4.7.7.3 Importing an External ONNX Model

Before You Begin

Review the following:
- ONNX Models Overview
- ONNX Model Metadata
Review ONNX Model Metadata.

Ways to Import External ONNX Model

You have the following ways to import an external ONNX model.

Import model as a string: For smaller models, you can copy the encoded string and paste it into a session variable or temporary table column. You can then import the table with the copied string. To do this, you run the ML_MODEL_IMPORT routine and import the model as a pre-processed model object.
Import model directly from a table: For larger models, you can load the entire file into a table with the appropriate parameters. You can then import the table directly into your model catalog. If needed, you can load the model in batches of smaller files. To do this, you run the ML_MODEL_IMPORT routine and import the model as a table.
The table that you load the model into must have the following columns:
- chunk_id: The recommended parameters are INT AUTO_INCREMENT PRIMARY KEY. There must be only one row in the table with chunk_id = 1.
- model_object: The recommended parameters are LONGTEXT NOT NULL.
- model_metadata: The recommended parameters are JSON DEFAULT NULL.

Workflow to Import an ONNX Model

The workflow to import an ONNX model includes the following:

Convert the ONNX file to Base 64 encoding and carry out sting serialization. See Encoding ONNX File.
Depending on the size of the model, select if you want to import the model as a string in a pre-processed model object (smaller files) or as a table (larger files). Then, refer to the appropriate section to prepare the model file. See either Preparing to Import ONNX Model as a Pre-Processed Object or Preparing to Import ONNX Model as a Table.
Define the model metadata as needed depending on the type of machine learning task for the model. See Defining Model Metadata.
Import the model by using the ML_MODEL_IMPORT routine. See either Importing ONNX Model as a Pre-processed Object to import the model as a string or Importing ONNX Model as a Table.

Encoding ONNX File

Before importing an ONNX model, you must convert the ONNX file to Base 64 encoding and carry out string serialization. Do this with the Python base64 module. Ensure you have the appropriate version of Python installed.

To encode the ONNX file:

Open a terminal window (command prompt on Windows).
Install the ONNX library.
```
pip install onnx
```

Launch Python and run the following code.

# python3 encode_onnx_base64.py
import onnx
import base64

with open("output_file_name", "wb") as f:
    model = onnx.load("input_file_name")
    f.write(base64.b64encode(model.SerializeToString()))

Replace input_file_name with the full file path to the ONNX file and output_file_name with the desired file name for the encoded file. If needed, set a file path for the output file.

The following example converts the /Users/user1/iris.onnx file and creates the output file iris_base64.onnx.

# python3 encode_onnx_base64.py
import onnx
import base64

with open("iris_base64.onnx", "wb") as f:
    model = onnx.load("/Users/user1/iris.onnx")
    f.write(base64.b64encode(model.SerializeToString()))

After encoding the ONNX file, select the method to import the model and review the appropriate steps.

Preparing to Import ONNX Model as a Pre-Processed Object

For smaller model files, you can import the ONNX model as a string into a pre-processed object.

To prepare to import the ONNX Model as a string:

Open the encoded file and copy the string.
Connect to the MySQL server.
Copy and paste the converted string for the file into a session variable. For example:
```
mysql> SET @onnx_string_model_object='ONNX_file_string';
```

Alternatively, you can load the encoded file directly into a table column. Make sure you do the following:

Set the appropriate local-infile setting for the client. The server setting local_infile=ON is enabled by default. Verify with your admin before using these settings. See Security Considerations for LOAD DATA LOCAL to learn more.
Upload the file to the appropriate folder in the MySQL server based on the secure_file_priv setting. To review this setting, connect to the MySQL server and run the following command:
```
mysql> SHOW VARIABLES LIKE 'secure_file_priv';
```

To load the encoded file directly into a table column:

