diff --git a/samples/features/machine-learning-services/r/iris/iris-native-scoring.sql b/samples/features/machine-learning-services/r/iris/iris-native-scoring.sql new file mode 100644 index 00000000..91a1240c --- /dev/null +++ b/samples/features/machine-learning-services/r/iris/iris-native-scoring.sql @@ -0,0 +1,73 @@ +Use sqlr; +GO +/* Step 1: Setup schema */ +drop table if exists iris_data, iris_models; +go +create table iris_data ( + id int not null identity primary key + , "Sepal.Length" float not null, "Sepal.Width" float not null + , "Petal.Length" float not null, "Petal.Width" float not null + , "Species" varchar(100) null +); +create table iris_models ( + model_name varchar(30) not null primary key, + model varbinary(max) not null, + native_model varbinary(max) not null +); +go + +/* Step 2: Populate test data from iris dataset in R */ +insert into iris_data +("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species") +execute sp_execute_external_script + @language = N'R' + , @script = N'iris_data <- iris;' + , @input_data_1 = N'' + , @output_data_1_name = N'iris_data'; +go + +/* Step 3: Create procedure for training model */ +create or alter procedure generate_iris_model +(@trained_model varbinary(max) OUTPUT, @native_trained_model varbinary(max) OUTPUT) +as +begin + execute sp_execute_external_script + @language = N'R' + , @script = N' +# Build decision tree model to predict species based on sepal/petal attributes +iris_model <- rxDTree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris_rx_data); + +# Serialize model to binary format for storage in SQL Server +trained_model <- as.raw(serialize(iris_model, connection=NULL)); + +# Serialize model to native binary format for scoring using PREDICT function in SQL Server +native_trained_model <- rxSerializeModel(iris_model, realtimeScoringOnly = TRUE) +' + , @input_data_1 = N' +select "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species" +from iris_data' + , @input_data_1_name = N'iris_rx_data' + + , @params = N' +@trained_model varbinary(max) OUTPUT, @native_trained_model varbinary(max) OUTPUT' + , @trained_model = @trained_model OUTPUT + , @native_trained_model = @native_trained_model OUTPUT; +end; +go + +/* Step 3: Train & store a decision tree model that will predict species of flowers */ +declare @model varbinary(max), @native_model varbinary(max); +exec generate_iris_model @model OUTPUT, @native_model OUTPUT; +delete from iris_models where model_name = 'iris.dtree'; +insert into iris_models (model_name, model, native_model) values('iris.dtree', @model, @native_model); +select model_name, datalength(model)/1024. as model_size_kb, datalength(native_model)/1024. as native_model_size_kb + from iris_models; +go + +/* Step 4: Generate predictions using PREDICT function */ +declare @native_model varbinary(max) = + (select native_model from iris_models where model_name = 'iris.dtree'); +select p.*, d.Species as "Species.Actual", d.id + from PREDICT(MODEL = @native_model, DATA = dbo.iris_data as d) + with(setosa_Pred float, versicolor_Pred float, virginica_Pred float) as p; + diff --git a/samples/features/machine-learning-services/r/iris/iris.sql b/samples/features/machine-learning-services/r/iris/iris.sql new file mode 100644 index 00000000..d326fe9e --- /dev/null +++ b/samples/features/machine-learning-services/r/iris/iris.sql @@ -0,0 +1,94 @@ +USE sqlr; +GO +/* Step 1: Setup schema */ +drop table if exists iris_data, iris_models; +go +create table iris_data ( + id int not null identity primary key + , "Sepal.Length" float not null, "Sepal.Width" float not null + , "Petal.Length" float not null, "Petal.Width" float not null + , "Species" varchar(100) null +); +create table iris_models ( + model_name varchar(30) not null primary key, + model varbinary(max) not null, + native_model varbinary(max) not null +); +go + +/* Step 2: Populate test data from iris dataset in R */ +insert into iris_data +("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species") +execute sp_execute_external_script + @language = N'R' + , @script = N'iris_data <- iris;' + , @input_data_1 = N'' + , @output_data_1_name = N'iris_data'; +go + +/* Step 3: Create procedure for training model */ +create or alter procedure generate_iris_model +(@trained_model varbinary(max) OUTPUT, @native_trained_model varbinary(max) OUTPUT) +as +begin + execute sp_execute_external_script + @language = N'R' + , @script = N' +# Build decision tree model to predict species based on sepal/petal attributes +iris_model <- rxDTree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris_rx_data); + +# Serialize model to binary format for storage in SQL Server +trained_model <- as.raw(serialize(iris_model, connection=NULL)); + +# Serialize model to native binary format for scoring using PREDICT function in SQL Server +native_trained_model <- rxSerializeModel(iris_model, realtimeScoringOnly = TRUE) +' + , @input_data_1 = N' +select "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species" +from iris_data' + , @input_data_1_name = N'iris_rx_data' + + , @params = N' +@trained_model varbinary(max) OUTPUT, @native_trained_model varbinary(max) OUTPUT' + , @trained_model = @trained_model OUTPUT + , @native_trained_model = @native_trained_model OUTPUT; +end; +go + +/* Step 3: Train & store a decision tree model that will predict species of flowers */ +declare @model varbinary(max), @native_model varbinary(max); +exec generate_iris_model @model OUTPUT, @native_model OUTPUT; +delete from iris_models where model_name = 'iris.dtree'; +insert into iris_models (model_name, model, native_model) values('iris.dtree', @model, @native_model); +select model_name, datalength(model)/1024. as model_size_kb, datalength(native_model)/1024. as native_model_size_kb + from iris_models; +go + +/* Step 4: Create procedure for scoring using the decision tree model */ +create or alter procedure predict_iris_species (@model varchar(100)) +as +begin + declare @rx_model varbinary(max) = (select model from iris_models where model_name = @model); + -- Predict based on the specified model: + exec sp_execute_external_script + @language = N'R' + , @script = N' +# Unserialize model from SQL Server +irismodel<-unserialize(rx_model); + +# Predict species for new data using rxDTree model +OutputDataSet <-rxPredict(irismodel, iris_rx_data, extraVarsToWrite = c("Species", "id")); + ' + , @input_data_1 = N' +select id, "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species" +from iris_data' + , @input_data_1_name = N'iris_rx_data' + , @params = N'@rx_model varbinary(max)' + , @rx_model = @rx_model + with result sets ( ("setosa_Pred" float, "versicolor_Pred" float, "virginica_Pred" float, "Species.Actual" varchar(100), "id" int)); +end; +go + +/* Step 5: Test scoring of model */ +exec predict_iris_species 'iris.dtree'; +go