---
title: "Python tutorial: Train and save model"
titleSuffix: SQL machine learning
description: In part four of this five-part tutorial series, you'll train and save a model in Python using Transact-SQL on SQL Server with SQL machine learning.
ms.prod: sql
ms.technology: machine-learning

ms.date: 07/29/2020
ms.topic: tutorial
author: dphansen
ms.author: davidph
ms.custom: seo-lt-2019
monikerRange: ">=sql-server-2017||>=sql-server-linux-ver15||>=azuresqldb-mi-current"
---

# Python tutorial: Train and save a Python model using T-SQL
[!INCLUDE [SQL Server 2017 SQL MI](../../includes/applies-to-version/sqlserver2017-asdbmi.md)]

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 [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] table.

In this article, you'll:

> [!div class="checklist"]
> + Create and train a model using a SQL stored procedure
> + Save the trained model to a SQL table

In [part one](python-taxi-classification-introduction.md), you installed the prerequisites and restored the sample database.

In [part two](python-taxi-classification-explore-data.md), you explored the sample data and generated some plots.

In [part three](python-taxi-classification-create-features.md), 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](python-taxi-classification-deploy-model.md), 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. 

   This stored procedure should already be created for you, but you can run the following code to create it:

   ```sql
   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 type an integer that represents the percentage of data allocated to the training set. For example, the following statement would allocate 60% of data to the training set.

   ```sql
   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](../../relational-databases/system-stored-procedures/sp-execute-external-script-transact-sql.md).

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 [!INCLUDE[ssManStudio](../../includes/ssmanstudio-md.md)], 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.

   ```sql
   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.

   ```sql
   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 mins. Messages that would be piped to Python's **stdout** stream are displayed in the **Messages** window of [!INCLUDE[ssManStudio](../../includes/ssmanstudio-md.md)]. For example:

   ```text
   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_.

   ```text
   SciKit_model
   0x800363736B6C6561726E2E6C696E6561....
   ```

### TrainTipPredictionModelRxPy

This stored procedure uses the new **revoscalepy** package, which is a new package for Python. 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](../python/ref-py-revoscalepy.md) and [revoscalepy function reference](/r-server/python-reference/revoscalepy/revoscalepy-package).

1. In [!INCLUDE[ssManStudio](../../includes/ssmanstudio-md.md)], 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.

   ```sql
   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*.

   ```sql
   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 [!INCLUDE[ssManStudio](../../includes/ssmanstudio-md.md)]. For example:

   ```text
   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_.

   ```text
   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:

> [!div class="checklist"]
> + Created and trained a model using a SQL stored procedure
> + Saved the trained model to a SQL table

> [!div class="nextstepaction"]
> [Python tutorial: Run predictions using Python embedded in a stored procedure](python-taxi-classification-deploy-model.md)