From a terminal window, upload the ONNX file to the folder of your username in the compute instance.
```
$> scp -v -i ssh-key.key /Users/user1/iris_base64.onnx user1@ComputeInstancePublicIP:/home/user1/
```
Replace the following:
- ssh-key.key: The full file path to the SSH key file (.key) for the compute instance.
- /Users/user1/iris_base64.onnx: The full file path to the ONNX file on your device.
- user1@ComputeInstancePublicIP: The appropriate username and public IP for the compute instance.
- /home/user1/: The appropriate file path to your username in the compute instance.
Once the upload successfully completes, SSH into the compute instance.
```
$> ssh -i ssh-key.key user1@computeInstancePublicIP
```
Replace the following:
- ssh-key.key: The full file path to the SSH key file (.key) for the compute instance.
- user1@ComputeInstancePublicIP: The appropriate username and public IP for the compute instance.
Change the directory to the one for your username.
```
$> cd /home/user1
```
Replace user1 with your own username.
Create a copy of the ONNX file.
```
$> touch iris_base64.onnx
```
Replace iris_base64.onnx with the file name of the ONNX file.
Copy the ONNX file to the appropriate folder in the MySQL server based on the secure_file_priv setting.
```
$> sudo cp iris_base64.onnx /var/lib/mysql-files
```
Replace the following:
- iris_base64.onnx: The file name of the ONNX file.
- /var/lib/mysql-files: The file path based on the secure_file_priv setting.
Update the owner and group of the file path previously specified that has the uploaded ONNX file.
```
$> sudo chown -R mysql:mysql /var/lib/mysql-files
```
Replace /var/lib/mysql-files with the file path previously specified.
Connect to the MySQL server with the local-infile setting to 1.
```
> mysql -u user1 -p --local-infile=1
```
Replace user1 with your MySQL username.
Create and use the database to store the table. For example:
```
mysql> CREATE DATABASE onnx_model;
mysql> USE onnx_model;
```
Create a table with only one column to store the string.
The following example creates the onnx_temp table with the onnx_string column with the LONGTEXT data type.
```
mysql> CREATE TABLE onnx_temp (onnx_string LONGTEXT);
```
Use a LOAD DATA INFILE statement to load the pre-processed .onnx file into the temporary table.
The following example loads the iris_base64.onnx file with the string into the onnx_string column in the onnx_temp table.
```
mysql> LOAD DATA INFILE 'iris_base64.onnx' 
       INTO TABLE onnx_temp 
       CHARACTER SET binary 
       FIELDS TERMINATED BY '\t' 
       LINES TERMINATED BY '\r' (onnx_string);
```
Insert the loaded string into a session variable.
The following example loads the loaded string in the onnx_string column into the @onnx_table_model_object session variable.
```
mysql> SELECT onnx_string FROM onnx_temp INTO @onnx_table_model_object;
```

After preparing the model, you can Defining Model Metadata.

Preparing to Import ONNX Model as a Table

For larger model files, you must import the model as a table. Make sure you do the following:

Set the appropriate local-infile setting for the client. The server setting local_infile=ON is enabled by default. Verify with your admin before using these settings. See Security Considerations for LOAD DATA LOCAL to learn more.
Upload the file to the appropriate folder in the MySQL server based on the secure_file_priv setting. To review this setting, connect to the MySQL server and run the following command:
```
mysql> SHOW VARIABLES LIKE 'secure_file_priv';
```

To import the model as a table:

From a terminal window, upload the ONNX file to the folder of your username in the compute instance.
```
$> scp -v -i ssh-key.key /Users/user1/iris_base64.onnx user1@ComputeInstancePublicIP:/home/user1/
```
Replace the following:
- ssh-key.key: The full file path to the SSH key file (.key) for the compute instance.
- /Users/user1/iris_base64.onnx: The full file path to the ONNX file on your device.
- user1@ComputeInstancePublicIP: The appropriate username and public IP for the compute instance.
- /home/user1/: The appropriate file path to your username in the compute instance.
Once the upload successfully completes, SSH into the compute instance.
```
$> ssh -i ssh-key.key user1@computeInstancePublicIP
```
Replace the following:
- ssh-key.key: The full file path to the SSH key file (.key) for the compute instance.
- user1@ComputeInstancePublicIP: The appropriate username and public IP for the compute instance.
Change the directory to the one for your username.
```
$> cd /home/user1
```
Replace user1 with your own username.
Create a copy of the ONNX file.
```
$> touch iris_base64.onnx
```
Replace iris_base64.onnx with the file name of the ONNX file.
Copy the ONNX file to the appropriate folder in the MySQL server based on the secure_file_priv setting.
```
$> sudo cp iris_base64.onnx /var/lib/mysql-files
```
Replace the following:
- iris_base64.onnx: The file name of the ONNX file.
- /var/lib/mysql-files: The file path based on the secure_file_priv setting.
Update the owner and group of the file path previously specified that has the uploaded ONNX file.
```
$> sudo chown -R mysql:mysql /var/lib/mysql-files
```
Replace /var/lib/mysql-files with the file path previously specified.
Connect to the MySQL server with the local-infile setting to 1.
```
> mysql -u user1 -p --local-infile=1
```
Replace user1 with your MySQL username.
Create and use the database to store the table. For example:
```
mysql> CREATE DATABASE onnx_model;
mysql> USE onnx_model;
```
Create a table to store the model. The table must have the three required columns to store the details for the model (chunk_id, model_object, and model_metadata). See ML_MODEL_IMPORT Overview. For example:
```
mysql> CREATE TABLE model_table (chunk_id INT AUTO_INCREMENT PRIMARY KEY, model_object LONGTEXT NOT NULL, model_metadata JSON DEFAULT NULL);
```

