Machine Learning Models - Ocient Documentation

functionality enables you to create a machine learning model, rename the model, export the syntax for the model creation, retrain the model, execute a query against the model, and drop the model.

CREATE MLMODEL

Train a new machine learning model of type <model type> on the result set returned by the SQL SELECT statement. After the database creates the model, <model name> becomes a callable function in SQL SELECT statements. Syntax

SQL

CREATE [ OR REPLACE ] MLMODEL <model_name> TYPE <model_type>
    ON( <SQL SELECT statement> ) [options(<option_list>)]

model_name

Parameter	Data Type	Description
`model name`	`VARCHAR`	The name of the model to create.

model_type

Parameter	Data Type	Description
`model type`	`VARCHAR`	The type of machine learning model to create.

These models are supported. You can find full descriptions of each model in Regression Models, Classification Models, Clustering and Dimension Reduction Models, Ensemble Models, or Other Models.

SIMPLE LINEAR REGRESSION
MULTIPLE LINEAR REGRESSION
POLYNOMIAL REGRESSION
LINEAR COMBINATION REGRESSION
VECTOR AUTOREGRESSION
KMEANS
KNN (K Nearest Neighbors)
LOGISTIC REGRESSION
NAIVE BAYES
NONLINEAR REGRESSION
FEEDFORWARD NETWORK
PRINCIPAL COMPONENT ANALYSIS
LINEAR DISCRIMINANT ANALYSIS
SUPPORT VECTOR MACHINE
DECISION TREE
GAUSSIAN MIXTURE MODEL
ASSOCIATION RULES
GRADIENT BOOSTED TREES
REGRESSION TREE
BAGGING
BOOSTING
STACKING

option_list

Parameter	Data Type	Description
`option_list`	`VARCHAR`	The options for the specified machine learning model that is specified as a comma-separated list in the format: `<option name 1> -> <value 1>, <option name 2> -> <value 2>`, and so on. Names and values must be all enclosed in single quotes and are case sensitive with the exception that Boolean values can be `true`, `false`, `TRUE`, or `FALSE`. Refer to the respective model for the full options list. Example options list: `options(` `'yIntercept' -> '10',` `'metrics' -> 'true'` `)`

The SQL SELECT statement that serves as the basis for the model must return rows that fit the specified requirements of the model. For example, in multiple linear regression, the first N columns are the independent variables, and the last column is the dependent variable.

You cannot create a machine learning model with an existing schema and name combination.

Example Assume you created the mldata table that contains the data for the model. Then, you can create the my_model machine learning model based on that data.

SQL

CREATE MLMODEL my_model
TYPE SIMPLE LINEAR REGRESSION ON (
  SELECT
    x1,
    y
  FROM mldata
)
options(
  'yIntercept' -> '10',
  'metrics' -> 'true'
);

ALTER MLMODEL RENAME

Rename a machine learning model. Use the IF EXISTS clause to ignore any models that do not exist. Required Privileges To rename a view, you must have the ALTER MLMODEL privilege for the model. The System requires these privileges if this statement includes a change to the schema:

VIEW privilege on the current schema of the model
VIEW MLMODEL and CREATE MLMODEL privileges on the target schema (if the schema already exists)
CREATE MLMODEL privilege on the database (if the schema does not exist)

Syntax

SQL

ALTER MLMODEL [IF EXISTS] <model name> RENAME TO <new model name>

Parameter	Data Type	Description
`model name`	`VARCHAR`	The name of the model to rename.
`new model name`	`VARCHAR`	The new name of the model.

Example

SQL

ALTER MLMODEL my_model RENAME TO my_model_slr;

DROP MLMODEL

Drop a machine learning model. Use the IF EXISTS clause to ignore any models that do not exist. Syntax

SQL

DROP MLMODEL [IF EXISTS] model_name [, ...]

Parameter	Data Type	Description
`model name`	`VARCHAR`	The name of the model to drop. You can drop multiple models by specifying additional names and separating each with commas.

Example Drop the machine learning model my_model.

SQL

DROP MLMODEL my_model;

Drop multiple machine learning models.

SQL

DROP MLMODEL my_model1, my_model2;

EXPORT MLMODEL

Return the SQL statement that can recreate the machine learning model. Syntax

SQL

EXPORT MLMODEL <model name>

Parameter	Data Type	Description
`model name`	`VARCHAR`	The name of the model to create.

Example

SQL

EXPORT MLMODEL my_model;

Output

SQL

export
---------------------------------------------
CREATE MLMODEL "my_schema"."my_model" TYPE simple linear regression ON(select x, y from mldemo.slr) OPTIONS('metrics' -> 'false');

Fetched 1 row

The output SQL statement includes the schema explicitly.

REFRESH MLMODEL

Retrain a machine learning model without changing any model options. Syntax

SQL

REFRESH MLMODEL <model name>

Parameter	Data Type	Description
`model name`	`VARCHAR`	The name of the model to retrain.

Example

SQL

REFRESH MLMODEL my_model;

Machine Learning in Ocient Machine Learning Model Functions Regression Models Classification Models Clustering and Dimension Reduction Models Other Models

​CREATE MLMODEL

​model_name

​model_type

​option_list

​ALTER MLMODEL RENAME

​DROP MLMODEL

​EXPORT MLMODEL

​REFRESH MLMODEL

​Related Links

CREATE MLMODEL

model_name

model_type

option_list

ALTER MLMODEL RENAME

DROP MLMODEL

EXPORT MLMODEL

REFRESH MLMODEL

Related Links