy-model/model_training_deployment.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "50fdf956-7baf-4998-90ae-1a90db603182",
   "metadata": {},
   "source": [
    "### model training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8eb136a5-0eb8-472e-a08a-f0af2e304178",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "from math import sqrt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pins\n",
    "import pyodbc\n",
    "import vetiver\n",
    "from dotenv import load_dotenv\n",
    "from pprint import pprint\n",
    "from rsconnect.api import RSConnectServer\n",
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "from sklearn.model_selection import TimeSeriesSplit, train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import OneHotEncoder, FunctionTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5f4a6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import custom functions\n",
    "from bikeshare.data import clean_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d7a18c1-2e8e-4251-8bdf-9236aaaa889b",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "load_dotenv(override=True)\n",
    "\n",
    "rsc_server = os.getenv(\"CONNECT_SERVER\")\n",
    "rsc_key = os.getenv(\"CONNECT_API_KEY\")\n",
    "connect_server = RSConnectServer(url=rsc_server,api_key=rsc_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c9058b",
   "metadata": {},
   "source": [
    "## Raw data\n",
    "\n",
    "Read in the raw data from the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ad432dc-87c3-4155-89f7-ff23e2022822",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>hour</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>n_bikes</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.919086</td>\n",
       "      <td>-77.034502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.110314</td>\n",
       "      <td>-77.182669</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>298</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.114688</td>\n",
       "      <td>-77.171487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>298</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.114688</td>\n",
       "      <td>-77.171487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>298</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>13.0</td>\n",
       "      <td>39.114688</td>\n",
       "      <td>-77.171487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>39093</th>\n",
       "      <td>125</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>6.0</td>\n",
       "      <td>38.897857</td>\n",
       "      <td>-77.026975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39094</th>\n",
       "      <td>326</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.964992</td>\n",
       "      <td>-77.103381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39095</th>\n",
       "      <td>326</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.964992</td>\n",
       "      <td>-77.103381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39096</th>\n",
       "      <td>326</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.964992</td>\n",
       "      <td>-77.103381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39097</th>\n",
       "      <td>101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.894832</td>\n",
       "      <td>-76.987633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39098 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  hour        date  month     dow  n_bikes        lat        lon\n",
       "0      453   4.0  2023-06-09    6.0  Friday     16.0  38.919086 -77.034502\n",
       "1      299   0.0  2023-06-09    6.0  Friday      1.0  39.110314 -77.182669\n",
       "2      298   8.0  2023-06-09    6.0  Friday     13.0  39.114688 -77.171487\n",
       "3      298  10.0  2023-06-09    6.0  Friday     13.0  39.114688 -77.171487\n",
       "4      298  12.0  2023-06-09    6.0  Friday     13.0  39.114688 -77.171487\n",
       "...    ...   ...         ...    ...     ...      ...        ...        ...\n",
       "39093  125   8.0  2023-05-29    5.0  Monday      6.0  38.897857 -77.026975\n",
       "39094  326  16.0  2023-05-29    5.0  Monday      8.0  38.964992 -77.103381\n",
       "39095  326  18.0  2023-05-29    5.0  Monday      8.0  38.964992 -77.103381\n",
       "39096  326  20.0  2023-05-29    5.0  Monday      8.0  38.964992 -77.103381\n",
       "39097  101   0.0  2023-05-29    5.0  Monday      4.0  38.894832 -76.987633\n",
       "\n",
       "[39098 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read table built by R ETL process in the database\n",
    "connection = pyodbc.connect('DSN=Content DB')\n",
    "\n",
    "sql = \"select * from bike_model_data where date in (select distinct date from bike_model_data order by date desc limit 12);\"\n",
    "\n",
    "all_days = pd.read_sql_query(sql, connection)\n",
    "all_days.sort_values(by='date', inplace=True, ascending=False)\n",
    "all_days = all_days.reset_index(drop=True)\n",
    "\n",
    "all_days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab35e2fb",
   "metadata": {},
   "source": [
    "## Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60b4ce5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = all_days.drop(columns=[\"n_bikes\", \"id\", \"date\"])\n",
