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getting started samples for Rservices added
This commit is contained in:
Nellie Gustafsson
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# Input Query
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input_query <- "
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SELECT
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ss_customer_sk AS customer,
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round(CASE WHEN ((orders_count = 0) OR (returns_count IS NULL) OR (orders_count IS NULL) OR ((returns_count / orders_count) IS NULL) ) THEN 0.0 ELSE (cast(returns_count as nchar(10)) / orders_count) END, 7) AS orderRatio,
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round(CASE WHEN ((orders_items = 0) OR(returns_items IS NULL) OR (orders_items IS NULL) OR ((returns_items / orders_items) IS NULL) ) THEN 0.0 ELSE (cast(returns_items as nchar(10)) / orders_items) END, 7) AS itemsRatio,
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round(CASE WHEN ((orders_money = 0) OR (returns_money IS NULL) OR (orders_money IS NULL) OR ((returns_money / orders_money) IS NULL) ) THEN 0.0 ELSE (cast(returns_money as nchar(10)) / orders_money) END, 7) AS monetaryRatio,
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round(CASE WHEN ( returns_count IS NULL ) THEN 0.0 ELSE returns_count END, 0) 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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# Define the connection string
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connStr <- paste("Driver=SQL Server;Server=", "NELLIELAPTOP", ";Database=", "tpcx1b", ";Trusted_Connection=true;", sep = "");
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# Input customer data that needs to be classified
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customer_returns <- RxSqlServerData(sqlQuery = input_query,
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colClasses = c(customer = "numeric", orderRatio = "numeric", itemsRatio = "numeric", monetaryRatio = "numeric", frequency = "numeric"),
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connectionString = connStr);
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# Transform the data from an input dataset to an output dataset
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customer_data <- rxDataStep(customer_returns);
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#Look at the data we just loaded from SQL Server
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head(customer_data, n = 5);
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# Determine number of clusters
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#Using a plot of the within groups sum of squares by number of clusters extracted can help determine the appropriate number of clusters.
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#We are looking for a bend in the plot. It is at this "elbow" in the plot that we have the appropriate number of clusters
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wss <- (nrow(customer_data) - 1) * sum(apply(customer_data, 2, var))
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for (i in 2:20) {
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xt = kmeans(customer_data, centers = i)
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print(xt$ifault)
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wss[i] <- sum(kms = kmeans(customer_data, centers = i)$withinss)
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}
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plot(1:20, wss, type = "b", xlab = "Number of Clusters", ylab = "Within groups sum of squares")
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# Output table to hold the customer group mappings
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return_cluster = RxSqlServerData(table = "return_cluster", connectionString = connStr);
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# Set.seed for random number generator for predicatability
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set.seed(10);
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# Generate clusters using rxKmeans and output key / cluster to a table in SQL Server called return_cluster
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clust <- rxKmeans( ~ orderRatio + itemsRatio + monetaryRatio + frequency, customer_returns, numClusters = 4
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, outFile = return_cluster, outColName = "cluster", extraVarsToWrite = c("customer"), overwrite = TRUE);
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# Read the custome returns cluster table
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customer_cluster <- rxDataStep(return_cluster);
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#Plot the clusters (need to install library "cluster")
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#install.packages("cluster")
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library("cluster");
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clusplot(customer_data, customer_cluster$cluster, color=TRUE, shade=TRUE, labels=4, lines=0, plotchar = TRUE);
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#Look at the clustering details and analyze results
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clust
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@@ -0,0 +1,102 @@
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USE [tpcx1b]
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DROP PROC IF EXISTS generate_customer_return_clusters;
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GO
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CREATE procedure [dbo].[generate_customer_return_clusters]
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AS
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/*
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This procedure uses R to classify customers into different groups based on their
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purchase & return history.
