BigData_Demo/xgboost_tpcds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big Data Demo\n",
    "\n",
    "Let's see how snowflake does at scale using either a 10TB or 100TB dataset.\n",
    "This demo walks through data prep and modeling for a large dataset.\n",
    "This notebook comes from [TPCDS Customer Lifetime demo](https://github.com/Snowflake-Labs/snowpark-python-demos/tree/main/tpcds-customer-lifetime-value)\n",
    "\n",
    "If you are running this in your local environment, you will need to make sure you use versions of sklearn and xgboost that are supported in snowflake. Otherwise, you will get an error when you try to push the model to snowflake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rajishah/Code/snowflake-notebooks/BigData_Demo\n"
     ]
    }
   ],
   "source": [
    "# Base dir for this code\n",
    "import os\n",
    "base_dir = os.getcwd()\n",
    "print(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "from snowflake.snowpark import functions as F\n",
    "from snowflake.snowpark.version import VERSION\n",
    "from snowflake.snowpark.session import Session\n",
    "\n",
    "from snowflake.ml._internal import env as snowml_env\n",
    "from snowflake.ml.modeling.xgboost import XGBRegressor\n",
    "from snowflake.ml.modeling.preprocessing import KBinsDiscretizer, OneHotEncoder\n",
    "from snowflake.ml.modeling.impute import SimpleImputer\n",
    "#from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.0 Snowflake Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": "code",
   "execution_count": 5,
   "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           : 8.20.0\n",
      "Snowpark for Python version : 1.15.0a1\n",
      "Snowflake Python 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 Python ML version : {}'.format(snowml_env.VERSION))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the TPC-DS dataset\n",
    "I found it within the SNOWFLAKE_SAMPLE_DATA shared database. For demo purposes, we are going to create two warehouses:  \n",
    "\n",
    "- FE_AND_INFERENCE_WH for data engineering\n",
    "- snowpark_opt_wh for model training \n",
    "\n",
    "For demo purposes, we create all of this in a seperate database and schema. This way you can tear it down all easily after testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.sql('CREATE DATABASE IF NOT EXISTS tpcds_xgboost').collect()\n",
    "session.sql('CREATE SCHEMA IF NOT EXISTS tpcds_xgboost.demo').collect()\n",
    "session.sql(\"create or replace warehouse FE_AND_INFERENCE_WH with warehouse_size='3X-LARGE'\").collect()\n",
    "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()\n",
    "session.sql(\"CREATE OR REPLACE STAGE TPCDS_XGBOOST.DEMO.ML_MODELS\").collect()\n",
    "session.use_warehouse('FE_AND_INFERENCE_WH')\n",
    "session.use_database('tpcds_xgboost')\n",
    "session.use_schema('demo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select either 100 or 10 for the [TPC-DS Dataset](https://docs.snowflake.com/en/user-guide/sample-data-tpcds.html) size to use below. If you choose 100, I recommend >= 3XL warehouse. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "TPCDS_SIZE_PARAM = 10\n",
    "SNOWFLAKE_SAMPLE_DB = 'SFSALESSHARED_SFC_SAMPLES_PROD3_SAMPLE_DATA' # Name of Snowflake Sample Database might be different...\n",
    "\n",
    "if TPCDS_SIZE_PARAM == 100: \n",
    "    TPCDS_SCHEMA = 'TPCDS_SF100TCL'\n",
    "elif TPCDS_SIZE_PARAM == 10:\n",
    "    TPCDS_SCHEMA = 'TPCDS_SF10TCL'\n",
    "else:\n",
    "    raise ValueError(\"Invalid TPCDS_SIZE_PARAM selection\")\n",
    "    \n",
    "store_sales = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.store_sales')\n",
    "catalog_sales = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.catalog_sales') \n",
    "web_sales = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.web_sales') \n",
    "date = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.date_dim')\n",
    "dim_stores = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.store')\n",
    "customer = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.customer')\n",
    "address = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.customer_address')\n",
    "demo = session.table(f'{SNOWFLAKE_SAMPLE_DB}.{TPCDS_SCHEMA}.customer_demographics')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 Data Engineering\n",
