Forecasting_ChicagoBus/Snowpark_Forecasting_Bus.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5f02f3fc",
   "metadata": {},
   "source": [
    "# Snowpark Intro Notebook - ML Forecasting\n",
    "This notebook introduces several key features of Snowpark in the process of training a machine learning model for forecasting Chicago bus ridership.  \n",
    "\n",
    "* Establish secure connection to Snowflake\n",
    "* Load features and target from Snowflake table into Snowpark DataFrame\n",
    "* Prepare features for model training\n",
    "* Train ML model using Snowpark ML distributed processing\n",
    "* Save the model to the Snowflake Model Registry\n",
    "* Run model predictions inside Snowflake\n",
    "\n",
    "This notebook is intended to highlight Snowflake functionality and should not be taken as a best practice for time series forecasting. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e2e1c4a",
   "metadata": {},
   "source": [
    "## 1. Setup Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "persistent-reasoning",
   "metadata": {
    "papermill": {
     "duration": 0.872363,
     "end_time": "2021-05-15T09:33:41.413139",
     "exception": false,
     "start_time": "2021-05-15T09:33:40.540776",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Snowflake connector\n",
    "from snowflake import connector\n",
    "#from snowflake.ml.utils import connection_params\n",
    "\n",
    "# Snowpark for Python\n",
    "from snowflake.snowpark.session import Session\n",
    "from snowflake.snowpark.types import Variant\n",
    "from snowflake.snowpark.version import VERSION\n",
    "from snowflake.snowpark import functions as F\n",
    "from snowflake.snowpark.types import *\n",
    "\n",
    "# Snowpark ML\n",
    "from snowflake.ml.modeling.compose import ColumnTransformer\n",
    "from snowflake.ml.modeling.pipeline import Pipeline\n",
    "from snowflake.ml.modeling.preprocessing import StandardScaler, OrdinalEncoder\n",
    "from snowflake.ml.modeling.impute import SimpleImputer\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "from snowflake.ml.modeling.xgboost import XGBRegressor\n",
    "from snowflake.ml import version\n",
    "mlversion = version.VERSION\n",
    "\n",
    "\n",
    "# Misc\n",
    "import pandas as pd\n",
    "import json\n",
    "import logging \n",
    "logger = logging.getLogger(\"snowflake.snowpark.session\")\n",
    "logger.setLevel(logging.ERROR)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b855e80e",
   "metadata": {},
   "source": [
    "## Establish Secure Connection to Snowflake\n",
    "\n",
    "Using the Snowpark Python API, it’s quick and easy to establish a secure connection between Snowflake and Notebook.\n",
    " *Note: Other connection options include Username/Password, MFA, OAuth, Okta, SSO*\n",
    "\n",
    "I like to store my credentials in creds.json so they aren't in the notebook.\n",
    "The file should look like this:\n",
    "```\n",
    "{\n",
    "    \"account\": \"awb99999\",\n",
    "    \"user\": \"your_user_name\",\n",
    "    \"password\": \"your_password\",\n",
    "    \"warehouse\": \"your_warehouse\"\n",
    "  }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "953f6906",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../../creds.json') as f:\n",
    "    data = json.load(f)\n",
    "    USERNAME = data['user']\n",
    "    PASSWORD = data['password']\n",
    "    SF_ACCOUNT = data['account']\n",
    "    SF_WH = data['warehouse']\n",
    "\n",
    "CONNECTION_PARAMETERS = {\n",
    "   \"account\": SF_ACCOUNT,\n",
    "   \"user\": USERNAME,\n",
    "   \"password\": PASSWORD,\n",
    "}\n",
    "\n",
    "session = Session.builder.configs(CONNECTION_PARAMETERS).create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c333eb49",
   "metadata": {},
   "source": [
    "Verify everything is connected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4fb1f7af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User                        : RSHAH\n",
      "Role                        : \"RAJIV\"\n",
      "Database                    : \"RAJIV\"\n",
      "Schema                      : \"DOCAI\"\n",
      "Warehouse                   : \"RAJIV\"\n",
      "Snowflake version           : 8.17.3\n",
      "Snowpark for Python version : 1.11.1\n",
      "Snowflake ML version        : 1.5.0\n"
     ]
    }
   ],
   "source": [
    "snowflake_environment = session.sql('select current_user(), current_version()').collect()\n",
    "snowpark_version = VERSION\n",
    "\n",
    "# Current Environment Details\n",
    "print('User                        : {}'.format(snowflake_environment[0][0]))\n",
    "print('Role                        : {}'.format(session.get_current_role()))\n",
    "print('Database                    : {}'.format(session.get_current_database()))\n",
    "print('Schema                      : {}'.format(session.get_current_schema()))\n",
    "print('Warehouse                   : {}'.format(session.get_current_warehouse()))\n",
    "print('Snowflake version           : {}'.format(snowflake_environment[0][1]))\n",
    "print('Snowpark for Python version : {}.{}.{}'.format(snowpark_version[0],snowpark_version[1],snowpark_version[2]))\n",
    "print('Snowflake ML version        : {}.{}.{}'.format(mlversion[0],mlversion[2],mlversion[4]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e11789e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'\").collect()\n",
