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R tutorial: Train and save model

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Applies to: SQL Server 2016 (13.x) and later Azure SQL Managed Instance

In part four of this five-part tutorial series, you'll learn how to train a machine learning model by using R. You'll train the model using the data features you created in the previous part, and then save the trained model in a SQL Server table. In this case, the R packages are already installed with R Services (In-Database), so everything can be done from SQL.

In this article, you'll:

  • Create and train a model using a SQL stored procedure
  • Save the trained model to a SQL table

In part one, you installed the prerequisites and restored the sample database.

In part two, you reviewed the sample data and generate some plots.

In part three, you learned how to create features from raw data by using a Transact-SQL function. You then called that function from a stored procedure to create a table that contains the feature values.

In part five, you'll learn how to operationalize the models that you trained and saved in part four.

Create the stored procedure

When calling R from T-SQL, you use the system stored procedure, sp_execute_external_script. However, for processes that you repeat often, such as retraining a model, it is easier to encapsulate the call to sp_execute_external_script in another stored procedure.

  1. In Management Studio, open a new Query window.

  2. Run the following statement to create the stored procedure RTrainLogitModel. This stored procedure defines the input data and uses glm to create a logistic regression model.

    CREATE PROCEDURE [dbo].[RTrainLogitModel] (@trained_model varbinary(max) OUTPUT)

AS
BEGIN
  DECLARE @inquery nvarchar(max) = N'
    select tipped, fare_amount, passenger_count,trip_time_in_secs,trip_distance,
    pickup_datetime, dropoff_datetime,
    dbo.fnCalculateDistance(pickup_latitude, pickup_longitude,  dropoff_latitude, dropoff_longitude) as direct_distance
    from nyctaxi_sample
    tablesample (70 percent) repeatable (98052)
'

  EXEC sp_execute_external_script @language = N'R',
                                  @script = N'
## Create model
logitObj <- glm(tipped ~ passenger_count + trip_distance + trip_time_in_secs + direct_distance, data = InputDataSet, family = binomial)
summary(logitObj)

## Serialize model 
trained_model <- as.raw(serialize(logitObj, NULL));
',
  @input_data_1 = @inquery,
  @params = N'@trained_model varbinary(max) OUTPUT',
  @trained_model = @trained_model OUTPUT; 
END
GO

    • To ensure that some data is left over to test the model, 70% of the data are randomly selected from the taxi data table for training purposes.

    • The SELECT query uses the custom scalar function fnCalculateDistance to calculate the direct distance between the pick-up and drop-off locations. The results of the query are stored in the default R input variable, InputDataset.

    • The R script calls the R function glm to create the logistic regression model.

      The binary variable tipped is used as the label or outcome column, and the model is fit using these feature columns: passenger_count, trip_distance, trip_time_in_secs, and direct_distance.

    • The trained model, saved in the R variable logitObj, is serialized and returned as an output parameter.

Train and deploy the R model using the stored procedure

Because the stored procedure already includes a definition of the input data, you don't need to provide an input query.

  1. To train and deploy the R model, call the stored procedure and insert it into the database table nyc_taxi_models, so that you can use it for future predictions:

    DECLARE @model VARBINARY(MAX);
EXEC RTrainLogitModel @model OUTPUT;
INSERT INTO nyc_taxi_models (name, model) VALUES('RTrainLogit_model', @model);

  2. Watch the Messages window of Management Studio for messages that would be piped to R's stdout stream, like this message:

    "STDOUT message(s) from external script: Rows Read: 1193025, Total Rows Processed: 1193025, Total Chunk Time: 0.093 seconds"

  3. When the statement has completed, open the table nyc_taxi_models. Processing of the data and fitting the model might take a while.

    You can see that one new row has been added, which contains the serialized model in the column model and the model name RTrainLogit_model in the column name.

    model                        name
---------------------------- ------------------
0x580A00000002000302020....  RTrainLogit_model

In the next part of this tutorial you'll use the trained model to generate predictions.

Next steps

In this article, you:

  • Created and trained a model using a SQL stored procedure
  • Saved the trained model to a SQL table

R tutorial: Run predictions in SQL stored procedures