    "We will aggregate sales by customer across all channels(web, store, catalogue) and join that to customer demographic data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_sales_agged = store_sales.group_by('ss_customer_sk').agg(F.sum('ss_sales_price').as_('total_sales'))\n",
    "web_sales_agged = web_sales.group_by('ws_bill_customer_sk').agg(F.sum('ws_sales_price').as_('total_sales'))\n",
    "catalog_sales_agged = catalog_sales.group_by('cs_bill_customer_sk').agg(F.sum('cs_sales_price').as_('total_sales'))\n",
    "store_sales_agged = store_sales_agged.rename('ss_customer_sk', 'customer_sk')\n",
    "web_sales_agged = web_sales_agged.rename('ws_bill_customer_sk', 'customer_sk')\n",
    "catalog_sales_agged = catalog_sales_agged.rename('cs_bill_customer_sk', 'customer_sk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_sales = store_sales_agged.union_all(web_sales_agged)\n",
    "total_sales = total_sales.union_all(catalog_sales_agged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_sales = total_sales.group_by('customer_sk').agg(F.sum('total_sales').as_('total_sales'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = customer.select('c_customer_sk','c_current_hdemo_sk', 'c_current_addr_sk', 'c_customer_id', 'c_birth_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = customer.join(address.select('ca_address_sk', 'ca_zip'), customer['c_current_addr_sk'] == address['ca_address_sk'] )\n",
    "customer = customer.join(demo.select('cd_demo_sk', 'cd_gender', 'cd_marital_status', 'cd_credit_rating', 'cd_education_status', 'cd_dep_count'),\n",
    "                                customer['c_current_hdemo_sk'] == demo['cd_demo_sk'] )\n",
    "customer = customer.rename('c_customer_sk', 'customer_sk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CUSTOMER_SK\"  |\"C_CURRENT_HDEMO_SK\"  |\"C_CURRENT_ADDR_SK\"  |\"C_CUSTOMER_ID\"   |\"C_BIRTH_YEAR\"  |\"CA_ADDRESS_SK\"  |\"CA_ZIP\"  |\"CD_DEMO_SK\"  |\"CD_GENDER\"  |\"CD_MARITAL_STATUS\"  |\"CD_CREDIT_RATING\"  |\"CD_EDUCATION_STATUS\"  |\"CD_DEP_COUNT\"  |\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|13823036       |384                   |5281091              |AAAAAAAAMDMOCNAA  |1954            |5281091          |89303     |384           |F            |S                    |Good                |2 yr Degree            |0               |\n",
      "|13823037       |5298                  |29603881             |AAAAAAAANDMOCNAA  |1962            |29603881         |71749     |5298          |F            |W                    |Unknown             |4 yr Degree            |0               |\n",
      "|13823038       |3958                  |15978447             |AAAAAAAAODMOCNAA  |1987            |15978447         |26192     |3958          |F            |W                    |High Risk           |2 yr Degree            |0               |\n",
      "|13823039       |3605                  |4928437              |AAAAAAAAPDMOCNAA  |1965            |4928437          |18054     |3605          |M            |D                    |High Risk           |2 yr Degree            |0               |\n",
      "|13823040       |7051                  |4356655              |AAAAAAAAAEMOCNAA  |1946            |4356655          |61904     |7051          |M            |M                    |Low Risk            |Advanced Degree        |1               |\n",
      "|13823041       |4584                  |4305847              |AAAAAAAABEMOCNAA  |1966            |4305847          |54854     |4584          |F            |S                    |Unknown             |2 yr Degree            |0               |\n",
      "|13823042       |3161                  |18848050             |AAAAAAAACEMOCNAA  |1952            |18848050         |81933     |3161          |M            |M                    |High Risk           |Secondary              |0               |\n",
      "|13823043       |6277                  |22841905             |AAAAAAAADEMOCNAA  |1973            |22841905         |21952     |6277          |M            |W                    |Good                |4 yr Degree            |1               |\n",
      "|13823044       |1927                  |29223380             |AAAAAAAAEEMOCNAA  |1964            |29223380         |56668     |1927          |M            |W                    |Low Risk            |2 yr Degree            |0               |\n",