    "session.sql(\"USE SCHEMA PUBLIC\").collect()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2817d70c",
   "metadata": {},
   "source": [
    "## 2. Load Data in Snowflake "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df957705",
   "metadata": {},
   "source": [
    "Let's get the data (900k rows) and also make the column names all upper cases. It's easier to work with columns names that aren't case sensitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8dfb28da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(893603, 4)\n",
      "ROUTE      object\n",
      "DATE       object\n",
      "DAYTYPE    object\n",
      "RIDES       int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>ROUTE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>DAYTYPE</th>\n",
       "      <th>RIDES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>U</td>\n",
       "      <td>7354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>U</td>\n",
       "      <td>9288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>U</td>\n",
       "      <td>6048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>U</td>\n",
       "      <td>6309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>01/01/2001</td>\n",
       "      <td>U</td>\n",
       "      <td>11207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ROUTE        DATE DAYTYPE  RIDES\n",
       "0     3  01/01/2001       U   7354\n",
       "1     4  01/01/2001       U   9288\n",
       "2     6  01/01/2001       U   6048\n",
       "3     8  01/01/2001       U   6309\n",
       "4     9  01/01/2001       U  11207"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = pd.read_csv('CTA_Daily_Totals_by_Route.csv')\n",
    "df_clean.columns = df_clean.columns.str.upper()\n",
    "print (df_clean.shape)\n",
    "print (df_clean.dtypes)\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c72b14",
   "metadata": {},
   "source": [
    "Let's create a Snowpark dataframe and split the data for test/train. This operation is done inside Snowflake and not in your local environment. We will also save this as a table so we don't ever have to manually upload this dataset again.\n",
    "\n",
    "PRO TIP -- Snowpark will inherit the schema of a pandas dataframe into Snowflake. Either change your schema before importing or after it has landed in snowflake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77038199",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StructType([StructField('ROUTE', StringType(16777216), nullable=True), StructField('DATE', StringType(16777216), nullable=True), StructField('DAYTYPE', StringType(16777216), nullable=True), StructField('RIDES', LongType(), nullable=True)])\n"
     ]
    }
   ],
   "source": [
    "input_df = session.create_dataframe(df_clean)\n",
    "schema = input_df.schema\n",
    "print(schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a6209bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df.write.mode('overwrite').save_as_table('CHICAGO_BUS_RIDES')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3ac037",
   "metadata": {},
   "source": [
    "Let's read from the table, since that is generally what you will be doing in production. We have 893,000 rows of ridership data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2b86720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "893603\n",
      "----------------------------------------------\n",
      "|\"ROUTE\"  |\"DATE\"      |\"DAYTYPE\"  |\"RIDES\"  |\n",
      "----------------------------------------------\n",
      "|3        |01/01/2001  |U          |7354     |\n",
      "|4        |01/01/2001  |U          |9288     |\n",
      "|6        |01/01/2001  |U          |6048     |\n",
      "|8        |01/01/2001  |U          |6309     |\n",
      "|9        |01/01/2001  |U          |11207    |\n",
      "|10       |01/01/2001  |U          |385      |\n",
      "|11       |01/01/2001  |U          |610      |\n",
      "|12       |01/01/2001  |U          |3678     |\n",
      "|18       |01/01/2001  |U          |375      |\n",
      "|20       |01/01/2001  |U          |7096     |\n",
      "----------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = session.read.table('CHICAGO_BUS_RIDES')\n",
    "print (df.count())\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdb214c",
   "metadata": {},
   "source": [
    "## 3. Distributed Feature Engineering\n",
    "\n",
    "Let's add the Day of the week and then Aggregate the data by day.\n",
    "Let's join in weather data\n",
    "\n",
    "These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions. This dataset uses SMALL, but you can always move up to larger ones including Snowpark Optimized warehouses (16x memory per node than a standard warehouse), e.g., `session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "894622be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "50e5d5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------\n",
      "|\"ROUTE\"  |\"DAYTYPE\"  |\"RIDES\"  |\"DATE\"               |\"DAY_OF_WEEK\"  |\n",
      "-----------------------------------------------------------------------\n",
      "|3        |U          |7354     |2001-01-01 00:00:00  |1              |\n",
      "|4        |U          |9288     |2001-01-01 00:00:00  |1              |\n",
      "|6        |U          |6048     |2001-01-01 00:00:00  |1              |\n",
