AI Tables in PostgreSQL

Now, you can train machine learning models straight from the database by using MindsDB and PostgreSQL.

MindsDB-Postgres

Prerequisite

You will need MindsDB version >= 2.3.0 and PostgreSQL installed:

Configuration

Default configuration

MindsDB will try to use the default configuration(hosts, ports, usernames) for each of the database integrations. If you want to extend that or you are using different parameters create a new config.json file.

The avaiable configuration options are:

api['http] -- This key is used for starting the MindsDB http server by providing:
- host(default 127.0.0.1) - The mindsdb server address.
- port(default 47334) - The mindsdb server port.
api['mysql'] -- This key is used for database integrations that works through MySQL protocol. The required keys are:
- user(default root).
- password(default empty).
- host(default 127.0.0.1).
- port(default 47335).
integrations['default_postgres'] -- This key specifies the integration type in this case default_postgres. The required keys are:
- user(default postgres) - The Postgres user name.
- host(default 127.0.0.1) - Connect to the PostgreSQL server on the given host.
- password - The password of the Postgres account.
- type - Integration type(mariadb, postgresql, mysql, clickhouse, mongodb).
- port(default 5432) - The TCP/IP port number to use for the connection.
log['level'] -- The logging configuration(not required):
- console - "INFO", "DEBUG", "ERROR".
- file - Location of the log file.
storage_dir -- The directory where mindsdb will store models and configuration.

Configuration example

{
    "api": {
        "http": {
            "host": "0.0.0.0",
            "port": "47334"
        },
        "mysql": {
            "host": "127.0.0.1",
            "password": "",
            "port": "47335",
            "user": "root"
        }
    },
    "config_version": "1.3",
    "debug": true,
    "integrations": {
        "default_postgres": {
            "database": "postgres",
            "enabled": true,
            "host": "localhost",
            "password": "postgres",
            "port": 5432,
            "type": "postgres",
            "user": "postgres"
        }
    },
    "log": {
        "level": {
            "console": "DEBUG",
            "file": "INFO"
        }
    },
    "storage_dir": "/storage"
}

Install PostgreSQL foreign data wrapper for MySQL

The Foreign Data Wrapper (mysql_fwd) can be installed from the EnterpriseDB repo.

Start MindsDB

To start mindsdb run following command:

python3 -m mindsdb --api=mysql --config=config.json

The --api parameter specifies the type of API to use in this case mysql. The --config specifies the location of the configuration file.

Train new model

To train a new model, insert a new record inside the mindsdb.predictors table as:

INSERT INTO mindsdb.predictors(name, predict, select_data_query, training_options) VALUES ('us_consumption', 'consumption', 'SELECT * FROM us_consumption', '{"timeseries_settings":{"order_by": ["t"], "window":20}}');

name (string) -- The name of the predictor.
predict (string) -- The feature you want to predict, in this example consumption. To predict multiple featurs include a comma separated string e.g 'consumption,income'.
select_data_query (string) -- The SELECT query that will ingest the data to train the model.
training_options (JSON as comma separated string) -- optional value that contains additional training parameters. For a full list of the parameters check the PredictorInterface. If you are using timeseries data check the Timeseries settings.

Query the model

To query the model and get the predictions SELECT the target variable, confidence and explanation for that prediction.

SELECT
   consumption AS predicted,
   consumption_confidence AS confidence,
   consumption_explain AS info 
FROM
   mindsdb.us_consumption 
WHERE
   income = 1.182497938 
   AND production = 5.854555956  
   AND savings = 3.183292657 
   AND unemployment = 0.1

You should get a similar response from MindsDB as:

consumption	predicted	info
1.4979682087292199	0.9475	Check JSON bellow

{
    "predicted_value": 1.4979682087292199,
    "confidence": 0.9475,
    "prediction_quality": "very confident",
    "confidence_interval": [1.025658879956537, 1.9702775375019028],
    "important_missing_information": [],
    "confidence_composition": {},
    "extra_insights": {
        "if_missing": [{
            "income": 0.6966906986877563
        }, {
            "production": 2.5382917051924445
        }, {
            "savings": 1.169812868271305
        }, {
            "unemployment": 1.3
            443338862946717
        }]
    }
}

Delete the model

To delete the predictor that you have previously created, you need to delete it from mindsdb.predictors table. The name should be equal to name added in the INSERT statment while creating the predictor, e.g:

DELETE FROM mindsdb.predictors WHERE name='us_consumption'

Train and predict multiple features

You can train a model that will predict multiple features by adding a comma separated features values in the predict column. e.g to predict the consumption and a income:

INSERT INTO
   mindsdb.predictors(name, predict, select_data_query, training_options) 
VALUES
   ('us_consumption', 'consumption, income', 'SELECT * FROM us_consumption', "option,value"});

And query it using the select_data_query:

SELECT
   consumption AS predicted,
FROM
   mindsdb.used_cars_model 
WHERE
    select_data_query='SELECT income FROM us_consumption';

The requirements to query with select_data_query are:

It must be a valid SQL statement
It must return columns with names the same as predictor fields.

To get additional information follow the AiTables in PostgreSQL tutorail.