      "|13823045       |2825                  |2269825              |AAAAAAAAFEMOCNAA  |1989            |2269825          |78784     |2825          |M            |D                    |High Risk           |College                |0               |\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "customer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = total_sales.join(customer, on='customer_sk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62726989"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Size of the final DF is around 95 Million.\n",
    "# with 10 - raj gets 62,726,989\n",
    "final_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.use_database('tpcds_xgboost')\n",
    "session.use_schema('demo')\n",
    "final_df.write.mode('overwrite').save_as_table('feature_store')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the heavy data engineering finished, we can move to smaller warehouse, but one optimized for ML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.use_warehouse('snowpark_opt_wh')\n",
    "session.use_database('tpcds_xgboost')\n",
    "session.use_schema('demo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "snowdf = session.table(\"feature_store\")\n",
    "snowdf = snowdf.drop(['CA_ZIP','CUSTOMER_SK', 'C_CURRENT_HDEMO_SK', 'C_CURRENT_ADDR_SK', 'C_CUSTOMER_ID', 'CA_ADDRESS_SK', 'CD_DEMO_SK'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"TOTAL_SALES\"  |\"C_BIRTH_YEAR\"  |\"CD_GENDER\"  |\"CD_MARITAL_STATUS\"  |\"CD_CREDIT_RATING\"  |\"CD_EDUCATION_STATUS\"  |\"CD_DEP_COUNT\"  |\n",
      "------------------------------------------------------------------------------------------------------------------------------------\n",
      "|30368.88       |1972            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|39077.29       |1976            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|33980.60       |1976            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|40226.64       |1949            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|25446.41       |1952            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|34458.32       |1982            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|28561.76       |1942            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|32611.44       |1978            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|32713.36       |1939            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "|34231.25       |1951            |M            |U                    |Good                |4 yr Degree            |0               |\n",
      "------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "snowdf.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = ['CD_GENDER', 'CD_MARITAL_STATUS', 'CD_CREDIT_RATING', 'CD_EDUCATION_STATUS']\n",
    "num_cols = ['C_BIRTH_YEAR', 'CD_DEP_COUNT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Missing Value Imputation\n",
    "\n",
    "We can use the SimpleImputer in snowflake.ml.preprocessing to replace missing values with the most frequent.\n",
    "\n",
    "```python\n",
    "# SimpleImputer in snowflake.ml.preprocessing\n",
    "from snowflake.ml.modeling.impute import SimpleImputer\n",
    "my_imputer = sfml.preprocessing.SimpleImputer(input_cols=['your_column'],\n",
    "                                output_cols=['your_column'],\n",
    "                                strategy='constant',\n",
    "                                fill_value='OTHER')\n",
    "my_imputer.fit(my_sdf)\n",
    "my_sdf = my_imputer.transform(my_sdf)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input value type doesn't match the target column data type, this replacement was skipped. Column Name: \"C_BIRTH_YEAR\", Type: LongType(), Input Value: 1958.0, Type: <class 'float'>\n",
      "Input value type doesn't match the target column data type, this replacement was skipped. Column Name: \"CD_DEP_COUNT\", Type: LongType(), Input Value: 0.0, Type: <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "# Imputation of Numeric Cols\n",
    "my_imputer = SimpleImputer(input_cols= num_cols,\n",
    "                           output_cols= num_cols,\n",
    "                           strategy='median')\n",