      "|8        |U          |6309     |2001-01-01 00:00:00  |1              |\n",
      "|9        |U          |11207    |2001-01-01 00:00:00  |1              |\n",
      "|10       |U          |385      |2001-01-01 00:00:00  |1              |\n",
      "|11       |U          |610      |2001-01-01 00:00:00  |1              |\n",
      "|12       |U          |3678     |2001-01-01 00:00:00  |1              |\n",
      "|18       |U          |375      |2001-01-01 00:00:00  |1              |\n",
      "|20       |U          |7096     |2001-01-01 00:00:00  |1              |\n",
      "-----------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from snowflake.snowpark.functions import col, to_timestamp, dayofweek, month,sum, listagg, lag\n",
    "from snowflake.snowpark import Window\n",
    "\n",
    "df = df.with_column('DATE', to_timestamp(col('DATE'), 'MM/DD/YYYY'))\n",
    "\n",
    "# Add a new column for the day of the week\n",
    "# The day of week is represented as an integer, with 0 = Sunday, 1 = Monday, ..., 6 = Saturday\n",
    "df = df.with_column('DAY_OF_WEEK', dayofweek(col('DATE')))\n",
    "\n",
    "# Show the resulting dataframe\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0022d07b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7364\n",
      "--------------------------------------------------------------------------------------------------\n",
      "|\"DATE\"               |\"DAY_OF_WEEK\"  |\"MONTH\"  |\"DAYTYPE\"  |\"TOTAL_RIDERS\"  |\"PREV_DAY_RIDERS\"  |\n",
      "--------------------------------------------------------------------------------------------------\n",
      "|2001-01-01 00:00:00  |1              |1        |U          |295439          |NULL               |\n",
      "|2001-01-02 00:00:00  |2              |1        |W          |776862          |295439             |\n",
      "|2001-01-03 00:00:00  |3              |1        |W          |820048          |776862             |\n",
      "|2001-01-04 00:00:00  |4              |1        |W          |867675          |820048             |\n",
      "|2001-01-05 00:00:00  |5              |1        |W          |887519          |867675             |\n",
      "|2001-01-06 00:00:00  |6              |1        |A          |575407          |887519             |\n",
      "|2001-01-07 00:00:00  |0              |1        |U          |374435          |575407             |\n",
      "|2001-01-08 00:00:00  |1              |1        |W          |980660          |374435             |\n",
      "|2001-01-09 00:00:00  |2              |1        |W          |974858          |980660             |\n",
      "|2001-01-10 00:00:00  |3              |1        |W          |980656          |974858             |\n",
      "--------------------------------------------------------------------------------------------------\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Add a new column for the month\n",
    "df = df.with_column('MONTH', month(col('DATE')))\n",
    "\n",
    "# Group by DATE, DAY_OF_WEEK, and MONTH, then aggregate\n",
    "total_riders = df.group_by('DATE','DAY_OF_WEEK','MONTH').agg(\n",
    "    F.listagg('DAYTYPE', is_distinct=True).alias('DAYTYPE'),\n",
    "    F.sum('RIDES').alias('TOTAL_RIDERS')\n",
    ").order_by('DATE')\n",
    "\n",
    "#Define a window specification\n",
    "window_spec = Window.order_by('DATE')\n",
    "\n",
    "# Add a lagged column for total ridership of the previous day\n",
    "total_riders = total_riders.with_column('PREV_DAY_RIDERS', lag(col('TOTAL_RIDERS'), 1).over(window_spec))\n",
    "\n",
    "# Show the resulting dataframe\n",
    "print (total_riders.count())\n",
    "print (total_riders.show())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a03d95b",
   "metadata": {},
   "source": [
    "### Also, you can use ChatGPT to generate the code for you.\n",
    "\n",
    " <img src=\"fe_forecasting.png\" alt=\"Forecasting Visualization\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e52f46a",
   "metadata": {},
   "source": [
    "## Join in the Weather Data from the Snowflake Marketplace\n",
    " [Cybersyn Weather](https://app.snowflake.com/marketplace/listing/GZTSZAS2KIM/cybersyn-inc-weather-environmental-essentials?search=weather)\n",
    "\n",
    "SQL QUERY: \n",
    "```\n",
    "SELECT\n",
    "  ts.noaa_weather_station_id,\n",
    "  ts.DATE,\n",
    "  COALESCE(MAX(CASE WHEN ts.variable = 'minimum_temperature' THEN ts.Value ELSE NULL END), 0) AS minimum_temperature,\n",
    "  COALESCE(MAX(CASE WHEN ts.variable = 'precipitation' THEN ts.Value ELSE NULL END), 0) AS precipitation,\n",
    "  COALESCE(MAX(CASE WHEN ts.variable = 'maximum_temperature' THEN ts.Value ELSE NULL END), 0) AS maximum_temperature\n",
    "FROM\n",
    "  cybersyn.noaa_weather_metrics_timeseries AS ts\n",
    "JOIN\n",
    "  cybersyn.noaa_weather_station_index AS idx\n",
    "ON\n",
    "  (ts.noaa_weather_station_id = idx.noaa_weather_station_id)\n",
    "WHERE\n",
    "  idx.NOAA_WEATHER_STATION_ID = 'USW00014819'\n",
    "  AND (ts.VARIABLE = 'minimum_temperature' OR ts.VARIABLE = 'precipitation' OR ts.VARIABLE = 'maximum_temperature')\n",
    "GROUP BY\n",
    "  ts.noaa_weather_station_id,\n",
    "  ts.DATE\n",
    "LIMIT 1000;\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b56ea3fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------------------------------\n",