    "y = all_days[[\"n_bikes\"]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf9f16e6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>dow</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>31122</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>38.934600</td>\n",
       "      <td>-76.995500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37816</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>38.909394</td>\n",
       "      <td>-77.048728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30580</th>\n",
       "      <td>10.0</td>\n",
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       "      <td>Wednesday</td>\n",
       "      <td>38.813485</td>\n",
       "      <td>-77.049468</td>\n",
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       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>38.903584</td>\n",
       "      <td>-77.044789</td>\n",
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       "    <tr>\n",
       "      <th>30222</th>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Wednesday</td>\n",
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       "      <th>34667</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>38.887312</td>\n",
       "      <td>-77.025762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12461</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>38.990249</td>\n",
       "      <td>-77.029350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24822</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>38.988562</td>\n",
       "      <td>-77.096539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26272</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>38.927497</td>\n",
       "      <td>-76.997194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29323 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       hour  month        dow        lat        lon\n",
       "31122  10.0    5.0  Wednesday  38.934600 -76.995500\n",
       "37816   8.0    5.0     Monday  38.909394 -77.048728\n",
       "30580  10.0    5.0  Wednesday  38.813485 -77.049468\n",
       "2004   18.0    6.0   Thursday  38.903584 -77.044789\n",
       "30222  14.0    5.0  Wednesday  38.897612 -77.080851\n",
       "...     ...    ...        ...        ...        ...\n",
       "34667   2.0    5.0    Tuesday  38.887312 -77.025762\n",
       "12461   4.0    6.0     Monday  38.990249 -77.029350\n",
       "24822  18.0    6.0     Friday  38.908142 -77.038359\n",
       "3540    0.0    6.0   Thursday  38.988562 -77.096539\n",
       "26272   6.0    6.0   Thursday  38.927497 -76.997194\n",
       "\n",
       "[29323 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20e2eaf5",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_bikes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31122</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <th>2004</th>\n",
       "      <td>14.0</td>\n",
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       "      <th>30222</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>7.0</td>\n",
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       "       n_bikes\n",
       "31122      2.0\n",
       "37816     15.0\n",
       "30580      6.0\n",
       "2004      14.0\n",
       "30222      3.0\n",
       "...        ...\n",
       "34667     14.0\n",
       "12461      5.0\n",
       "24822      7.0\n",
       "3540       1.0\n",
       "26272      7.0\n",
       "\n",
       "[29323 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed6ada2",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b36155f0-6117-4870-99ef-30f028a6e8ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;clean-data&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function clean_data at 0x7fa6a3197130&gt;)),\n",
       "                (&#x27;regressor&#x27;,\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;clean-data&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function clean_data at 0x7fa6a3197130&gt;)),\n",
       "                (&#x27;regressor&#x27;,\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function clean_data at 0x7fa6a3197130&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=0)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('clean-data',\n",
       "                 FunctionTransformer(func=<function clean_data at 0x7fa6a3197130>)),\n",
       "                ('regressor',\n",
       "                 RandomForestRegressor(n_jobs=-1, random_state=0))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a random forest model\n",
    "model = Pipeline(\n",
    "    steps=[\n",
    "        (\"clean-data\", FunctionTransformer(clean_data)),\n",
    "        (\"regressor\", RandomForestRegressor(\n",
    "            n_estimators=100, \n",
    "            random_state=0, \n",
    "            n_jobs=-1\n",
    "        ))\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b23cc484-8153-4f66-a3b6-1ac58e8c99c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 2.826245\n"
     ]
    }
   ],
   "source": [
    "# test the random forest model\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# compare predictions\n",
    "test_mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = sqrt(test_mse)\n",
