Merge pull request #329 from NelGson/master

Adding iris demo scripts

Author: uc-msft

samples/features/machine-learning-services/r/iris/0-setup schema.sql

@@ -0,0 +1,32 @@
+
+CREATE DATABASE sqlr;
+GO
+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
+
+

samples/features/machine-learning-services/r/iris/1-R-iris-train.sql

@@ -0,0 +1,42 @@
+USE sqlr;
+GO
+
+/* Step 1: 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 2: 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
+
+

samples/features/machine-learning-services/r/iris/2-R-iris-score.sql

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+USE sqlr;
+GO
+
+/* 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
+
+/* Test scoring of model */
+exec predict_iris_species 'iris.dtree';
+go

samples/features/machine-learning-services/r/iris/3-iris-native-scoring.sql

@@ -0,0 +1,22 @@
+Use sqlr;
+GO
+
+create or alter procedure native_predict_iris_species
+as
+begin
+		declare @native_model varbinary(max) = (select native_model from iris_models where model_name = 'iris.dtree');
+		-- Predict using native scoring on the specified model:
+		/*Generate predictions using PREDICT function */
+
+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;
+
+end;
+go
+
+EXECUTE native_predict_iris_species;
+
+
+
+