      "|\"NOAA_WEATHER_STATION_ID\"  |\"DATE\"      |\"MINIMUM_TEMPERATURE\"  |\"MAXIMUM_TEMPERATURE\"  |\"PRECIPITATION\"  |\n",
      "------------------------------------------------------------------------------------------------------------\n",
      "|USW00014819                |2019-07-16  |22.2                   |28.9                   |3.8              |\n",
      "|USW00014819                |2002-01-06  |-3.9                   |3.3                    |0.0              |\n",
      "|USW00014819                |2008-03-17  |-0.5                   |4.4                    |2.0              |\n",
      "|USW00014819                |2000-01-29  |-6.7                   |-2.2                   |0.0              |\n",
      "|USW00014819                |2004-06-12  |16.7                   |26.7                   |6.6              |\n",
      "|USW00014819                |2017-07-15  |16.1                   |28.3                   |0.0              |\n",
      "|USW00014819                |2001-10-22  |12.2                   |18.9                   |2.3              |\n",
      "|USW00014819                |2021-05-01  |6.1                    |28.3                   |0.0              |\n",
      "|USW00014819                |2016-11-29  |7.2                    |14.4                   |0.0              |\n",
      "|USW00014819                |2020-08-01  |18.3                   |26.1                   |5.1              |\n",
      "------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weather = session.read.table('CHICAGO_WEATHER')\n",
    "\n",
    "from snowflake.snowpark.types import DoubleType\n",
    "weather = weather.withColumn('MINIMUM_TEMPERATURE', weather['MINIMUM_TEMPERATURE'].cast(DoubleType()))\n",
    "weather = weather.withColumn('MAXIMUM_TEMPERATURE', weather['MAXIMUM_TEMPERATURE'].cast(DoubleType()))\n",
    "weather = weather.withColumn('PRECIPITATION', weather['PRECIPITATION'].cast(DoubleType()))\n",
    "\n",
    "weather.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "027f9449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"NOAA_WEATHER_STATION_ID\"  |\"DATEW\"     |\"MINIMUM_TEMPERATURE\"  |\"MAXIMUM_TEMPERATURE\"  |\"PRECIPITATION\"  |\"DATE\"               |\"DAY_OF_WEEK\"  |\"MONTH\"  |\"DAYTYPE\"  |\"TOTAL_RIDERS\"  |\"PREV_DAY_RIDERS\"  |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|USW00014819                |2006-03-06  |-1.6                   |2.2                    |0.3              |2006-03-06 00:00:00  |1              |3        |W          |791939          |358365             |\n",
      "|USW00014819                |2015-12-25  |2.2                    |7.8                    |0.0              |2015-12-25 00:00:00  |5              |12       |U          |229983          |528494             |\n",
      "|USW00014819                |2019-06-18  |14.4                   |26.1                   |0.0              |2019-06-18 00:00:00  |2              |6        |W          |779717          |734899             |\n",
      "|USW00014819                |2018-02-01  |-12.7                  |2.2                    |0.0              |2018-02-01 00:00:00  |4              |2        |W          |821729          |812563             |\n",
      "|USW00014819                |2006-05-17  |8.9                    |22.8                   |6.6              |2006-05-17 00:00:00  |3              |5        |W          |960703          |990239             |\n",
      "|USW00014819                |2001-03-11  |-1.1                   |5.0                    |1.8              |2001-03-11 00:00:00  |0              |3        |U          |408271          |629429             |\n",
      "|USW00014819                |2001-04-04  |3.3                    |11.7                   |0.0              |2001-04-04 00:00:00  |3              |4        |W          |1043650         |1052404            |\n",
      "|USW00014819                |2002-09-16  |11.1                   |26.1                   |0.0              |2002-09-16 00:00:00  |1              |9        |W          |1045402         |419540             |\n",
      "|USW00014819                |2004-11-30  |0.6                    |5.0                    |13.5             |2004-11-30 00:00:00  |2              |11       |W          |966120          |958522             |\n",
      "|USW00014819                |2017-05-02  |5.6                    |10.6                   |2.5              |2017-05-02 00:00:00  |2              |5        |W          |843168          |844234             |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform the join operation\n",
    "joined_df = weather.join(\n",
    "    total_riders,\n",
    "    weather[\"DATE\"] == total_riders[\"DATE\"],\n",
    "    \"inner\",  # This is the type of join: inner, outer, left, right,\n",
    "    lsuffix=\"w\"\n",
    ")\n",
    "# Show the result of the join\n",
    "joined_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c5237c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping any null values\n",
    "from snowflake.snowpark.functions import col, is_null\n",
    "\n",
    "# Create a filter condition for non-finite values across all columns\n",
    "non_finite_filter = None\n",
    "\n",
    "# Iterate over all columns and update the filter condition\n",
    "for column in joined_df.columns:\n",
    "    current_filter = is_null(col(column))\n",
    "    non_finite_filter = current_filter if non_finite_filter is None else (non_finite_filter | current_filter)\n",
