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Irid demo scripts updated

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nelgson
2017-11-09 14:29:13 -08:00
parent f7e8e838a3
commit 48dd84f358
5 changed files with 88 additions and 128 deletions
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samples/features/machine-learning-services/r/iris/0-setup schema.sql
+32
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@@ -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/iris-native-scoring.sql → samples/features/machine-learning-services/r/iris/1-R-iris-train.sql
+3 -34
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@@ -1,32 +1,7 @@
Use sqlr;
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 */
/* 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
@@ -55,7 +30,7 @@ from iris_data'
end;
go
/* Step 3: Train & store a decision tree model that will predict species of flowers */
/* 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';
@@ -64,10 +39,4 @@ select model_name, datalength(model)/1024. as model_size_kb, datalength(native_m
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;
samples/features/machine-learning-services/r/iris/2-R-iris-score.sql
+31
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@@ -0,0 +1,31 @@
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
+22
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@@ -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;
samples/features/machine-learning-services/r/iris/iris.sql
-94
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@@ -1,94 +0,0 @@
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