    "print('RMSE: %f' % rmse)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "055fd964-0478-4cb2-bb89-6fe6475d6da3",
   "metadata": {},
   "source": [
    "## Deploy model\n",
    "\n",
    "Deploy the model with vetiver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c23fec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_name=\"sam.edwardes\"\n",
    "pin_name = f\"{user_name}/bikeshare-rf-python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd5657b5-a2d4-4115-b729-22adeeccf497",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# convert the random forest model into a vetiver model\n",
    "v = vetiver.VetiverModel(\n",
    "    model=model,\n",
    "    model_name=pin_name,\n",
    "    prototype_data=X_train.head(1),\n",
    "    description=\"A model to predict the number of bikes that will be available.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa856aef-8792-40ac-832f-f768e07811e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a board on Posit Connect\n",
    "board = pins.board_connect(\n",
    "    server_url=\"https://colorado.posit.co/rsc\",\n",
    "    allow_pickle_read=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f194413b-8c71-487c-acef-305eef16b800",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model Cards provide a framework for transparent, responsible reporting. \n",
      " Use the vetiver `.qmd` Quarto template as a place to start, \n",
      " with vetiver.model_card()\n",
      "Writing pin:\n",
      "Name: 'sam.edwardes/bikeshare-rf-python'\n",
      "Version: 20230609T223110Z-a9c79\n"
     ]
    }
   ],
   "source": [
    "# write the vetiver model as pin to Posit Connect\n",
    "vetiver.pin_read_write.vetiver_pin_write(\n",
    "    board=board, \n",
    "    model=v\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2327a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>75706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  version\n",
       "0   75565\n",
       "1   75569\n",
       "2   75571\n",
       "3   75685\n",
       "4   75693\n",
       "5   75696\n",
       "6   75701\n",
       "7   75704\n",
       "8   75706"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board.pin_versions(pin_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b720a03f-ccae-4a28-8d3e-80407a80e89c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[0mValidating server...\u001b[0m\u001b[32;20m \t[OK]\n",
      "\u001b[0m\u001b[0mValidating app mode...\u001b[0m\u001b[32;20m \t[OK]\n",
      "\u001b[0m\u001b[0mMaking bundle ...\u001b[0m\u001b[32;20m \t[OK]\n",
      "\u001b[0m\u001b[0mDeploying bundle ...\u001b[0m\u001b[32;20m \t[OK]\n",
      "\u001b[0m\u001b[0mSaving deployed information...\u001b[0m\u001b[32;20m \t[OK]\n",
      "\u001b[0m\u001b[0mBuilding FastAPI application...\u001b[0m\n",
      "\u001b[0mBundle created with Python version 3.10.11 is compatible with environment Kubernetes::ghcr.io/rstudio/content-pro:r4.1.3-py3.10.11-ubuntu2204 with Python version 3.10.11 from /opt/python/3.10.11/bin/python3 \u001b[0m\n",
      "\u001b[0mBundle requested Python version 3.10.11; using /opt/python/3.10.11/bin/python3 from Kubernetes::ghcr.io/rstudio/content-pro:r4.1.3-py3.10.11-ubuntu2204 which has version 3.10.11\u001b[0m\n",
      "\u001b[0mDetermining session server location ...\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.381217506 [rsc-session] Content GUID: 28923e33-dcb6-4774-b753-bf1d4c367579\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.381937397 [rsc-session] Content ID: 16788\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.381954293 [rsc-session] Bundle ID: 75707\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.530239213 Running on host: python-environment-restore-b7cz5-8vnj8\u001b[0m\n",
      "\u001b[0mConnecting to session server http://service-2f085301-ff21-4ea8-82a8-e212405b732d.rstudio-connect:50734 ...\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.970025130 Linux distribution: Ubuntu 22.04.2 LTS (jammy)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.972936780 Running as user: uid=999 gid=999 groups=999\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.972948661 Connect version: 2023.05.0\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.973058077 LANG: en_US.UTF-8\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.973099931 Working directory: /opt/rstudio-connect/mnt/app\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.973114210 Building environment using Python 3.10.11 (main, Jun  4 2023, 22:34:21) [GCC 11.3.0] at /opt/python/3.10.11/bin/python3\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:19.989056617 Creating environment: MIR5r4bk5IQWKKeAX4NTAw\u001b[0m\n",
