abacusai.batch_prediction

Classes

`BatchPredictionArgs`	An abstract class for Batch Prediction args specific to problem type.
`BatchPredictionVersion`	Batch Prediction Version
`PredictionFeatureGroup`	Batch Input Feature Group
`PredictionInput`	Batch inputs
`RefreshSchedule`	A refresh schedule for an object. Defines when the next version of the object will be created
`AbstractApiClass`
`BatchPrediction`	Make batch predictions.

Module Contents

class abacusai.batch_prediction.BatchPredictionArgs

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for Batch Prediction args specific to problem type.

_support_kwargs: bool

kwargs: dict

problem_type: abacusai.api_class.enums.ProblemType

classmethod _get_builder()

class abacusai.batch_prediction.BatchPredictionVersion(client, batchPredictionVersion=None, batchPredictionId=None, status=None, driftMonitorStatus=None, deploymentId=None, modelId=None, modelVersion=None, predictionsStartedAt=None, predictionsCompletedAt=None, databaseOutputError=None, totalPredictions=None, failedPredictions=None, databaseConnectorId=None, databaseOutputConfiguration=None, fileConnectorOutputLocation=None, fileOutputFormat=None, connectorType=None, legacyInputLocation=None, error=None, driftMonitorError=None, monitorWarnings=None, csvInputPrefix=None, csvPredictionPrefix=None, csvExplanationsPrefix=None, databaseOutputTotalWrites=None, databaseOutputFailedWrites=None, outputIncludesMetadata=None, resultInputColumns=None, modelMonitorVersion=None, algoName=None, algorithm=None, outputFeatureGroupId=None, outputFeatureGroupVersion=None, outputFeatureGroupTableName=None, batchPredictionWarnings=None, bpAcrossVersionsMonitorVersion=None, batchPredictionArgsType=None, batchInputs={}, inputFeatureGroups={}, globalPredictionArgs={}, batchPredictionArgs={})

Bases: abacusai.return_class.AbstractApiClass

Batch Prediction Version

Parameters:

client (ApiClient) – An authenticated API Client instance
batchPredictionVersion (str) – The unique identifier of the batch prediction version
batchPredictionId (str) – The unique identifier of the batch prediction
status (str) – The current status of the batch prediction
driftMonitorStatus (str) – The status of the drift monitor for this batch prediction version
deploymentId (str) – The deployment used to make the predictions
modelId (str) – The model used to make the predictions
modelVersion (str) – The model version used to make the predictions
predictionsStartedAt (str) – Predictions start date and time
predictionsCompletedAt (str) – Predictions completion date and time
databaseOutputError (bool) – If true, there were errors reported by the database connector while writing
totalPredictions (int) – Number of predictions performed in this batch prediction job
failedPredictions (int) – Number of predictions that failed
databaseConnectorId (str) – The database connector to write the results to
databaseOutputConfiguration (dict) – Contains information about where the batch predictions are written to
fileConnectorOutputLocation (str) – Contains information about where the batch predictions are written to
fileOutputFormat (str) – The format of the batch prediction output (CSV or JSON)
connectorType (str) – Null if writing to internal console, else FEATURE_GROUP | FILE_CONNECTOR | DATABASE_CONNECTOR
legacyInputLocation (str) – The location of the input data
error (str) – Relevant error if the status is FAILED
driftMonitorError (str) – Error message for the drift monitor of this batch predcition
monitorWarnings (str) – Relevant warning if there are issues found in drift or data integrity
csvInputPrefix (str) – A prefix to prepend to the input columns, only applies when output format is CSV
csvPredictionPrefix (str) – A prefix to prepend to the prediction columns, only applies when output format is CSV
csvExplanationsPrefix (str) – A prefix to prepend to the explanation columns, only applies when output format is CSV
databaseOutputTotalWrites (int) – The total number of rows attempted to write (may be less than total_predictions if write mode is UPSERT and multiple rows share the same ID)
databaseOutputFailedWrites (int) – The number of failed writes to the Database Connector
outputIncludesMetadata (bool) – If true, output will contain columns including prediction start time, batch prediction version, and model version
resultInputColumns (list[str]) – If present, will limit result files or feature groups to only include columns present in this list
modelMonitorVersion (str) – The version of the model monitor
algoName (str) – The name of the algorithm used to train the model
algorithm (str) – The algorithm that is currently deployed.
outputFeatureGroupId (str) – The Batch Prediction output feature group ID if applicable
outputFeatureGroupVersion (str) – The Batch Prediction output feature group version if applicable
outputFeatureGroupTableName (str) – The Batch Prediction output feature group name if applicable
batchPredictionWarnings (str) – Relevant warnings if any issues are found
bpAcrossVersionsMonitorVersion (str) – The version of the batch prediction across versions monitor
batchPredictionArgsType (str) – The type of the batch prediction args
batchInputs (PredictionInput) – Inputs to the batch prediction
inputFeatureGroups (PredictionFeatureGroup) – List of prediction feature groups
globalPredictionArgs (BatchPredictionArgs)
batchPredictionArgs (BatchPredictionArgs) – Argument(s) passed to every prediction call

__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

download_batch_prediction_result_chunk(offset=0, chunk_size=10485760)

Returns a stream containing the batch prediction results.

