Using these ML models, you can train model instances in TrainDB. These model instances can be used to generate synopsis data or to estimate aggregate values in approximate query processing.
- TrainDB
- Python 3.x
- SDGym
- Using
pip: pip install pomegranate==0.14.6 sdgym==0.5.0 - Using
conda: conda install -c sdv-dev -c conda-forge sdgym
- Using
In $TRAINDB_PREFIX directory, run the following command:
$ cd $TRAINDB_PREFIX $ svn co https://github.com/traindb-project/traindb-model/trunk/modelsYou can run SQL-like statements via trsql in TrainDB.
$ bin/trsql sqlline> !connect jdbc:traindb:<dbms>://<host> Enter username for jdbc:traindb:<dbms>://localhost: <username> Enter password for jdbc:traindb:<dbms>://localhost: <password> 0: jdbc:traindb:<dbms>://<host>> CREATE MODEL tablegan TYPE SYNOPSIS LOCAL AS 'TableGAN' in '$TRAINDB_PREFIX/models/TableGAN.py'; No rows affected (0.255 seconds) 0: jdbc:traindb:<dbms>://<host>> TRAIN MODEL tablegan INSTANCE tgan ON <schema>.<table>(<column 1>, <column 2>, ...); epoch 1 step 50 tensor(1.1035, grad_fn=<SubBackward0>) tensor(0.7770, grad_fn=<NegBackward>) None epoch 1 step 100 tensor(0.8791, grad_fn=<SubBackward0>) tensor(0.9682, grad_fn=<NegBackward>) None ... 0: jdbc:traindb:<dbms>://<host>> CREATE SYNOPSIS <synopsis> FROM MODEL INSTANCE tgan LIMIT <# of rows to generate>; ... 0: jdbc:traindb:<dbms>://<host>> SELECT APPROXIMATE avg(<column>) FROM <schema>.<table>;