      "\u001b[0mConnected to session server http://service-2f085301-ff21-4ea8-82a8-e212405b732d.rstudio-connect:50734\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:33.293047212 Requirement already satisfied: pip in /opt/rstudio-connect/mnt/python-environments/ghcr.io_rstudio_content-pro__r4.1.3-py3.10.11-ubuntu2204/pip/3.10.11/MIR5r4bk5IQWKKeAX4NTAw/lib/python3.10/site-packages (23.0.1)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:33.574187740 Collecting pip\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:33.691870587   Downloading pip-23.1.2-py3-none-any.whl (2.1 MB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:33.764693937      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1/2.1 MB 31.1 MB/s eta 0:00:00\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:33.777878694 Requirement already satisfied: setuptools in /opt/rstudio-connect/mnt/python-environments/ghcr.io_rstudio_content-pro__r4.1.3-py3.10.11-ubuntu2204/pip/3.10.11/MIR5r4bk5IQWKKeAX4NTAw/lib/python3.10/site-packages (65.5.0)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.114098480 Collecting setuptools\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.130598540   Downloading setuptools-67.8.0-py3-none-any.whl (1.1 MB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.148201398      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 76.1 MB/s eta 0:00:00\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.216065386 Collecting wheel\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.230645092   Downloading wheel-0.40.0-py3-none-any.whl (64 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.239131154      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 64.5/64.5 kB 11.1 MB/s eta 0:00:00\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:34.393138681 Installing collected packages: wheel, setuptools, pip\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:35.079019908   Attempting uninstall: setuptools\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:35.081275226     Found existing installation: setuptools 65.5.0\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:35.462955110     Uninstalling setuptools-65.5.0:\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:35.705107823       Successfully uninstalled setuptools-65.5.0\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:38.584863620   Attempting uninstall: pip\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:38.591953710     Found existing installation: pip 23.0.1\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:39.372899950     Uninstalling pip-23.0.1:\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:39.458774553       Successfully uninstalled pip-23.0.1\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:46.080196762 Successfully installed pip-23.1.2 setuptools-67.8.0 wheel-0.40.0\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:47.452537056 Collecting bikeshare==0.1.0 (from -r python/requirements.txt (line 8))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:47.801266297   Downloading https://github.com/sol-eng/bike_predict_python/releases/download/v0.0.1/bikeshare-0.1.0-py3-none-any.whl (1.6 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:48.603685692 Collecting pandas==2.0.2 (from -r python/requirements.txt (line 1))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:48.680139577   Using cached pandas-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.036911356 Collecting pins==0.8.1 (from -r python/requirements.txt (line 2))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.052014142   Using cached pins-0.8.1-py2.py3-none-any.whl (110 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.285893976 Collecting python-dotenv==1.0.0 (from -r python/requirements.txt (line 3))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.303855346   Using cached python_dotenv-1.0.0-py3-none-any.whl (19 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.660517750 Collecting scikit-learn==1.2.2 (from -r python/requirements.txt (line 4))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.719067545   Using cached scikit_learn-1.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.6 MB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:32:49.923040269 Collecting vetiver==0.2.1 (from -r python/requirements.txt (line 5))\u001b[0m\n",
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      "\u001b[0m2023/06/09 22:33:04.030774693 Requirement already satisfied: pip>=10.0.0 in /opt/rstudio-connect/mnt/python-environments/ghcr.io_rstudio_content-pro__r4.1.3-py3.10.11-ubuntu2204/pip/3.10.11/MIR5r4bk5IQWKKeAX4NTAw/lib/python3.10/site-packages (from rsconnect-python>=1.8.0->vetiver==0.2.1->-r python/requirements.txt (line 5)) (23.1.2)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.192380448 Collecting semver<3.0.0,>=2.0.0 (from rsconnect-python>=1.8.0->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.206271489   Using cached semver-2.13.0-py2.py3-none-any.whl (12 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.362488363 Collecting pyjwt>=2.4.0 (from rsconnect-python>=1.8.0->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.376488288   Using cached PyJWT-2.7.0-py3-none-any.whl (22 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.595861132 Collecting starlette (from -r python/requirements.txt (line 12))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.609312365   Using cached starlette-0.27.0-py3-none-any.whl (66 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.798697380 Collecting typing-extensions>=4.2.0 (from pydantic->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.812848322   Using cached typing_extensions-4.6.3-py3-none-any.whl (31 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.985143802 Collecting certifi (from httpx->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:04.996256315   Using cached certifi-2023.5.7-py3-none-any.whl (156 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.138147750 