Parameters:

offset (int) – The offset to read from.
chunk_size (int) – The maximum amount of data to read.

get_batch_prediction_connector_errors()

Returns a stream containing the batch prediction database connection write errors, if any writes failed for the specified batch prediction job.

Parameters:: batch_prediction_version (str) – Unique string identifier of the batch prediction job to get the errors for.

refresh()

Calls describe and refreshes the current object’s fields

Returns:: The current object
Return type:: BatchPredictionVersion

describe()

Describes a Batch Prediction Version.

Parameters:: batch_prediction_version (str) – Unique string identifier of the Batch Prediction Version.
Returns:: The Batch Prediction Version.
Return type:: BatchPredictionVersion

get_logs()

Retrieves the batch prediction logs.

Parameters:: batch_prediction_version (str) – The unique version ID of the batch prediction version.
Returns:: The logs for the specified batch prediction version.
Return type:: BatchPredictionVersionLogs

download_result_to_file(file)

Downloads the batch prediction version in a local file.

Parameters:: file (file object) – A file object opened in a binary mode e.g., file=open(‘/tmp/output’, ‘wb’).

wait_for_predictions(timeout=86400)

A waiting call until batch prediction version is ready.

Parameters:: timeout (int) – 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_drift_monitor(timeout=86400)

A waiting call until batch prediction drift monitor calculations are ready.

Parameters:: timeout (int) – 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(drift_monitor_status=False)

Gets the status of the batch prediction version.

Returns:: A string describing the status of the batch prediction version, for e.g., pending, complete, etc.
Return type:: str
Parameters:: drift_monitor_status (bool)

load_results_as_pandas()

Loads the output feature groups into a python pandas dataframe.

Returns:: A pandas dataframe with annotations and text_snippet columns.
Return type:: DataFrame

class abacusai.batch_prediction.PredictionFeatureGroup(client, featureGroupId=None, featureGroupVersion=None, datasetType=None, default=None, required=None)

Bases: abacusai.return_class.AbstractApiClass

Batch Input Feature Group

Parameters:

client (ApiClient) – An authenticated API Client instance
featureGroupId (str) – The unique identifier of the feature group
featureGroupVersion (str) – The unique identifier of the feature group version used for predictions
datasetType (str) – dataset type
default (bool) – If true, this feature group is the default feature group in the model
required (bool) – If true, this feature group is required for the batch prediction

__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

class abacusai.batch_prediction.PredictionInput(client, featureGroupDatasetIds=None, datasetIdRemap=None, featureGroups={}, datasets={})

Bases: abacusai.return_class.AbstractApiClass

Batch inputs

Parameters:

client (ApiClient) – An authenticated API Client instance
featureGroupDatasetIds (list) – The list of dataset IDs to use as input
datasetIdRemap (dict) – Replacement datasets to swap as prediction input
featureGroups (PredictionFeatureGroup) – List of prediction feature groups
datasets (PredictionDataset) – List of prediction datasets

__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

class abacusai.batch_prediction.RefreshSchedule(client, refreshPolicyId=None, nextRunTime=None, cron=None, refreshType=None, error=None)

Bases: abacusai.return_class.AbstractApiClass

A refresh schedule for an object. Defines when the next version of the object will be created

Parameters:

client (ApiClient) – An authenticated API Client instance
refreshPolicyId (str) – The unique identifier of the refresh policy
nextRunTime (str) – The next run time of the refresh policy. If null, the policy is paused.
cron (str) – A cron-style string that describes the when this refresh policy is to be executed in UTC
refreshType (str) – The type of refresh that will be run
error (str) – An error message for the last pipeline run of a policy

__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

class abacusai.batch_prediction.AbstractApiClass(client, id)

__eq__(other): Return self==value.

_get_attribute_as_dict(attribute)

class abacusai.batch_prediction.BatchPrediction(client, batchPredictionId=None, createdAt=None, name=None, deploymentId=None, fileConnectorOutputLocation=None, databaseConnectorId=None, databaseOutputConfiguration=None, fileOutputFormat=None, connectorType=None, legacyInputLocation=None, outputFeatureGroupId=None, featureGroupTableName=None, outputFeatureGroupTableName=None, summaryFeatureGroupTableName=None, csvInputPrefix=None, csvPredictionPrefix=None, csvExplanationsPrefix=None, outputIncludesMetadata=None, resultInputColumns=None, modelMonitorId=None, modelVersion=None, bpAcrossVersionsMonitorId=None, algorithm=None, batchPredictionArgsType=None, batchInputs={}, latestBatchPredictionVersion={}, refreshSchedules={}, inputFeatureGroups={}, globalPredictionArgs={}, batchPredictionArgs={})

Bases: abacusai.return_class.AbstractApiClass

Make batch predictions.