Use a LOAD DATA INFILE statement to load the model. If needed, load the model in batches of files depending on the size of the model. See LOAD DATA Statement to learn more. The following example loads the model in three separate files into the model_object column in the model_table table previously created:

mysql> LOAD DATA INFILE '/onnx_examples/x00' 
       INTO TABLE model_table 
       CHARACTER SET binary 
       FIELDS TERMINATED BY '\t' 
       LINES TERMINATED BY '\r' 
       (model_object);
Query OK, 1 row affected (34.96 sec)
Records: 1  Deleted: 0  Skipped: 0  Warnings: 0

mysql> LOAD DATA INFILE '/onnx_examples/x01' 
       INTO TABLE model_table 
       CHARACTER SET binary 
       FIELDS TERMINATED BY '\t' 
       LINES TERMINATED BY '\r' 
       (model_object);
Query OK, 1 row affected (32.74 sec)
Records: 1  Deleted: 0  Skipped: 0  Warnings: 0

mysql> LOAD DATA INFILE '/onnx_examples/x02' 
       INTO TABLE model_table 
       CHARACTER SET binary 
       FIELDS TERMINATED BY '\t' 
       LINES TERMINATED BY '\r' 
       (model_object);
Query OK, 1 row affected (11.90 sec)
Records: 1  Deleted: 0  Skipped: 0  Warnings: 0

After preparing the model, you can Defining Model Metadata.

Defining Model Metadata

After preparing the ONNX model (either as a string or table), define the metadata for the model as required. See Model Metadata and ONNX Model Metadata to learn more about requirements depending on the task type of the model.

To define the metadata for the ONNX model:

If including the column names for the model in the metadata, you have the option to set them into a JSON object as key-value pairs.
```
mysql> SET @variable = JSON_OBJECT("key","value"[,"key","value"] ...);
```
For example:
```
mysql> SET @column_names = JSON_OBJECT("0","f1", "1","f2", "2","f3");
```
Set the metadata for the model as required into a JSON object as key-value pairs. To learn more about metadata requirements, see ONNX Model Metadata. You can also include additional information that allows you to properly configure input tables and columns for generating predictions.
```
mysql> SET @variable = JSON_OBJECT("key","value"[,"key","value"] ...);
```
The following example shows how to define the metadata if you import the model as a string (pre-processed object). The predictions_name and prediction_probabilities_name variables are provided because it is a classification task. Including the column_names allows you to refer to the metadata to ensure that input tables for predictions have the same details. Otherwise an error generates.
```
mysql> SET @model_metadata = JSON_OBJECT('task','classification','onnx_outputs_info', 
       JSON_OBJECT('predictions_name','label','prediction_probabilities_name', 'probabilities'),'target_column_name','target',
       'train_table_name','mlcorpus.`classification_3_table`','column_names',@column_names,'notes','user notes for the model',
       'training_score',0.734,'training_time',100.34,'n_rows',1000,'n_columns',3,'algorithm_name','xgboost');
```
The following example shows how to define the metadata if you import the model from a table. The predictions_name and prediction_probabilities_name variables are provided because it is a classification task. After defining the metadata, update the metadata for the temporary table for the row that is chunk_id=1.
```
mysql> SET @model_metadata = JSON_OBJECT('task','classification','onnx_outputs_info', 
       JSON_OBJECT('predictions_name','label','prediction_probabilities_name', 'probabilities'),'target_column_name','target');

mysql> UPDATE mlcorpus.model_table SET model_metadata=@model_metadata WHERE chunk_id=1;
```

