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43 lines
1.6 KiB
Transact-SQL
43 lines
1.6 KiB
Transact-SQL
USE sqlr;
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GO
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/* Step 1: Create procedure for training model */
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create or alter procedure generate_iris_model
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(@trained_model varbinary(max) OUTPUT, @native_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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# Build decision tree model to predict species based on sepal/petal attributes
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iris_model <- rxDTree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris_rx_data);
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# Serialize model to binary format for storage in SQL Server
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trained_model <- as.raw(serialize(iris_model, connection=NULL));
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# Serialize model to native binary format for scoring using PREDICT function in SQL Server
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native_trained_model <- rxSerializeModel(iris_model, realtimeScoringOnly = TRUE)
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'
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, @input_data_1 = N'
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select "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"
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from iris_data'
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, @input_data_1_name = N'iris_rx_data'
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, @params = N'
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@trained_model varbinary(max) OUTPUT, @native_trained_model varbinary(max) OUTPUT'
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, @trained_model = @trained_model OUTPUT
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, @native_trained_model = @native_trained_model OUTPUT;
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end;
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go
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/* Step 2: Train & store a decision tree model that will predict species of flowers */
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declare @model varbinary(max), @native_model varbinary(max);
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exec generate_iris_model @model OUTPUT, @native_model OUTPUT;
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delete from iris_models where model_name = 'iris.dtree';
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insert into iris_models (model_name, model, native_model) values('iris.dtree', @model, @native_model);
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select model_name, datalength(model)/1024. as model_size_kb, datalength(native_model)/1024. as native_model_size_kb
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from iris_models;
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go
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