    "\n",
    "# Apply the filter to the DataFrame to exclude rows with any non-finite values\n",
    "df_filtered = joined_df.filter(~non_finite_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96a10c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into training and test sets\n",
    "train = df_filtered.filter(col('DATE') < '2019-01-01')\n",
    "test = df_filtered.filter(col('DATE') >= '2019-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db5114b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6570\n",
      "790\n"
     ]
    }
   ],
   "source": [
    "print (train.count())\n",
    "print (test.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774e0c4f",
   "metadata": {},
   "source": [
    "## 4. Distributed Feature Engineering in a Pipeline\n",
    "\n",
    "Feature engineering + XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc637e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()\n",
    "session.sql(\"alter warehouse snowpark_opt_wh set max_concurrency_level = 1\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad265fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    " ## Distributed Preprocessing - 25X to 50X faster\n",
    "numeric_features = ['DAY_OF_WEEK','MONTH','PREV_DAY_RIDERS','MINIMUM_TEMPERATURE','MAXIMUM_TEMPERATURE','PRECIPITATION']\n",
    "numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\n",
    "\n",
    "categorical_cols = ['DAYTYPE']\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-99999))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_cols)\n",
    "        ])\n",
    "\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),('model', XGBRegressor())])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35dca763",
   "metadata": {},
   "source": [
    "## 5. Distributed Training\n",
    "\n",
    "These operations are done inside the Snowpark warehouse which provides improved performance and scalability with distributed execution for these scikit-learn preprocessing functions and XGBoost training (and many other types of models)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a1c973e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<snowflake.ml.modeling.model_selection.grid_search_cv.GridSearchCV at 0x16c8f9d90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ## Distributed HyperParameter Optimization\n",
    "hyper_param = dict(\n",
    "        model__max_depth=[2,4],\n",
    "        model__learning_rate=[0.1,0.3],\n",
    "    )\n",
    "\n",
    "xg_model = GridSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_grid=hyper_param,\n",
    "    #cv=5,\n",
    "    input_cols=numeric_features + categorical_cols,\n",
    "    label_cols=['TOTAL_RIDERS'],\n",
    "    output_cols=[\"TOTAL_RIDERS_FORECAST\"],\n",
    ")\n",
    "\n",
    "# Fit and Score\n",
    "xg_model.fit(train)\n",
    "##Takes 25 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1788805",
   "metadata": {},
   "source": [
    "## 6. Model Evaluation\n",
    "Look at the results of the mode. cv_results is a dictionary, where each key is a string describing one of the metrics or parameters, and the corresponding value is an array with one entry per combination of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d0e9845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70d680fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 2}\n",
      "Mean Test Score: 0.9276901128451863\n",
      "\n",
      "Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 4}\n",
      "Mean Test Score: 0.9428721557568004\n",
      "\n",
      "Parameters: {'model__learning_rate': 0.3, 'model__max_depth': 2}\n",
      "Mean Test Score: 0.9364254261734043\n",
      "\n",
      "Parameters: {'model__learning_rate': 0.3, 'model__max_depth': 4}\n",
      "Mean Test Score: 0.9421547569632706\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cv_results = xg_model.to_sklearn().cv_results_\n",
    "\n",
    "for i in range(len(cv_results['params'])):\n",
    "    print(f\"Parameters: {cv_results['params'][i]}\")\n",
    "    print(f\"Mean Test Score: {cv_results['mean_test_score'][i]}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c34113",
   "metadata": {},
   "source": [
    "Look at the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73c36d50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 183320.1351068038\n",
      "---------------------------------------------------------\n",
      "|\"DATEW\"     |\"TOTAL_RIDERS\"  |\"TOTAL_RIDERS_FORECAST\"  |\n",
      "---------------------------------------------------------\n",
      "|2019-07-16  |733857          |819819.375               |\n",
      "|2020-08-01  |233815          |490637.1875              |\n",
      "|2021-02-08  |240644          |651060.8125              |\n",
      "|2019-11-15  |785975          |790946.3125              |\n",
      "|2021-02-03  |312535          |651066.1875              |\n",
      "|2020-07-24  |314921          |704918.3125              |\n",
      "|2019-07-11  |758510          |775658.125               |\n",
      "|2019-03-23  |467570          |506125.09375             |\n",
      "|2020-01-05  |312920          |352203.6875              |\n",
      "|2019-01-09  |743021          |790567.625               |\n",
      "---------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from snowflake.ml.modeling.metrics import mean_absolute_error\n",