Depending on how you prepared the model, follow the appropriate steps to import the model:

Importing ONNX Model as a Pre-processed Object

If you followed the steps to Preparing to Import ONNX Model as a Pre-Processed Object, review the following steps to import the model as a pre-processed object.

To import the model as a pre-processed object:

Optionally, define the model handle for the imported model instead of automatically generating one. See Work with Model Handles.
```
mysql> SET @variable = 'model_handle';
```
For example:
```
mysql> SET @model = 'onnx_model_string';
```
Run ML_MODEL_IMPORT to import the model.
```
mysql> CALL sys.ML_MODEL_IMPORT (model_object, model_metadata, model_handle);
```
Since you are importing a pre-processed object, the model_object is defined by the string you previously set in the in either the @onnx_string_model_object or @onnx_table_model_object session variable. The model_metadata is defined by the metadata previously set in the @model_metadata session variable. The model_handle is defined by the session variable created for the model handle.
See the following example:
```
mysql> CALL sys.ML_MODEL_IMPORT(@onnx_string_model_object, @model_metadata, @model);
```

Confirm the model successfully loaded by querying the model_id and model_handle from the model catalog. Query the model by using the model handle previously created. Replace user1 with your own MySQL user name.

mysql> SELECT model_id, model_handle FROM ML_SCHEMA_user1.MODEL_CATALOG WHERE model_handle='onnx_model_string';
+----------+--------------------+
| model_id | model_handle       |
+----------+--------------------+
|       1  | onnx_model_table   |
+----------+--------------------+
1 row in set (0.0485 sec)

To load the model into MySQL AI so you can start using it with MySQL AI routines, run ML_MODEL_LOAD.
```
mysql> CALL sys.ML_MODEL_LOAD(model_handle, NULL);
```
For example:
```
mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
```

Importing ONNX Model as a Table

If you followed the steps to Preparing to Import ONNX Model as a Table, review the following steps to import the model as a table.

To import the model as a table:

Optionally, define the model handle for the imported model instead of automatically generating one. See Work with Model Handles.
```
mysql> SET @variable = 'model_handle';
```
For example:
```
mysql> SET @model = 'onnx_model_table';
```
Run ML_MODEL_IMPORT to import the model.
```
mysql> CALL sys.ML_MODEL_IMPORT (model_object, model_metadata, model_handle);
```
Since you are importing a table, the model_object is set to NULL. The model_metadata is defined by the schema name and table name storing the string for the ONNX model. The metadata for the model is stored in the table when following the steps to Defining Model Metadata. The model_handle is defined by the session variable created for the model handle.
See the following example:
```
mysql> CALL sys.ML_MODEL_IMPORT(NULL, JSON_OBJECT('schema', 'onnx_models', 'table', 'model_table'), @model);
```

Confirm the model successfully loaded by querying the model_id and model_handle from the model catalog. Query the model by using the model handle previously created. Replace user1 with your own MySQL user name.

mysql> SELECT model_id, model_handle FROM ML_SCHEMA_user1.MODEL_CATALOG WHERE model_handle='onnx_model_table';
+----------+--------------------+
| model_id | model_handle       |
+----------+--------------------+
|       2  | onnx_model_table   |
+----------+--------------------+
1 row in set (0.0485 sec)

To load the model into MySQL AI so you can start using it with MySQL AI routines, run ML_MODEL_LOAD.
```
mysql> CALL sys.ML_MODEL_LOAD(model_handle, NULL);
```
For example:
```
mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
```

ONNX Import Examples

Review the following additional examples for importing ONNX models.