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*/
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BEGIN
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DECLARE @duration FLOAT
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, @predict_duration FLOAT
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, @instance_name NVARCHAR(100) = @@SERVERNAME
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, @database_name NVARCHAR(128) = db_name()
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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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round(CASE WHEN ((orders_count = 0) OR (returns_count IS NULL) OR (orders_count IS NULL) OR ((returns_count / orders_count) IS NULL) ) THEN 0.0 ELSE (cast(returns_count as nchar(10)) / orders_count) END, 7) AS orderRatio,
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round(CASE WHEN ((orders_items = 0) OR(returns_items IS NULL) OR (orders_items IS NULL) OR ((returns_items / orders_items) IS NULL) ) THEN 0.0 ELSE (cast(returns_items as nchar(10)) / orders_items) END, 7) AS itemsRatio,
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round(CASE WHEN ((orders_money = 0) OR (returns_money IS NULL) OR (orders_money IS NULL) OR ((returns_money / orders_money) IS NULL) ) THEN 0.0 ELSE (cast(returns_money as nchar(10)) / orders_money) END, 7) AS monetaryRatio,
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round(CASE WHEN ( returns_count IS NULL ) THEN 0.0 ELSE returns_count END, 0) 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'R'
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, @script = N'
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# Define the connection string
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connStr <- paste("Driver=SQL Server;Server=", instance_name, ";Database=", database_name, ";Trusted_Connection=true;", sep="");
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# Input customer data that needs to be classified. This is the result we get from our query
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customer_returns <- RxSqlServerData(sqlQuery = input_query,
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colClasses = c(customer = "numeric", orderRatio = "numeric", itemsRatio = "numeric", monetaryRatio = "numeric", frequency = "numeric"),
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connectionString = connStr);
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# Output table to hold the customer cluster mappings
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return_cluster = RxSqlServerData(table = "customer_return_clusters", connectionString = connStr);
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# set.seed for random number generator for predicatability
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set.seed(10);
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# generate clusters using rxKmeans and output clusters to a table called "customer_return_clusters".
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clust <- rxKmeans( ~ orderRatio + itemsRatio + monetaryRatio + frequency, customer_returns, numClusters = 4
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, outFile = return_cluster, outColName = "cluster", writeModelVars = TRUE , extraVarsToWrite = c("customer"), overwrite = TRUE);
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'
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, @input_data_1 = N''
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, @params = N'@instance_name nvarchar(100), @database_name nvarchar(128), @input_query nvarchar(max), @duration float OUTPUT'
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, @instance_name = @instance_name
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, @database_name = @database_name
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, @input_query = @input_query
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, @duration = @duration OUTPUT;
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END;
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GO
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--Empty table of the results before running the stored procedure
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TRUNCATE TABLE customer_return_clusters;
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--Execute the clustering. This will load the table customer_return_clusters with cluster mappings
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EXEC [dbo].[generate_customer_return_clusters];
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--Now select data from table customer_return_clusters to verify that the clustering data was loaded
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SELECT * FROM customer_return_clusters;
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--Select email addresses of customers in cluster 1
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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].[customer_return_clusters] as r
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ON r.customer = customer.c_customer_sk
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WHERE r.cluster = 1
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@@ -0,0 +1,54 @@
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#Connection string to connect to SQL Server
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connStr <- paste("Driver=SQL Server; Server=", "MyServer",
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";Database=", "tutorialdb", ";Trusted_Connection=true;", sep = "");
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#Get the data from SQL Server Table
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SQL_rentaldata <- RxSqlServerData(table = "dbo.rental_data",
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connectionString = connStr, returnDataFrame = TRUE);
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#Import the data into a data frame
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rentaldata <- rxImport(SQL_rentaldata);
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#Let's see the structure of the data and the top rows
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head(rentaldata);
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str(rentaldata);
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#Changing the three factor columns to factor types
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#This helps when building the model because we are explicitly saying that these values are categorical
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rentaldata$Holiday <- factor(rentaldata$Holiday);
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rentaldata$Snow <- factor(rentaldata$Snow);
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rentaldata$WeekDay <- factor(rentaldata$WeekDay);
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#Visualize the dataset after the change
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str(rentaldata);
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#Now let's split the dataset into 2 different sets
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#One set for training the model and the other for validating it
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train_data = rentaldata[rentaldata$Year < 2015,];
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test_data = rentaldata[rentaldata$Year == 2015,];
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#Use this column to check the quality of the prediction against actual values
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actual_counts <- test_data$RentalCount;
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#Model 1: Use rxLinMod to create a linear regression model. We are training the data using the training data set
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model_linmod <- rxLinMod(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = train_data);
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#Model 2: Use rxDTree to create a decision tree model. We are training the data using the training data set
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model_dtree <- rxDTree(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = train_data);
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#Use the models we just created to predict using the test data set.
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#That enables us to compare actual values of RentalCount from the two models and compare to the actual values in the test data set
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predict_linmod <- rxPredict(model_linmod, test_data, writeModelVars = TRUE);
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predict_dtree <- rxPredict(model_dtree, test_data, writeModelVars = TRUE);
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#Look at the top rows of the two prediction data sets.
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head(predict_linmod);
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head(predict_dtree);
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#Now we will use the plotting functionality in R to viusalize the results from the predictions
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#We are plotting the difference between actual and predicted values for both models to compare accuracy
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par(mfrow = c(2, 1));
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plot(predict_linmod$RentalCount_Pred - predict_linmod$RentalCount, main = "Difference between actual and predicted. rxLinmod");
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plot(predict_dtree$RentalCount_Pred - predict_dtree$RentalCount, main = "Difference between actual and predicted. rxDTree");
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@@ -0,0 +1,103 @@
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--Before we start, we need to restore the DB for this tutorial.