    "testpreds = xg_model.predict(test)\n",
    "print('MSE:', mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='\"TOTAL_RIDERS_FORECAST\"'))\n",
    "testpreds.select(\"DATEW\", \"TOTAL_RIDERS\", \"TOTAL_RIDERS_FORECAST\").show(10)         "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93fa47b",
   "metadata": {},
   "source": [
    "Materialize the results to a table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5f7177",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d9489eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "testpreds.write.save_as_table(table_name='CHICAGO_BUS_RIDES_FORECAST', mode='overwrite')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39e5be6",
   "metadata": {},
   "source": [
    "Using metrics from snowpark so calculation is done inside snowflake"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39a775d",
   "metadata": {},
   "source": [
    "## 7. Save to the Model Registry and use for Predictions (Python & SQL)\n",
    "\n",
    "Connect to the registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c97f06d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.registry import Registry\n",
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99012d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/contextlib.py:113: UserWarning: `relax_version` is not set and therefore defaulted to True. Dependency version constraints relaxed from ==x.y.z to >=x.y, <(x+1). To use specific dependency versions for compatibility, reproducibility, etc., set `options={'relax_version': False}` when logging the model.\n",
      "  return next(self.gen)\n",
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/snowflake/ml/model/_packager/model_packager.py:92: UserWarning: Inferring model signature from sample input or providing model signature for Snowpark ML Modeling model is not required. Model signature will automatically be inferred during fitting. \n",
      "  handler.save_model(\n"
     ]
    }
   ],
   "source": [
    "model_ref = reg.log_model(\n",
    "    model_name=\"Forecasting_Bus_Ridership\",\n",
    "    version_name=\"v34\",    \n",
    "    model=xg_model,\n",
    "    conda_dependencies=[\"scikit-learn\",\"xgboost\"],\n",
    "    sample_input_data=train,\n",
    "    comment=\"XGBoost model, run 34, May6\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ac62368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>created_on</th>\n",
       "      <th>name</th>\n",
       "      <th>database_name</th>\n",
       "      <th>schema_name</th>\n",
       "      <th>comment</th>\n",
       "      <th>owner</th>\n",
       "      <th>default_version_name</th>\n",
       "      <th>versions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-23 18:58:41.929000-08:00</td>\n",
       "      <td>DIABETES_XGBOOSTER</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V2</td>\n",
       "      <td>[\"V2\",\"V3\",\"V4\",\"V5\",\"V7\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-19 17:12:27.005000-08:00</td>\n",
       "      <td>E5_BASE_V2</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V1</td>\n",
       "      <td>[\"V1\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-02-07 13:00:56.292000-08:00</td>\n",
       "      <td>FINBERT</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V1</td>\n",
       "      <td>[\"V1\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-02-26 18:55:00.548000-08:00</td>\n",
       "      <td>FORECASTING_BUS_RIDERSHIP</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V7</td>\n",
       "      <td>[\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-02-19 17:19:12.122000-08:00</td>\n",
       "      <td>MINILMV2</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V1</td>\n",
       "      <td>[\"V1\",\"V2\",\"V4\",\"V5\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-02-07 13:14:44.823000-08:00</td>\n",
       "      <td>MPNET_BASE</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V1</td>\n",
       "      <td>[\"V1\",\"V2\",\"V3\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024-01-25 14:54:04.655000-08:00</td>\n",
       "      <td>TPCDS_XGBOOST_DEMO</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V5</td>\n",
       "      <td>[\"V5\",\"V6\",\"V7\",\"V8\"]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-01-23 18:49:09.294000-08:00</td>\n",
       "      <td>XGBOOSTER</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>None</td>\n",
       "      <td>RAJIV</td>\n",
       "      <td>V1</td>\n",
       "      <td>[\"V1\",\"V2\"]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        created_on                       name database_name  \\\n",
       "0 2024-01-23 18:58:41.929000-08:00         DIABETES_XGBOOSTER         RAJIV   \n",
       "1 2024-02-19 17:12:27.005000-08:00                 E5_BASE_V2         RAJIV   \n",
       "2 2024-02-07 13:00:56.292000-08:00                    FINBERT         RAJIV   \n",
       "3 2024-02-26 18:55:00.548000-08:00  FORECASTING_BUS_RIDERSHIP         RAJIV   \n",
       "4 2024-02-19 17:19:12.122000-08:00                   MINILMV2         RAJIV   \n",
       "5 2024-02-07 13:14:44.823000-08:00                 MPNET_BASE         RAJIV   \n",
       "6 2024-01-25 14:54:04.655000-08:00         TPCDS_XGBOOST_DEMO         RAJIV   \n",
       "7 2024-01-23 18:49:09.294000-08:00                  XGBOOSTER         RAJIV   \n",
       "\n",