In the following example, a ONNX model for classification is imported. Then, the model is used to generate predictions, a score, and prediction explainers for a dataset in MySQL AI.

mysql> SET @model = 'sklearn_pipeline_classification_3_onnx';
Query OK, 0 rows affected (0.0003 sec)

mysql> SET @model_metadata = JSON_OBJECT('task','classification', 'onnx_outputs_info', 
       JSON_OBJECT('predictions_name','label','prediction_probabilities_name', 'probabilities'));
Query OK, 0 rows affected (0.0003 sec)

mysql> CALL sys.ML_MODEL_IMPORT(@onnx_encode_sklearn_pipeline_classification_3, @model_metadata, @model);
Query OK, 0 rows affected (1.2438 sec)

mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
Query OK, 0 rows affected (0.5372 sec)

mysql> CALL sys.ML_PREDICT_TABLE('mlcorpus.classification_3_predict', @model, 'mlcorpus.predictions', NULL);
Query OK, 0 rows affected (0.8743 sec)

mysql> SELECT * FROM mlcorpus.predictions;
+-------------------+----+----+-----+------------+----------------------------------------------------------------------------------------------------------+
| _4aad19ca6e_pk_id | f1 | f2 | f3  | Prediction | ml_results                                                                                               |
+-------------------+----+----+-----+------------+----------------------------------------------------------------------------------------------------------+
|                 1 | a  | 20 | 1.2 |          0 | {"predictions": {"prediction": 0}, "probabilities": {"0": 0.5099999904632568, "1": 0.49000000953674316}} |
|                 2 | b  | 21 | 3.6 |          1 | {"predictions": {"prediction": 1}, "probabilities": {"0": 0.3199999928474426, "1": 0.6800000071525574}}  |
|                 3 | c  | 19 | 7.8 |          1 | {"predictions": {"prediction": 1}, "probabilities": {"0": 0.3199999928474426, "1": 0.6800000071525574}}  |
|                 4 | d  | 18 |   9 |          0 | {"predictions": {"prediction": 0}, "probabilities": {"0": 0.5199999809265137, "1": 0.47999998927116394}} |
|                 5 | e  | 17 | 3.6 |          1 | {"predictions": {"prediction": 1}, "probabilities": {"0": 0.3199999928474426, "1": 0.6800000071525574}}  |
+-------------------+----+----+-----+------------+----------------------------------------------------------------------------------------------------------+
5 rows in set (0.0005 sec)

mysql> CALL sys.ML_SCORE('mlcorpus.classification_3_table','target', @model, 'accuracy', @score, NULL);
Query OK, 0 rows affected (0.9573 sec)

mysql> SELECT @score;
+--------+
| @score |
+--------+
|      1 |
+--------+
1 row in set (0.0003 sec)

mysql> CALL sys.ML_EXPLAIN('mlcorpus.classification_3_table', 'target', @model, JSON_OBJECT('model_explainer', 'shap', 'prediction_explainer', 'shap'));
Query OK, 0 rows affected (10.1771 sec)

mysql> SELECT model_explanation FROM ML_SCHEMA_root.MODEL_CATALOG WHERE model_handle=@model;
+------------------------------------------------------+
| model_explanation                                    |
+------------------------------------------------------+
| {"shap": {"f1": 0.0928, "f2": 0.0007, "f3": 0.0039}} |
+------------------------------------------------------+
1 row in set (0.0005 sec)

mysql> CALL sys.ML_EXPLAIN_TABLE('mlcorpus.classification_3_predict', @model, 'mlcorpus.explanations_shap', JSON_OBJECT('prediction_explainer', 'shap'));
Query OK, 0 rows affected (7.6577 sec)