Collecting httpcore<0.18.0,>=0.15.0 (from httpx->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.154446874   Using cached httpcore-0.17.2-py3-none-any.whl (72 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.353146372 Collecting build (from pip-tools->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.368762591   Using cached build-0.10.0-py3-none-any.whl (17 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.397214994 Requirement already satisfied: setuptools in /opt/rstudio-connect/mnt/python-environments/ghcr.io_rstudio_content-pro__r4.1.3-py3.10.11-ubuntu2204/pip/3.10.11/MIR5r4bk5IQWKKeAX4NTAw/lib/python3.10/site-packages (from pip-tools->vetiver==0.2.1->-r python/requirements.txt (line 5)) (67.8.0)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.397232689 Requirement already satisfied: wheel in /opt/rstudio-connect/mnt/python-environments/ghcr.io_rstudio_content-pro__r4.1.3-py3.10.11-ubuntu2204/pip/3.10.11/MIR5r4bk5IQWKKeAX4NTAw/lib/python3.10/site-packages (from pip-tools->vetiver==0.2.1->-r python/requirements.txt (line 5)) (0.40.0)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.541704870 Collecting tenacity>=6.2.0 (from plotly->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.552981709   Using cached tenacity-8.2.2-py3-none-any.whl (24 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.799643766 Collecting charset-normalizer<4,>=2 (from requests->pins==0.8.1->-r python/requirements.txt (line 2))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.814881925   Using cached charset_normalizer-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (199 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:05.987668717 Collecting urllib3<3,>=1.21.1 (from requests->pins==0.8.1->-r python/requirements.txt (line 2))\u001b[0m\n",
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      "\u001b[0m2023/06/09 22:33:06.265694695 Collecting parso<0.9.0,>=0.8.0 (from jedi>=0.16->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
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      "\u001b[0m2023/06/09 22:33:06.454498133 Collecting ptyprocess>=0.5 (from pexpect>4.3->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:06.469410363   Using cached ptyprocess-0.7.0-py2.py3-none-any.whl (13 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:06.651989050 Collecting wcwidth (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:06.662810300   Using cached wcwidth-0.2.6-py2.py3-none-any.whl (29 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.133099569 Collecting pyproject_hooks (from build->pip-tools->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.144053172   Using cached pyproject_hooks-1.0.0-py3-none-any.whl (9.3 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.263041590 Collecting tomli>=1.1.0 (from build->pip-tools->vetiver==0.2.1->-r python/requirements.txt (line 5))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.277011448   Using cached tomli-2.0.1-py3-none-any.whl (12 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.544701219 Collecting executing>=1.2.0 (from stack-data->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.558127928   Using cached executing-1.2.0-py2.py3-none-any.whl (24 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.714078193 Collecting asttokens>=2.1.0 (from stack-data->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.728123129   Using cached asttokens-2.2.1-py2.py3-none-any.whl (26 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.880378844 Collecting pure-eval (from stack-data->ipython>=7.23.1->ipykernel==6.23.1->-r python/requirements.txt (line 6))\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:07.890808364   Using cached pure_eval-0.2.2-py3-none-any.whl (11 kB)\u001b[0m\n",
      "\u001b[0m2023/06/09 22:33:09.091409974 Installing collected packages: wcwidth, pytz, pure-eval, ptyprocess, pickleshare, executing, backcall, appdirs, zipp, xxhash, websockets, urllib3, tzdata, typing-extensions, traitlets, tornado, tomli, threadpoolctl, tenacity, sniffio, six, semver, pyzmq, pyyaml, python-dotenv, pyodbc, pyjwt, pygments, psutil, prompt-toolkit, platformdirs, pexpect, parso, packaging, numpy, nest-asyncio, MarkupSafe, joblib, importlib-resources, idna, humanize, h11, fsspec, exceptiongroup, decorator, debugpy, click, charset-normalizer, certifi, aiofiles, uvicorn, scipy, rsconnect-python, requests, python-dateutil, pyproject_hooks, pydantic, plotly, matplotlib-inline, jupyter-core, jinja2, jedi, importlib-metadata, comm, asttokens, anyio, starlette, stack-data, scikit-learn, pandas, jupyter-client, httpcore, build, pip-tools, pins, ipython, httpx, fastapi, bikeshare, vetiver, ipykernel\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# use Vetiver provided Posit Connect deployment function \n",
    "# to deploy the model as a FASTApi\n",
    "vetiver.deploy_rsconnect(\n",
    "    connect_server=connect_server,\n",
    "    board=board,\n",
    "    pin_name=pin_name,\n",
    "    version=board.pin_versions(pin_name).tail(1)[\"version\"].values[0],\n",
    "    title=\"Random Forest model for Bikeshare Python\",\n",
    "    app_id=\"28923e33-dcb6-4774-b753-bf1d4c367579\",\n",
    "    extra_files=[\"requirements.txt\"]\n",
    ")"
   ]
  }
 ],
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