abacusai.model_version

Module Contents

Classes

ModelVersion

A version of a model

class abacusai.model_version.ModelVersion(client, modelVersion=None, status=None, modelId=None, modelConfig=None, modelPredictionConfig=None, trainingStartedAt=None, trainingCompletedAt=None, datasetVersions=None, featureGroupVersions=None, error=None, pendingDeploymentIds=None, failedDeploymentIds=None, cpuSize=None, memory=None, automlComplete=None, trainingFeatureGroupIds=None, deployableAlgorithms=None, bestAlgorithm=None, defaultAlgorithm=None, codeSource={})

Bases: abacusai.return_class.AbstractApiClass

A version of a model

Parameters:
  • client (ApiClient) – An authenticated API Client instance

  • modelVersion (str) – The unique identifier of a model version.

  • status (str) – The current status of the model.

  • modelId (str) – A reference to the model this version belongs to.

  • modelConfig (dict) – The training config options used to train this model.

  • modelPredictionConfig (dict) – The prediction config options for the model.

  • trainingStartedAt (str) – The start time and date of the training process.

  • trainingCompletedAt (str) – The end time and date of the training process.

  • datasetVersions (list of unique string identifiers) – Comma separated list of Dataset version IDs that this refresh pipeline run is monitoring.

  • featureGroupVersions (list) –

  • error (str) – Relevant error if the status is FAILED.

  • pendingDeploymentIds (list) – List of deployment IDs where deployment is pending.

  • failedDeploymentIds (list) – List of failed deployment IDs.

  • cpuSize (str) – Cpu size specified for the python model training.

  • memory (int) – Memory in GB specified for the python model training.

  • automlComplete (bool) – If true, all algorithms have compelted training

  • trainingFeatureGroupIds (list of unique string identifiers) – The unique identifiers of the feature group used as the inputs during training to create this ModelVersion.

  • deployableAlgorithms (dict) – List of deployable algorithms

  • bestAlgorithm (dict) – Best performing algorithm

  • defaultAlgorithm (dict) – Default algorithm that the user has selected

  • codeSource (CodeSource) – If a python model, information on where the source code

__repr__()

Return repr(self).

to_dict()

Get a dict representation of the parameters in this class

Returns:

The dict value representation of the class parameters

Return type:

dict

describe_train_test_data_split_feature_group_version()

Get the train and test data split for a trained model by model_version. Only supported for models with custom algorithms.

Parameters:

model_version (str) – The unique version ID of the model version

Returns:

The feature group version containing the training data and folds information.

Return type:

FeatureGroupVersion

delete()

Deletes the specified model version. Model Versions which are currently used in deployments cannot be deleted.

Parameters:

model_version (str) – The ID of the model version to delete.

export_model_artifact_as_feature_group(table_name, artifact_type)

Exports metric artifact data for a model as a feature group.

Parameters:
  • table_name (str) – The name of the feature group table to create.

  • artifact_type (str) – An EvalArtifact enum of which artifact to export.

Returns:

The created feature group.

Return type:

FeatureGroup

refresh()

Calls describe and refreshes the current object’s fields

Returns:

The current object

Return type:

ModelVersion

describe()

Retrieves a full description of the specified model version

Parameters:

model_version (str) – The unique version ID of the model version

Returns:

A model version.

Return type:

ModelVersion

get_training_data_logs()

Retrieves the data preparation logs during model training.

Parameters:

model_version (str) – The unique version ID of the model version

Returns:

A list of logs.

Return type:

DataPrepLogs

get_training_logs(stdout=False, stderr=False)

Returns training logs for the model.

Parameters:
  • stdout (bool) – Set True to get info logs

  • stderr (bool) – Set True to get error logs

Returns:

A function logs.

Return type:

FunctionLogs

wait_for_training(timeout=None)

A waiting call until model gets trained.

Parameters:

timeout (int, optional) – The waiting time given to the call to finish, if it doesn’t finish by the allocated time, the call is said to be timed out.

wait_for_full_automl(timeout=None)

A waiting call until full AutoML cycle is completed.

Parameters:

timeout (int, optional) – The waiting time given to the call to finish, if it doesn’t finish by the allocated time, the call is said to be timed out.

get_status()

Gets the status of the model version under training.

Returns:

A string describing the status of a model training (pending, complete, etc.).

Return type:

str

get_train_test_feature_group_as_pandas()

Get the model train test data split feature group of the model version as pandas data frame.

Returns:

A pandas dataframe for the training data with fold column.

Return type:

pandas.Dataframe