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Python tutorial: Train and save a Python model using T-SQL

  • Artikel

Applies to: SQL Server 2017 (14.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 using the Python packages scikit-learn and revoscalepy. These Python libraries are already installed with SQL Server machine learning.

You'll load the modules and call the necessary functions to create and train the model using a SQL Server stored procedure. The model requires the data features you engineered in earlier parts of this tutorial series. Finally, you'll save the trained model to a SQL Server table.

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 explored the sample data and generated 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.

Split the sample data into training and testing sets

  1. Create a stored procedure called PyTrainTestSplit to divide the data in the nyctaxi_sample table into two parts: nyctaxi_sample_training and nyctaxi_sample_testing.

    Run the following code to create it:

    DROP PROCEDURE IF EXISTS PyTrainTestSplit;
GO

CREATE PROCEDURE [dbo].[PyTrainTestSplit] (@pct int)
AS

DROP TABLE IF EXISTS dbo.nyctaxi_sample_training
SELECT * into nyctaxi_sample_training FROM nyctaxi_sample WHERE (ABS(CAST(BINARY_CHECKSUM(medallion,hack_license)  as int)) % 100) < @pct

DROP TABLE IF EXISTS dbo.nyctaxi_sample_testing
SELECT * into nyctaxi_sample_testing FROM nyctaxi_sample
WHERE (ABS(CAST(BINARY_CHECKSUM(medallion,hack_license)  as int)) % 100) > @pct
GO

  2. To divide your data using a custom split, run the stored procedure, and provide an integer parameter that represents the percentage of data to allocate to the training set. For example, the following statement would allocate 60% of data to the training set.

    EXEC PyTrainTestSplit 60
GO

Build a logistic regression model

After the data has been prepared, you can use it to train a model. You do this by calling a stored procedure that runs some Python code, taking as input the training data table. For this tutorial, you create two models, both binary classification models:

  • The stored procedure PyTrainScikit creates a tip prediction model using the scikit-learn package.
  • The stored procedure TrainTipPredictionModelRxPy creates a tip prediction model using the revoscalepy package.

Each stored procedure uses the input data you provide to create and train a logistic regression model. All Python code is wrapped in the system stored procedure, sp_execute_external_script.

To make it easier to retrain the model on new data, you wrap the call to sp_execute_external_script in another stored procedure, and pass in the new training data as a parameter. This section will walk you through that process.

PyTrainScikit

  1. In Management Studio, open a new Query window and run the following statement to create the stored procedure PyTrainScikit. The stored procedure contains a definition of the input data, so you don't need to provide an input query.

    DROP PROCEDURE IF EXISTS PyTrainScikit;
GO

CREATE PROCEDURE [dbo].[PyTrainScikit] (@trained_model varbinary(max) OUTPUT)
AS
BEGIN
EXEC sp_execute_external_script
  @language = N'Python',
  @script = N'
import numpy
import pickle
from sklearn.linear_model import LogisticRegression

##Create SciKit-Learn logistic regression model
X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]]
y = numpy.ravel(InputDataSet[["tipped"]])

SKLalgo = LogisticRegression()
logitObj = SKLalgo.fit(X, y)

##Serialize model
trained_model = pickle.dumps(logitObj)
',
@input_data_1 = N'
select tipped, fare_amount, passenger_count, trip_time_in_secs, trip_distance, 
dbo.fnCalculateDistance(pickup_latitude, pickup_longitude,  dropoff_latitude, dropoff_longitude) as direct_distance
from nyctaxi_sample_training
',
@input_data_1_name = N'InputDataSet',
@params = N'@trained_model varbinary(max) OUTPUT',
@trained_model = @trained_model OUTPUT;
;
END;
GO

  2. Run the following SQL statements to insert the trained model into table nyc_taxi_models.

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

    Processing of the data and fitting the model might take a couple of minutes. Messages that would be piped to Python's stdout stream are displayed in the Messages window of Management Studio. For example:

    STDOUT message(s) from external script:
C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\lib\site-packages\revoscalepy

  3. Open the table nyc_taxi_models. You can see that one new row has been added, which contains the serialized model in the column model.

    SciKit_model
0x800363736B6C6561726E2E6C696E6561....

TrainTipPredictionModelRxPy

This stored procedure uses the revoscalepy Python package. It contains objects, transformation, and algorithms similar to those provided for the R language's RevoScaleR package.

By using revoscalepy, you can create remote compute contexts, move data between compute contexts, transform data, and train predictive models using popular algorithms such as logistic and linear regression, decision trees, and more. For more information, see revoscalepy module in SQL Server and revoscalepy function reference.

  1. In Management Studio, open a new Query window and run the following statement to create the stored procedure TrainTipPredictionModelRxPy. Because the stored procedure already includes a definition of the input data, you don't need to provide an input query.

    DROP PROCEDURE IF EXISTS TrainTipPredictionModelRxPy;
GO

CREATE PROCEDURE [dbo].[TrainTipPredictionModelRxPy] (@trained_model varbinary(max) OUTPUT)
AS
BEGIN
EXEC sp_execute_external_script 
  @language = N'Python',
  @script = N'
import numpy
import pickle
from revoscalepy.functions.RxLogit import rx_logit

## Create a logistic regression model using rx_logit function from revoscalepy package
logitObj = rx_logit("tipped ~ passenger_count + trip_distance + trip_time_in_secs + direct_distance", data = InputDataSet);

## Serialize model
trained_model = pickle.dumps(logitObj)
',
@input_data_1 = N'
select tipped, fare_amount, passenger_count, trip_time_in_secs, trip_distance, 
dbo.fnCalculateDistance(pickup_latitude, pickup_longitude,  dropoff_latitude, dropoff_longitude) as direct_distance
from nyctaxi_sample_training
',
@input_data_1_name = N'InputDataSet',
@params = N'@trained_model varbinary(max) OUTPUT',
@trained_model = @trained_model OUTPUT;
;
END;
GO

    This stored procedure performs the following steps as part of model training:

    • The SELECT query applies 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 Python input variable, InputDataset.
    • 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 is serialized and stored in the Python variable logitObj. By adding the T-SQL keyword OUTPUT, you can add the variable as an output of the stored procedure. In the next step, that variable is used to insert the binary code of the model into a database table nyc_taxi_models. This mechanism makes it easy to store and re-use models.

  2. Run the stored procedure as follows to insert the trained revoscalepy model into the table nyc_taxi_models.

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

    Processing of the data and fitting the model might take a while. Messages that would be piped to Python's stdout stream are displayed in the Messages window of Management Studio. For example:

    STDOUT message(s) from external script:
C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\lib\site-packages\revoscalepy

  3. Open the table nyc_taxi_models. You can see that one new row has been added, which contains the serialized model in the column model.

    revoscalepy_model
0x8003637265766F7363616c....

In the next part of this tutorial, you'll use the trained models to create 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

Python tutorial: Run predictions using Python embedded in a stored procedure