    "sdf_prepared = my_imputer.fit(snowdf).transform(snowdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 One-Hot Encoding of Categorical Cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OHE of Categorical Cols\n",
    "my_ohe_encoder = OneHotEncoder(input_cols=cat_cols, output_cols=cat_cols, drop_input_cols=True)\n",
    "sdf_prepared = my_ohe_encoder.fit(sdf_prepared).transform(sdf_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CD_GENDER_F\"  |\"CD_GENDER_M\"  |\"CD_MARITAL_STATUS_D\"  |\"CD_MARITAL_STATUS_M\"  |\"CD_MARITAL_STATUS_S\"  |\"CD_MARITAL_STATUS_U\"  |\"CD_MARITAL_STATUS_W\"  |\"CD_CREDIT_RATING_Good\"  |\"CD_CREDIT_RATING_High Risk\"  |\"CD_CREDIT_RATING_Low Risk\"  |\"CD_CREDIT_RATING_Unknown\"  |\"CD_EDUCATION_STATUS_2 yr Degree\"  |\"CD_EDUCATION_STATUS_4 yr Degree\"  |\"CD_EDUCATION_STATUS_Advanced Degree\"  |\"CD_EDUCATION_STATUS_College\"  |\"CD_EDUCATION_STATUS_Primary\"  |\"CD_EDUCATION_STATUS_Secondary\"  |\"CD_EDUCATION_STATUS_Unknown\"  |\"C_BIRTH_YEAR\"  |\"CD_DEP_COUNT\"  |\"TOTAL_SALES\"  |\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1976            |0               |49801.41       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1943            |0               |32019.81       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1956            |0               |33910.22       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1977            |0               |29835.83       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1929            |0               |33102.04       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1976            |0               |33834.35       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1956            |0               |27398.56       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1937            |0               |29052.94       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1967            |0               |42830.48       |\n",
      "|1.0            |0.0            |0.0                    |0.0                    |0.0                    |1.0                    |0.0                    |1.0                      |0.0                           |0.0                          |0.0                         |0.0                                |0.0                                |0.0                                    |0.0                            |1.0                            |0.0                              |0.0                            |1933            |0               |28885.10       |\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sdf_prepared.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Clean column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning column names to make it easier for future referencing\n",
    "import re\n",
    "\n",
    "cols = sdf_prepared.columns\n",
    "for old_col in cols:\n",
    "    new_col = re.sub(r'[^a-zA-Z0-9_]', '', old_col)\n",
    "    new_col = new_col.upper()\n",
    "    #print (old_col)\n",
    "    #print (new_col)\n",
    "    sdf_prepared = sdf_prepared.rename((old_col), new_col)\n",
    "\n",
    "## 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 sdf_prepared.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 = sdf_prepared.filter(~non_finite_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61590731"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 ML Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 30,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare Data for modeling\n",
    "feature_cols = df_filtered.columns\n",
    "feature_cols.remove('TOTAL_SALES')\n",
    "target_col = 'TOTAL_SALES'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the train and test sets as time stamped tables in Snowflake\n",
    "snowdf_train, snowdf_test = df_filtered.random_split([0.8, 0.2], seed=82) \n",
    "snowdf_train.write.mode(\"overwrite\").save_as_table(\"tpcds_xgboost.demo.tpc_TRAIN\")\n",
    "snowdf_test.write.mode(\"overwrite\").save_as_table(\"tpcds_xgboost.demo.tpc_TEST\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Initialize Model and Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 40,
     "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "snowdf_train = session.table(\"tpcds_xgboost.demo.tpc_TRAIN\")\n",