mysql> SELECT * FROM mlcorpus.explanations_shap;
+-------------------+----+----+-----+------------+----------------+-----------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| _4aad19ca6e_pk_id | f1 | f2 | f3  | Prediction | f1_attribution | f2_attribution  | f3_attribution | ml_results                                                                                                                                                                     |
+-------------------+----+----+-----+------------+----------------+-----------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|                 1 | a  | 20 | 1.2 |          0 |       0.116909 |     0.000591494 |    -0.00524929 | {"predictions": {"prediction": 0}, "attributions": {"f1_attribution": 0.11690924863020577, "f2_attribution": 0.0005914936463038284, "f3_attribution": -0.005249293645222988}}  |
|                 2 | b  | 21 | 3.6 |          1 |      0.0772133 |     -0.00110559 |     0.00219658 | {"predictions": {"prediction": 1}, "attributions": {"f1_attribution": 0.07721325159072877, "f2_attribution": -0.0011055856943130368, "f3_attribution": 0.002196577191352772}}  |
|                 3 | c  | 19 | 7.8 |          1 |      0.0781372 | 0.0000000913938 |    -0.00324671 | {"predictions": {"prediction": 1}, "attributions": {"f1_attribution": 0.07813718219598137, "f2_attribution": 9.139378859268632e-08, "f3_attribution": -0.0032467077175776238}} |
|                 4 | d  | 18 |   9 |          0 |       0.115209 |    -0.000592354 |     0.00639341 | {"predictions": {"prediction": 0}, "attributions": {"f1_attribution": 0.11520911753177646, "f2_attribution": -0.0005923539400101152, "f3_attribution": 0.006393408775329595}}  |
|                 5 | e  | 17 | 3.6 |          1 |      0.0767679 |      0.00110463 |     0.00219425 | {"predictions": {"prediction": 1}, "attributions": {"f1_attribution": 0.0767679293950399, "f2_attribution": 0.0011046340068181504, "f3_attribution": 0.002194248636563534}}    |
+-------------------+----+----+-----+------------+----------------+-----------------+----------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows in set (0.0005 sec)

In the following example, a ONNX model for regression is imported. Then, the model is used to generate predictions, a score, and prediction explainers for a dataset in MySQL AI.

mysql> SET @model = 'sklearn_pipeline_regression_2_onnx';
Query OK, 0 rows affected (0.0003 sec)

mysql> SET @model_metadata = JSON_OBJECT('task','regression', 'onnx_outputs_info',JSON_OBJECT('predictions_name','variable'));
Query OK, 0 rows affected (0.0003 sec)

mysql> CALL sys.ML_MODEL_IMPORT(@onnx_encode_sklearn_pipeline_regression_2, @model_metadata, @model);
Query OK, 0 rows affected (1.0652 sec)

mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
Query OK, 0 rows affected (0.5141 sec)

mysql> CALL sys.ML_PREDICT_TABLE('mlcorpus.regression_2_table', @model, 'mlcorpus.predictions', NULL);
Query OK, 0 rows affected (0.8902 sec)

mysql> SELECT * FROM mlcorpus.predictions;
+-------------------+----+----+-----+--------+------------+-----------------------------------------------------+
| _4aad19ca6e_pk_id | f1 | f2 | f3  | target | Prediction | ml_results                                          |
+-------------------+----+----+-----+--------+------------+-----------------------------------------------------+
|                 1 | a  | 20 | 1.2 |   22.4 |     22.262 | {"predictions": {"prediction": 22.262039184570312}} |
|                 2 | b  | 21 | 3.6 |   32.9 |    32.4861 | {"predictions": {"prediction": 32.486114501953125}} |
|                 3 | c  | 19 | 7.8 |   56.8 |    56.2482 | {"predictions": {"prediction": 56.24815368652344}}  |
|                 4 | d  | 18 |   9 |   31.8 |       31.8 | {"predictions": {"prediction": 31.80000114440918}}  |
|                 5 | e  | 17 | 3.6 |   56.4 |    55.9861 | {"predictions": {"prediction": 55.986114501953125}} |
+-------------------+----+----+-----+--------+------------+-----------------------------------------------------+
5 rows in set (0.0005 sec)

mysql> CALL sys.ML_SCORE('mlcorpus.regression_2_table','target', @model, 'r2', @score, NULL);
Query OK, 0 rows affected (0.8688 sec)

mysql> SELECT @score;
+--------------------+
| @score             |
+--------------------+
| 0.9993192553520203 |
+--------------------+
1 row in set (0.0003 sec)

mysql> CALL sys.ML_EXPLAIN('mlcorpus.regression_2_table', 'target', @model, JSON_OBJECT('model_explainer', 'partial_dependence', 'columns_to_explain', 
       JSON_ARRAY('f1'), 'prediction_explainer', 'shap'));
Query OK, 0 rows affected (9.9860 sec)

