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100 lines
2.9 KiB
Transact-SQL
100 lines
2.9 KiB
Transact-SQL
USE [tpcxbb_1gb]
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GO
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-- Stored procedure that performs customer clustering using Python and SQL Server ML Services
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CREATE OR ALTER PROCEDURE [dbo].[py_generate_customer_return_clusters]
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AS
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BEGIN
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DECLARE
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-- Input query to generate the purchase history & return metrics
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@input_query NVARCHAR(MAX) = N'
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SELECT
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ss_customer_sk AS customer,
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CAST( (ROUND(COALESCE(returns_count / NULLIF(1.0*orders_count, 0), 0), 7) ) AS FLOAT) AS orderRatio,
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CAST( (ROUND(COALESCE(returns_items / NULLIF(1.0*orders_items, 0), 0), 7) ) AS FLOAT) AS itemsRatio,
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CAST( (ROUND(COALESCE(returns_money / NULLIF(1.0*orders_money, 0), 0), 7) ) AS FLOAT) AS monetaryRatio,
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CAST( (COALESCE(returns_count, 0)) AS FLOAT) AS frequency
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FROM
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(
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SELECT
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ss_customer_sk,
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-- return order ratio
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COUNT(distinct(ss_ticket_number)) AS orders_count,
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-- return ss_item_sk ratio
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COUNT(ss_item_sk) AS orders_items,
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-- return monetary amount ratio
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SUM( ss_net_paid ) AS orders_money
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FROM store_sales s
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GROUP BY ss_customer_sk
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) orders
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LEFT OUTER JOIN
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(
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SELECT
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sr_customer_sk,
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-- return order ratio
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count(distinct(sr_ticket_number)) as returns_count,
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-- return ss_item_sk ratio
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COUNT(sr_item_sk) as returns_items,
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-- return monetary amount ratio
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SUM( sr_return_amt ) AS returns_money
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FROM store_returns
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GROUP BY sr_customer_sk
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) returned ON ss_customer_sk=sr_customer_sk
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'
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EXEC sp_execute_external_script
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@language = N'Python'
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, @script = N'
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import pandas as pd
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from sklearn.cluster import KMeans
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#We concluded in step2 in the tutorial that 4 would be a good number of clusters
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n_clusters = 4
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#Perform clustering
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est = KMeans(n_clusters=n_clusters, random_state=111).fit(customer_data[["orderRatio","itemsRatio","monetaryRatio","frequency"]])
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clusters = est.labels_
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customer_data["cluster"] = clusters
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#OutputDataSet = customer_data
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'
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, @input_data_1 = @input_query
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, @input_data_1_name = N'customer_data'
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,@output_data_1_name = N'customer_data'
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with result sets (("Customer" int, "orderRatio" float,"itemsRatio" float,"monetaryRatio" float,"frequency" float,"cluster" float));
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END;
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GO
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--Creating a table for storing the clustering data
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DROP TABLE IF EXISTS [dbo].[py_customer_clusters];
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GO
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--Create a table to store the predictions in
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CREATE TABLE [dbo].[py_customer_clusters](
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[Customer] [bigint] NULL,
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[OrderRatio] [float] NULL,
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[itemsRatio] [float] NULL,
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[monetaryRatio] [float] NULL,
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[frequency] [float] NULL,
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[cluster] [int] NULL,
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) ON [PRIMARY]
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GO
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--Execute the clustering and insert results into table
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INSERT INTO py_customer_clusters
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EXEC [dbo].[py_generate_customer_return_clusters];
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-- Select contents of the table
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SELECT * FROM py_customer_clusters;
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--Get email addresses of customers in cluster 0
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SELECT customer.[c_email_address], customer.c_customer_sk
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FROM dbo.customer
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JOIN
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[dbo].[py_customer_clusters] as c
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ON c.Customer = customer.c_customer_sk
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WHERE c.cluster = 0;
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