    "snowdf_test = session.table(\"tpcds_xgboost.demo.tpc_TEST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['CD_GENDER_F',\n",
    " 'CD_GENDER_M',\n",
    " 'CD_MARITAL_STATUS_D',\n",
    " 'CD_MARITAL_STATUS_M',\n",
    " 'CD_MARITAL_STATUS_S',\n",
    " 'CD_MARITAL_STATUS_U',\n",
    " 'CD_MARITAL_STATUS_W',\n",
    " 'CD_CREDIT_RATING_GOOD',\n",
    " 'CD_CREDIT_RATING_HIGHRISK',\n",
    " 'CD_CREDIT_RATING_LOWRISK',\n",
    " 'CD_CREDIT_RATING_UNKNOWN',\n",
    " 'CD_EDUCATION_STATUS_2YRDEGREE',\n",
    " 'CD_EDUCATION_STATUS_4YRDEGREE',\n",
    " 'CD_EDUCATION_STATUS_ADVANCEDDEGREE',\n",
    " 'CD_EDUCATION_STATUS_COLLEGE',\n",
    " 'CD_EDUCATION_STATUS_PRIMARY',\n",
    " 'CD_EDUCATION_STATUS_SECONDARY',\n",
    " 'CD_EDUCATION_STATUS_UNKNOWN',\n",
    " 'C_BIRTH_YEAR',\n",
    " 'CD_DEP_COUNT']\n",
    "\n",
    "target_col = 'TOTAL_SALES'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49268805"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snowdf_train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CD_GENDER_F\"  |\"CD_GENDER_M\"  |\"CD_MARITAL_STATUS_D\"  |\"CD_MARITAL_STATUS_M\"  |\"CD_MARITAL_STATUS_S\"  |\"CD_MARITAL_STATUS_U\"  |\"CD_MARITAL_STATUS_W\"  |\"CD_CREDIT_RATING_GOOD\"  |\"CD_CREDIT_RATING_HIGHRISK\"  |\"CD_CREDIT_RATING_LOWRISK\"  |\"CD_CREDIT_RATING_UNKNOWN\"  |\"CD_EDUCATION_STATUS_2YRDEGREE\"  |\"CD_EDUCATION_STATUS_4YRDEGREE\"  |\"CD_EDUCATION_STATUS_ADVANCEDDEGREE\"  |\"CD_EDUCATION_STATUS_COLLEGE\"  |\"CD_EDUCATION_STATUS_PRIMARY\"  |\"CD_EDUCATION_STATUS_SECONDARY\"  |\"CD_EDUCATION_STATUS_UNKNOWN\"  |\"C_BIRTH_YEAR\"  |\"CD_DEP_COUNT\"  |\"TOTAL_SALES\"  |\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.0            |1.0            |0.0                    |1.0                    |0.0                    |0.0                    |0.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |0.0                            |0.0                              |1.0                            |1973            |0               |30267.93       |\n",
      "|0.0            |1.0            |0.0                    |1.0                    |0.0                    |0.0                    |0.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |0.0                            |0.0                              |1.0                            |1944            |0               |30990.81       |\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "snowdf_train.show(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional Code here: If you need to pull the data out to train locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Local XGBoost Model -- Painful to move it outside of Snowflake, but possible\n",
    "#snowdf_train1 = snowdf_train.limit(5000000)\n",
    "#snowdf_train_pd  = pd.DataFrame(snowdf_train1.collect())\n",
    "#y = snowdf_train_pd['TOTAL_SALES']\n",
    "# Selecting the rest of the columns for X\n",
    "#X = snowdf_train_pd.drop('TOTAL_SALES', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The version of package 'snowflake-snowpark-python' in the local environment is 1.15.0a1, which does not fit the criteria for the requirement 'snowflake-snowpark-python'. Your UDF might not work when the package version is different between the server and your local environment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<snowflake.ml.modeling.xgboost.xgb_regressor.XGBRegressor at 0x1407e7220>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the XGBRegressor and fit the model inside snowflake\n",
    "xgbmodel = XGBRegressor(random_state=123, input_cols=feature_cols, label_cols=target_col, output_cols='PREDICTION')\n",
    "xgbmodel.fit(snowdf_train)\n",
    "#4 minutes for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that is 4 minutes on a warehouse that as of May 2024 cost 6 credits per hour. So a rough estimate would be 4/60 * 6 = 0.4 credits. And at a cost of $3 per credit, that would be $1.20 to train a XGBoost model on 50 million rows in 4 minutes!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Predict on test set\n",