       "  schema_name comment  owner default_version_name  \\\n",
       "0      PUBLIC    None  RAJIV                   V2   \n",
       "1      PUBLIC    None  RAJIV                   V1   \n",
       "2      PUBLIC    None  RAJIV                   V1   \n",
       "3      PUBLIC    None  RAJIV                   V7   \n",
       "4      PUBLIC    None  RAJIV                   V1   \n",
       "5      PUBLIC    None  RAJIV                   V1   \n",
       "6      PUBLIC    None  RAJIV                   V5   \n",
       "7      PUBLIC    None  RAJIV                   V1   \n",
       "\n",
       "                                            versions  \n",
       "0                         [\"V2\",\"V3\",\"V4\",\"V5\",\"V7\"]  \n",
       "1                                             [\"V1\"]  \n",
       "2                                             [\"V1\"]  \n",
       "3  [\"V10\",\"V11\",\"V12\",\"V13\",\"V14\",\"V15\",\"V16\",\"V1...  \n",
       "4                              [\"V1\",\"V2\",\"V4\",\"V5\"]  \n",
       "5                                   [\"V1\",\"V2\",\"V3\"]  \n",
       "6                              [\"V5\",\"V6\",\"V7\",\"V8\"]  \n",
       "7                                        [\"V1\",\"V2\"]  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.show_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46750d4e",
   "metadata": {},
   "source": [
    "Let's retrieve the model from the registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16f2b93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_model = reg.get_model(\"Forecasting_Bus_Ridership\").version(\"v34\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bc70af",
   "metadata": {},
   "source": [
    "Let's do predictions inside the warehouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf7f3388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "|\"DATEW\"     |\"TOTAL_RIDERS\"  |\"TOTAL_RIDERS_FORECAST\"  |\n",
      "---------------------------------------------------------\n",
      "|2019-01-01  |247279          |290942.375               |\n",
      "|2019-01-02  |585996          |668251.3125              |\n",
      "|2019-01-03  |660631          |767229.875               |\n",
      "|2019-01-04  |662011          |759055.3125              |\n",
      "|2019-01-05  |440848          |491881.78125             |\n",
      "|2019-01-06  |316844          |351156.84375             |\n",
      "|2019-01-07  |717818          |762515.625               |\n",
      "|2019-01-08  |779946          |879376.0625              |\n",
      "|2019-01-09  |743021          |790567.625               |\n",
      "|2019-01-10  |743075          |764690.8125              |\n",
      "---------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "remote_prediction = reg_model.run(test, function_name='predict')\n",
    "remote_prediction.sort(\"DATEW\").select(\"DATEW\",\"TOTAL_RIDERS\",\"TOTAL_RIDERS_FORECAST\").show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c615b1b",
   "metadata": {},
   "source": [
    "If you look in the activity view, you can find the SQL which will run a bit faster.  This SQL command is showing the result in a snowflake dataframe. You could use `collect` to pull the info out into your local session.\n",
    "\n",
    "Modify the SQL with by adding in your specific model with this line: `WITH MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.DIABETES_XGBOOSTER VERSION V7` and updating the location of your target predictions which is located here: `SNOWPARK_ML_MODEL_INFERENCE_INPUT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19ab86b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqlquery = \"\"\"SELECT \"DATEW\", \"TOTAL_RIDERS\",  CAST (\"TMP_RESULT\"['TOTAL_RIDERS_FORECAST'] AS DOUBLE) AS \"TOTAL_RIDERS_FORECAST\" FROM (WITH SNOWPARK_ML_MODEL_INFERENCE_INPUT AS (SELECT  *  FROM ( SELECT  *  FROM (( SELECT \"NOAA_WEATHER_STATION_ID\" AS \"NOAA_WEATHER_STATION_ID\", \"DATE\" AS \"DATEW\", \"MINIMUM_TEMPERATURE\" AS \"MINIMUM_TEMPERATURE\", \"MAXIMUM_TEMPERATURE\" AS \"MAXIMUM_TEMPERATURE\", \"PRECIPITATION\" AS \"PRECIPITATION\" FROM ( SELECT \"NOAA_WEATHER_STATION_ID\", \"DATE\",  CAST (\"MINIMUM_TEMPERATURE\" AS DOUBLE) AS \"MINIMUM_TEMPERATURE\",  CAST (\"MAXIMUM_TEMPERATURE\" AS DOUBLE) AS \"MAXIMUM_TEMPERATURE\",  CAST (\"PRECIPITATION\" AS DOUBLE) AS \"PRECIPITATION\" FROM CHICAGO_WEATHER)) AS SNOWPARK_LEFT INNER JOIN ( SELECT \"DATE\" AS \"DATE\", \"DAY_OF_WEEK\" AS \"DAY_OF_WEEK\", \"MONTH\" AS \"MONTH\", \"DAYTYPE\" AS \"DAYTYPE\", \"TOTAL_RIDERS\" AS \"TOTAL_RIDERS\", \"PREV_DAY_RIDERS\" AS \"PREV_DAY_RIDERS\" FROM ( SELECT \"DATE\", \"DAY_OF_WEEK\", \"MONTH\", \"DAYTYPE\", \"TOTAL_RIDERS\", LAG(\"TOTAL_RIDERS\", 1, NULL) OVER (  ORDER BY \"DATE\" ASC NULLS FIRST ) AS \"PREV_DAY_RIDERS\" FROM ( SELECT \"DATE\", \"DAY_OF_WEEK\", \"MONTH\",  LISTAGG ( DISTINCT \"DAYTYPE\", '') AS \"DAYTYPE\", sum(\"RIDES\") AS \"TOTAL_RIDERS\" FROM ( SELECT \"ROUTE\", \"DAYTYPE\", \"RIDES\", \"DATE\", dayofweek(\"DATE\") AS \"DAY_OF_WEEK\", month(\"DATE\") AS \"MONTH\" FROM ( SELECT \"ROUTE\", \"DAYTYPE\", \"RIDES\", to_timestamp(\"DATE\", 'MM/DD/YYYY') AS \"DATE\" FROM CHICAGO_BUS_RIDES)) GROUP BY \"DATE\", \"DAY_OF_WEEK\", \"MONTH\") ORDER BY \"DATE\" ASC NULLS FIRST)) AS SNOWPARK_RIGHT ON (\"DATEW\" = \"DATE\"))) WHERE (NOT ((((((((((\"NOAA_WEATHER_STATION_ID\" IS NULL OR \"DATEW\" IS NULL) OR \"MINIMUM_TEMPERATURE\" IS NULL) OR \"MAXIMUM_TEMPERATURE\" IS NULL) OR \"PRECIPITATION\" IS NULL) OR \"DATE\" IS NULL) OR \"DAY_OF_WEEK\" IS NULL) OR \"MONTH\" IS NULL) OR \"DAYTYPE\" IS NULL) OR \"TOTAL_RIDERS\" IS NULL) OR \"PREV_DAY_RIDERS\" IS NULL) AND (\"DATE\" >= '2019-01-01'))),MODEL_VERSION_ALIAS AS MODEL RAJIV.PUBLIC.FORECASTING_BUS_RIDERSHIP VERSION V27\n",