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--Step1:Download the compressed backup file
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--Save the file on a location where SQL Server can access it. For example:C:\Program Files \Microsoft SQL Server \MSSQL13.MSSQLSERVER\MSSQL\Backup\
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--In a new query window in SSMS, execute the following restore statement, but REMEMBER TO CHANGE THE FILE PATHS
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--to match the directories of your installation!
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USE master;
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GO
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RESTORE DATABASE TutorialDB
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FROM DISK = 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\Backup\TutorialDB.bak'
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WITH
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MOVE 'TutorialDB' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.mdf'
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, MOVE 'TutorialDB_log' TO 'C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\DATA\TutorialDB.ldf';
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GO
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USE tutorialdb;
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SELECT * FROM [dbo].[rental_data];
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-- Operationalize
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USE tutorialdb;
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GO
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-- Setup model table
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DROP TABLE IF EXISTS rental_rx_models;
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GO
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CREATE TABLE rental_rx_models (
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model_name VARCHAR(30) NOT NULL DEFAULT('default model') PRIMARY KEY,
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model VARBINARY(MAX) NOT NULL
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);
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GO
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-- Stored procedure that trains and generates a model using the rental_data and a decision tree algorithm
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DROP PROCEDURE IF EXISTS generate_rental_rx_model;
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go
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CREATE PROCEDURE generate_rental_rx_model (@trained_model varbinary(max) OUTPUT)
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AS
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BEGIN
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EXECUTE sp_execute_external_script
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@language = N'R'
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, @script = N'
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require("RevoScaleR");
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rental_train_data$Holiday = factor(rental_train_data$Holiday);
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rental_train_data$Snow = factor(rental_train_data$Snow);
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rental_train_data$WeekDay = factor(rental_train_data$WeekDay);
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#Create a dtree model and train it using the training data set
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model_dtree <- rxDTree(RentalCount ~ Month + Day + WeekDay + Snow + Holiday, data = rental_train_data);
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#Before saving the model to the DB table, we need to serialize it
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trained_model <- as.raw(serialize(model_dtree, connection=NULL));'
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, @input_data_1 = N'select "RentalCount", "Month", "Day", "WeekDay", "Snow", "Holiday" from dbo.rental_data where Year < 2015'
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, @input_data_1_name = N'rental_train_data'
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, @params = N'@trained_model varbinary(max) OUTPUT'
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, @trained_model = @trained_model OUTPUT;
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END;
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GO
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TRUNCATE TABLE rental_rx_models;
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--Script to call the stored procedure that generates the rxDTree model and save the model in a table in SQL Server
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DECLARE @model VARBINARY(MAX);
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EXEC generate_rental_rx_model @model OUTPUT;
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INSERT INTO rental_rx_models (model_name, model) VALUES('rxDTree', @model);
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SELECT * FROM rental_rx_models;
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GO
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--Stored procedure that takes model name and new data as inout parameters and predicts the rental count for the new data
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DROP PROCEDURE IF EXISTS predict_rentals;
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GO
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CREATE PROCEDURE predict_rentals (@model VARCHAR(100),@q NVARCHAR(MAX))
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AS
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BEGIN
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DECLARE @rx_model VARBINARY(MAX) = (SELECT model FROM rental_rx_models WHERE model_name = @model);
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EXECUTE sp_execute_external_script
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@language = N'R'
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, @script = N'
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require("RevoScaleR");
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#The InputDataSet contains the new data passed to this stored proc. We will use this data to predict.
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rentals = InputDataSet;
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#Convert types to factors
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rentals$Holiday = factor(rentals$Holiday);
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rentals$Snow = factor(rentals$Snow);
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rentals$WeekDay = factor(rentals$WeekDay);
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#Before using the model to predict, we need to unserialize it
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rental_model = unserialize(rx_model);
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#Call prediction function
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rental_predictions = rxPredict(rental_model, rentals);'
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, @input_data_1 = @q
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, @output_data_1_name = N'rental_predictions'
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, @params = N'@rx_model varbinary(max)'
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, @rx_model = @rx_model
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WITH RESULT SETS (("RentalCount_Predicted" FLOAT));
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END;
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GO
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--Execute the predict_rentals stored proc and pass the modelname and a query string with a set of features we want to use to predict the rental count
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EXEC dbo.predict_rentals @model = 'rxDTree',
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@q ='SELECT CONVERT(INT, 3) AS Month, CONVERT(INT, 24) AS Day, CONVERT(INT, 4) AS WeekDay, CONVERT(INT, 1) AS Snow, CONVERT(INT, 1) AS Holiday';
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GO
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