    "You can also get the underlying SQL if you want to run that in the Activity Viewer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/snowflake/ml/_internal/utils/snowpark_dataframe_utils.py:88: UserWarning: Warning: The Decimal(31, 2) data type is being automatically converted to DoubleType in the Snowpark DataFrame. This automatic conversion may lead to potential precision loss and rounding errors. If you wish to prevent this conversion, you should manually perform the necessary data type conversion.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CD_GENDER_F\"  |\"CD_GENDER_M\"  |\"CD_MARITAL_STATUS_D\"  |\"CD_MARITAL_STATUS_M\"  |\"CD_MARITAL_STATUS_S\"  |\"CD_MARITAL_STATUS_U\"  |\"CD_MARITAL_STATUS_W\"  |\"CD_CREDIT_RATING_GOOD\"  |\"CD_CREDIT_RATING_HIGHRISK\"  |\"CD_CREDIT_RATING_LOWRISK\"  |\"CD_CREDIT_RATING_UNKNOWN\"  |\"CD_EDUCATION_STATUS_2YRDEGREE\"  |\"CD_EDUCATION_STATUS_4YRDEGREE\"  |\"CD_EDUCATION_STATUS_ADVANCEDDEGREE\"  |\"CD_EDUCATION_STATUS_COLLEGE\"  |\"CD_EDUCATION_STATUS_PRIMARY\"  |\"CD_EDUCATION_STATUS_SECONDARY\"  |\"CD_EDUCATION_STATUS_UNKNOWN\"  |\"C_BIRTH_YEAR\"  |\"CD_DEP_COUNT\"  |\"TOTAL_SALES\"  |\"PREDICTION\"     |\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1945            |0               |29611.61       |32305.572265625  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1959            |0               |27456.61       |32341.029296875  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1929            |0               |31906.68       |32346.060546875  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1972            |0               |35339.09       |32342.97265625   |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1987            |0               |30939.48       |32332.4609375    |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1931            |0               |28172.84       |32348.5546875    |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1959            |0               |34571.87       |32341.029296875  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1989            |0               |38508.09       |32337.607421875  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1943            |0               |36114.17       |32332.677734375  |\n",
      "|0.0            |1.0            |0.0                    |0.0                    |0.0                    |0.0                    |1.0                    |1.0                      |0.0                          |0.0                         |0.0                         |0.0                              |0.0                              |0.0                                   |0.0                            |1.0                            |0.0                              |0.0                            |1959            |0               |32617.54       |32341.029296875  |\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Score the data using the fitted xgbmodel -- #train predictations are fun, because its larger, but takes a few seconds\n",
    "temp = xgbmodel.predict(snowdf_train)\n",
    "temp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Save to the model registry\n",
    "\n",
    "If you get an error, you might be using a version of XGboost that is not supported in snowflake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "from snowflake.ml.registry import Registry\n",
    "\n",
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")\n",
    "\n",
    "model_ref = reg.log_model(\n",
    "    model_name=\"TPCDS_XGBOOST_DEMO\",\n",
    "    version_name=\"v9\",    \n",
    "    model=xgbmodel,\n",
    "    sample_input_data=snowdf_train,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#session.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Grid search for hyperparameter tuning \n",
    "(takes much longer - 27m on 2X-Large Optimized Warehouse - you can save time by making the grid search smaller)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse snowpark_opt_wh with warehouse_size = '2X-LARGE' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()\n",
    "session.sql(\"alter warehouse snowpark_opt_wh set max_concurrency_level = 1\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Snowpark ML's Distributed GridSearchCV() function to find optimal model parameters\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=XGBRegressor(),\n",
    "    param_grid={\n",
    "        \"n_estimators\":[50, 100, 150, 200, 250],\n",
    "        \"learning_rate\":[0.1, 0.2],\n",
    "    },\n",
    "    scoring=\"neg_mean_absolute_percentage_error\",\n",