m
mysql> CALL sys.ML_EXPLAIN_TABLE('mlcorpus.regression_2_predict', @model, 'mlcorpus.explanations', JSON_OBJECT('prediction_explainer', 'shap'));
Query OK, 0 rows affected (8.2625 sec)

mysql> SELECT * FROM mlcorpus.explanations;
+-------------------+----+----+-----+------------+----------------+----------------+----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| _4aad19ca6e_pk_id | f1 | f2 | f3  | Prediction | f1_attribution | f2_attribution | f3_attribution | ml_results                                                                                                                                                                               |
+-------------------+----+----+-----+------------+----------------+----------------+----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|                 1 | a  | 20 | 1.2 |     22.262 |       -10.7595 |       -4.25162 |       -2.48331 | {"predictions": {"prediction": 22.262039184570312}, "attributions": {"f1_attribution": -10.759506797790523, "f2_attribution": -4.251623916625977, "f3_attribution": -2.483314704895024}} |
|                 2 | b  | 21 | 3.6 |    32.4861 |        2.33657 |       -8.50325 |        -1.1037 | {"predictions": {"prediction": 32.486114501953125}, "attributions": {"f1_attribution": 2.336572837829592, "f2_attribution": -8.50324745178223, "f3_attribution": -1.1036954879760748}}   |
|                 3 | c  | 19 | 7.8 |    56.2482 |        14.8361 |              0 |        1.65554 | {"predictions": {"prediction": 56.24815368652344}, "attributions": {"f1_attribution": 14.83612575531006, "f2_attribution": 0.0, "f3_attribution": 1.6555433273315412}}                   |
|                 4 | d  | 18 |   9 |       31.8 |       -15.2433 |        4.25162 |        3.03516 | {"predictions": {"prediction": 31.80000114440918}, "attributions": {"f1_attribution": -15.243269538879392, "f2_attribution": 4.251623725891111, "f3_attribution": 3.0351623535156236}}   |
|                 5 | e  | 17 | 3.6 |    55.9861 |        8.83008 |        8.50325 |        -1.1037 | {"predictions": {"prediction": 55.986114501953125}, "attributions": {"f1_attribution": 8.830077743530275, "f2_attribution": 8.50324764251709, "f3_attribution": -1.1036954879760756}}    |
+-------------------+----+----+-----+------------+----------------+----------------+----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
5 rows in set (0.0006 sec)

An example with task set to NULL.

mysql> SET @model = 'tensorflow_recsys_onnx';

mysql> CALL sys.ML_MODEL_IMPORT(@onnx_encode_tensorflow_recsys, NULL, @model);
Query OK, 0 rows affected (1.0037 sec)

mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
Query OK, 0 rows affected (0.5116 sec)

mysql> CALL sys.ML_PREDICT_TABLE('mlcorpus.recsys_predict', @model, 'mlcorpus.predictions', NULL);
Query OK, 0 rows affected (0.8271 sec)

mysql> SELECT * FROM mlcorpus.predictions;
+-------------------+---------+-------------+--------------------------+-----------------------------------------------------------+
| _4aad19ca6e_pk_id | user_id | movie_title | Prediction               | ml_results                                                |
+-------------------+---------+-------------+--------------------------+-----------------------------------------------------------+
|                 1 | a       | A           | {"output_1": ["0.7558"]} | {"predictions": {"prediction": {"output_1": ["0.7558"]}}} |
|                 2 | b       | B           | {"output_1": ["1.0443"]} | {"predictions": {"prediction": {"output_1": ["1.0443"]}}} |
|                 3 | c       | A           | {"output_1": ["0.8483"]} | {"predictions": {"prediction": {"output_1": ["0.8483"]}}} |
|                 4 | d       | B           | {"output_1": ["1.2986"]} | {"predictions": {"prediction": {"output_1": ["1.2986"]}}} |
|                 5 | e       | C           | {"output_1": ["1.1568"]} | {"predictions": {"prediction": {"output_1": ["1.1568"]}}} |
+-------------------+---------+-------------+--------------------------+-----------------------------------------------------------+
5 rows in set (0.0005 sec)

What's Next

Review how to Create a Machine Learning Model.
Review Machine Learning Use Cases to create machine learning models with sample datasets.