Model Maintenance
Model maintenance consists of viewing, altering, and deleting models.
Viewing Models
When IntegratedML performs training or validation, this process is known as a “training run” or a “validation run.”
IntegratedML provides the following views, within the INFORMATION_SCHEMA class, that can be used to query information about models, trained models, training runs, and validation runs:
ML_MODELS
This view returns one row for each model definition.
INFORMATION_SCHEMA.ML_MODELS contains the following columns:
| Column Name | Description |
|---|---|
| CREATE_TIME_STAMP | Time when the model definition was created (UTC) |
| DEFAULT_SETTINGS | Default settings the model definition’s provider uses |
| DEFAULT_TRAINED_MODEL_NAME | Default trained model name, if one has been trained |
| DEFAULT_TRAINING_QUERY | The FROM clause from the CREATE MODEL statement, if one was used |
| DESCRIPTION | Description of model definition |
| MODEL_NAME | Name of the model definition |
| PREDICTING_COLUMN_NAME | Name of the label column |
| PREDICTING_COLUMN_TYPE | Type of the label column |
| WITH_COLUMNS | Names of the feature columns |
See Creating Model Definitions for information about model definitions.
ML_TRAINED_MODELS
This view returns one row for each trained model.
INFORMATION_SCHEMA.ML_TRAINED_MODELS contains the following columns:
| Column Name | Description |
|---|---|
| MODEL_INFO | Model information |
| MODEL_NAME | Name of the model definition |
| MODEL_TYPE | The model type (classification, regression, or time series) |
| PROVIDER | Provider used for training |
| TRAINED_MODEL_NAME | Name of the trained model |
| TRAINED_TIMESTAMP | Time when the trained model was created (UTC) |
See Training Models for information about trained models.
See Providers for information about providers.
ML_TRAINING_RUNS
This view returns one row for each training run.
INFORMATION_SCHEMA.ML_TRAINING_RUNS contains the following columns:
| Column Name | Description |
|---|---|
| COMPLETED_TIMESTAMP | Time when the training run completed (UTC) |
| LOG | Training log output from the provider |
| ML_CONFIGURATION_NAME | Name of the ML configuration used for training |
| MODEL_NAME | Name of the model definition |
| PROVIDER | Name of the provider used for training |
| RUN_STATUS | Status of training run |
| SETTINGS | Any settings passed by a USING clause for the training run |
| START_TIMESTAMP | Time when the training run started (UTC) |
| STATUS_CODE | Training error (if encountered) |
| TRAINING_DURATION | Duration of training (in seconds) |
| TRAINING_RUN_NAME | Name of the training run |
| TRAINING_RUN_QUERY | Query used to source data from feature and label columns for training |
See Training Models for information about training runs.
ML_VALIDATION_RUNS
This view returns one row for each validation run.
INFORMATION_SCHEMA.ML_VALIDATION_RUNS contains the following columns:
| Column Name | Description |
|---|---|
| COMPLETED_TIMESTAMP | Time when the validation run completed (UTC) |
| LOG | Validation log output |
| MODEL_NAME | Name of the model definition |
| RUN_STATUS | Validation status |
| SETTINGS | Validation run settings |
| START_TIMESTAMP | Time when the validation run started (UTC) |
| STATUS_CODE | Validation error (if encountered) |
| TRAINED_MODEL_NAME | Name of the trained model being validated |
| VALIDATION_DURATION | Validation duration (in seconds) |
| VALIDATION_RUN_NAME | Name of the validation run |
| VALIDATION_RUN_QUERY | Full query for dataset specified by FROM |
See Validating Models for information about validation runs.
ML_VALIDATION_METRICS
This view returns one row for each validation metric of each validation run.
INFORMATION_SCHEMA.ML_VALIDATION_METRICS contains the following columns:
| Column Name | Description |
|---|---|
| METRIC_NAME | Validation metric name |
| METRIC_VALUE | Validation metric value |
| MODEL_NAME | Model name |
| TARGET_VALUE | Target value for validation metric |
| TRAINED_MODEL_NAME | Name of the trained model for this run |
| VALIDATION_RUN_NAME | Name of the validation run |
For information about the validation metrics that populate METRIC_NAME and METRIC_VALUE, see the InterSystems SQL Reference.
Altering Models
You can modify a model by using the ALTER MODEL statement.
The ALTER MODEL statement has the following syntax:
ALTER MODEL model-name alter-action
Where alter-action can be one of the following:
-
PURGE ALL
-
PURGE integer DAYS
-
DEFAULT preferred-model-name
This example uses the PURGE clause to delete all training run and validation run data associated with the model WillLoanDefault:
ALTER MODEL WillLoanDefault PURGE ALLThis example uses the PURGE clause to delete training run and validation run data associated with the model WillLoanDefault that is older than 7 days old:
ALTER MODEL WillLoanDefault PURGE 7 DAYSYou can confirm that your alter statements succeeded by querying the views listed in Viewing Models.
For complete information about the ALTER MODEL command, see the refernece.
Deleting Models
You can delete a model by using the DROP MODEL statement.
The DROP MODEL statement has the following syntax:
DROP MODEL model-name
DROP MODEL deletes all training runs and validation runs for the associated model.
You can confirm that your model has been deleted by querying the INFORMATION_SCHEMA.ML_MODELS view.
For complete information about the DROP MODEL command, see the InterSystems SQL Reference.