    "                SELECT *,\n",
    "                    MODEL_VERSION_ALIAS!PREDICT(DAY_OF_WEEK, MONTH, PREV_DAY_RIDERS, MINIMUM_TEMPERATURE, MAXIMUM_TEMPERATURE, PRECIPITATION, DAYTYPE) AS TMP_RESULT\n",
    "                FROM SNOWPARK_ML_MODEL_INFERENCE_INPUT) ORDER BY \"DATEW\" ASC NULLS FIRST LIMIT 10\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d60a60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------\n",
      "|\"DATEW\"     |\"TOTAL_RIDERS\"  |\"TOTAL_RIDERS_FORECAST\"  |\n",
      "---------------------------------------------------------\n",
      "|2019-01-01  |247279          |290942.375               |\n",
      "|2019-01-02  |585996          |668251.3125              |\n",
      "|2019-01-03  |660631          |767229.875               |\n",
      "|2019-01-04  |662011          |759055.3125              |\n",
      "|2019-01-05  |440848          |491881.78125             |\n",
      "|2019-01-06  |316844          |351156.84375             |\n",
      "|2019-01-07  |717818          |762515.625               |\n",
      "|2019-01-08  |779946          |879376.0625              |\n",
      "|2019-01-09  |743021          |790567.625               |\n",
      "|2019-01-10  |743075          |764690.8125              |\n",
      "---------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "results = session.sql(sqlquery).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0412e19f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"NOAA_WEATHER_STATION_ID\"  |\"DATEW\"     |\"MINIMUM_TEMPERATURE\"  |\"MAXIMUM_TEMPERATURE\"  |\"PRECIPITATION\"  |\"DATE\"               |\"DAY_OF_WEEK\"  |\"MONTH\"  |\"DAYTYPE\"  |\"TOTAL_RIDERS\"  |\"PREV_DAY_RIDERS\"  |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|USW00014819                |2020-07-03  |20.6                   |31.7                   |0.0              |2020-07-03 00:00:00  |5              |7        |U          |270792          |316747             |\n",
      "|USW00014819                |2021-02-09  |-16.0                  |-7.1                   |0.0              |2021-02-09 00:00:00  |2              |2        |W          |258202          |240644             |\n",
      "|USW00014819                |2020-04-04  |2.2                    |13.3                   |2.0              |2020-04-04 00:00:00  |6              |4        |A          |153561          |239180             |\n",
      "|USW00014819                |2020-12-18  |-4.3                   |2.8                    |0.0              |2020-12-18 00:00:00  |5              |12       |W          |281184          |271043             |\n",
      "|USW00014819                |2019-03-21  |2.8                    |7.8                    |0.0              |2019-03-21 00:00:00  |4              |3        |W          |791505          |779764             |\n",
      "|USW00014819                |2019-09-19  |17.8                   |29.4                   |0.0              |2019-09-19 00:00:00  |4              |9        |W          |842073          |852242             |\n",
      "|USW00014819                |2019-02-11  |-1.6                   |0.0                    |4.8              |2019-02-11 00:00:00  |1              |2        |W          |775564          |295038             |\n",
      "|USW00014819                |2019-10-11  |3.9                    |20.0                   |11.7             |2019-10-11 00:00:00  |5              |10       |W          |773303          |850944             |\n",
      "|USW00014819                |2020-10-05  |2.2                    |17.2                   |0.0              |2020-10-05 00:00:00  |1              |10       |W          |323561          |178613             |\n",
      "|USW00014819                |2020-01-23  |-1.0                   |2.2                    |2.3              |2020-01-23 00:00:00  |4              |1        |W          |761208          |789261             |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f392487",
   "metadata": {},
   "source": [
    "Let's calculate and save the metrics to the registry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a6bea723",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.modeling.metrics import mean_absolute_error\n",
    "testpreds = reg_model.run(test, function_name='predict')\n",
    "mae = mean_absolute_error(df=testpreds, y_true_col_names='TOTAL_RIDERS', y_pred_col_names='\"TOTAL_RIDERS_FORECAST\"')\n",
    "reg_model.set_metric(\"MAE\", value=mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c587021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MAE': 183320.1351068038}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model.show_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "00315bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Warehouse SNOWPARK_OPT_WH successfully created.')]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = 'SMALL'\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b233260",
   "metadata": {},
   "outputs": [],
   "source": [
    "#session.close()"
   ]
  }
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