    "    input_cols=feature_cols,\n",
    "    label_cols=target_col,\n",
    "    output_cols='PREDICTION'\n",
    ")\n",
    "# Train\n",
    "grid_search.fit(snowdf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([139.79411783, 315.42633963, 476.36620684, 627.08776531,\n",
       "        784.09252443, 169.23699589, 322.95549426, 480.5162087 ,\n",
       "        614.07631731, 770.6251276 ]),\n",
       " 'std_fit_time': array([ 4.828583  , 12.54658166, 26.29290556, 32.02743109, 17.70279617,\n",
       "         3.64934373, 10.98043695, 18.60329192, 22.06916218, 14.27847063]),\n",
       " 'mean_score_time': array([ 3.94958577,  7.72560515, 11.50131021, 15.39802165, 19.28434114,\n",
       "         4.57844481,  8.41202927, 12.06959987, 15.93090591, 19.69101286]),\n",
       " 'std_score_time': array([0.07672712, 0.16355887, 0.24963713, 0.19942028, 0.33762245,\n",
       "        0.09705693, 0.21695816, 0.16393069, 0.41849308, 0.25554989]),\n",
       " 'param_learning_rate': masked_array(data=[0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[50, 100, 150, 200, 250, 50, 100, 150, 200, 250],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'learning_rate': 0.1, 'n_estimators': 50},\n",
       "  {'learning_rate': 0.1, 'n_estimators': 100},\n",
       "  {'learning_rate': 0.1, 'n_estimators': 150},\n",
       "  {'learning_rate': 0.1, 'n_estimators': 200},\n",
       "  {'learning_rate': 0.1, 'n_estimators': 250},\n",
       "  {'learning_rate': 0.2, 'n_estimators': 50},\n",
       "  {'learning_rate': 0.2, 'n_estimators': 100},\n",
       "  {'learning_rate': 0.2, 'n_estimators': 150},\n",
       "  {'learning_rate': 0.2, 'n_estimators': 200},\n",
       "  {'learning_rate': 0.2, 'n_estimators': 250}],\n",
       " 'split0_test_score': array([-0.1027807 , -0.10486604, -0.10487987, -0.10488393, -0.10488471,\n",
       "        -0.10487579, -0.10488675, -0.10489091, -0.10489198, -0.10489174]),\n",
       " 'split1_test_score': array([-0.10278045, -0.1048647 , -0.10487601, -0.10487577, -0.10487553,\n",
       "        -0.10487021, -0.10487656, -0.10487592, -0.10487663, -0.10487895]),\n",
       " 'split2_test_score': array([-0.10275241, -0.1048409 , -0.10485405, -0.1048545 , -0.10485508,\n",
       "        -0.10484534, -0.10485319, -0.10485457, -0.10485711, -0.10485794]),\n",
       " 'split3_test_score': array([-0.10280473, -0.10488825, -0.10489964, -0.10490087, -0.10490087,\n",
       "        -0.1048924 , -0.10490057, -0.10490149, -0.10490425, -0.1049039 ]),\n",
       " 'split4_test_score': array([-0.10276982, -0.10485601, -0.10486954, -0.10487134, -0.10487208,\n",
       "        -0.1048608 , -0.10486985, -0.10487308, -0.10487586, -0.1048783 ]),\n",
       " 'mean_test_score': array([-0.10277762, -0.10486318, -0.10487582, -0.10487728, -0.10487765,\n",
       "        -0.10486891, -0.10487738, -0.10487919, -0.10488117, -0.10488217]),\n",
       " 'std_test_score': array([1.70215746e-05, 1.54053525e-05, 1.48139963e-05, 1.52118419e-05,\n",
       "        1.50602090e-05, 1.56349518e-05, 1.59246719e-05, 1.60567064e-05,\n",
       "        1.59822407e-05, 1.53362922e-05]),\n",
       " 'rank_test_score': array([ 1,  2,  4,  5,  7,  3,  6,  8,  9, 10], dtype=int32)}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_results = grid_search.to_sklearn().cv_results_\n",
    "gs_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Convert gs_results to the required lists\n",
    "n_estimators_val = [param_dict[\"n_estimators\"] for param_dict in gs_results[\"params\"]]\n",
    "learning_rate_val = [param_dict[\"learning_rate\"] for param_dict in gs_results[\"params\"]]\n",
    "mape_val = [-score for score in gs_results[\"mean_test_score\"]]  # Negating to get positive MAPE values\n",
    "\n",
    "# Create a scatter plot of hyperparameter combinations vs MAPE\n",
    "plt.figure(figsize=(10, 6))\n",
    "sc = plt.scatter(n_estimators_val, learning_rate_val, c=mape_val, cmap='viridis')\n",
    "plt.colorbar(sc, label='Mean Absolute Percentage Error (MAPE)')\n",
    "plt.xlabel('Number of Estimators')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.title('Hyperparameter Tuning Results')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "353961104846001ffa111d7d98923933ef13c251c8e9b3ebc563f652eb6b45f2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}