Parameters:

client (ApiClient) – An authenticated API Client instance
batchPredictionId (str) – The unique identifier of the batch prediction request.
createdAt (str) – When the batch prediction was created, in ISO-8601 format.
name (str) – Name given to the batch prediction object.
deploymentId (str) – The deployment used to make the predictions.
fileConnectorOutputLocation (str) – Contains information about where the batch predictions are written to.
databaseConnectorId (str) – The database connector to write the results to.
databaseOutputConfiguration (dict) – Contains information about where the batch predictions are written to.
fileOutputFormat (str) – The format of the batch prediction output (CSV or JSON).
connectorType (str) – Null if writing to internal console, else FEATURE_GROUP | FILE_CONNECTOR | DATABASE_CONNECTOR.
legacyInputLocation (str) – The location of the input data.
outputFeatureGroupId (str) – The Batch Prediction output feature group ID if applicable
featureGroupTableName (str) – The table name of the Batch Prediction output feature group.
outputFeatureGroupTableName (str) – The table name of the Batch Prediction output feature group.
summaryFeatureGroupTableName (str) – The table name of the metrics summary feature group output by Batch Prediction.
csvInputPrefix (str) – A prefix to prepend to the input columns, only applies when output format is CSV.
csvPredictionPrefix (str) – A prefix to prepend to the prediction columns, only applies when output format is CSV.
csvExplanationsPrefix (str) – A prefix to prepend to the explanation columns, only applies when output format is CSV.
outputIncludesMetadata (bool) – If true, output will contain columns including prediction start time, batch prediction version, and model version.
resultInputColumns (list) – If present, will limit result files or feature groups to only include columns present in this list.
modelMonitorId (str) – The model monitor for this batch prediction.
modelVersion (str) – The model instance used in the deployment for the batch prediction.
bpAcrossVersionsMonitorId (str) – The model monitor for this batch prediction across versions.
algorithm (str) – The algorithm that is currently deployed.
batchPredictionArgsType (str) – The type of batch prediction arguments used for this batch prediction.
batchInputs (PredictionInput) – Inputs to the batch prediction.
latestBatchPredictionVersion (BatchPredictionVersion) – The latest batch prediction version.
refreshSchedules (RefreshSchedule) – List of refresh schedules that dictate the next time the batch prediction will be run.
inputFeatureGroups (PredictionFeatureGroup) – List of prediction feature groups.
globalPredictionArgs (BatchPredictionArgs)
batchPredictionArgs (BatchPredictionArgs) – Argument(s) passed to every prediction call.

__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

start()

Creates a new batch prediction version job for a given batch prediction job description.

Parameters:: batch_prediction_id (str) – The unique identifier of the batch prediction to create a new version of.
Returns:: The batch prediction version started by this method call.
Return type:: BatchPredictionVersion

refresh()

Calls describe and refreshes the current object’s fields

Returns:: The current object
Return type:: BatchPrediction

describe()

Describe the batch prediction.

Parameters:: batch_prediction_id (str) – The unique identifier associated with the batch prediction.
Returns:: The batch prediction description.
Return type:: BatchPrediction

list_versions(limit=100, start_after_version=None)

Retrieves a list of versions of a given batch prediction

Parameters:

limit (int) – Number of versions to list.
start_after_version (str) – Version to start after.

Returns:

List of batch prediction versions.

Return type:

list[BatchPredictionVersion]

update(deployment_id=None, global_prediction_args=None, batch_prediction_args=None, explanations=None, output_format=None, csv_input_prefix=None, csv_prediction_prefix=None, csv_explanations_prefix=None, output_includes_metadata=None, result_input_columns=None, name=None)

Update a batch prediction job description.

Parameters:

deployment_id (str) – Unique identifier of the deployment.
batch_prediction_args (BatchPredictionArgs) – Batch Prediction args specific to problem type.
output_format (str) – If specified, sets the format of the batch prediction output (CSV or JSON).
csv_input_prefix (str) – Prefix to prepend to the input columns, only applies when output format is CSV.
csv_prediction_prefix (str) – Prefix to prepend to the prediction columns, only applies when output format is CSV.
csv_explanations_prefix (str) – Prefix to prepend to the explanation columns, only applies when output format is CSV.
output_includes_metadata (bool) – If True, output will contain columns including prediction start time, batch prediction version, and model version.
result_input_columns (list) – If present, will limit result files or feature groups to only include columns present in this list.
name (str) – If present, will rename the batch prediction.
global_prediction_args (Union[dict, abacusai.api_class.BatchPredictionArgs])
explanations (bool)

Returns:

The batch prediction.

Return type:

BatchPrediction

set_file_connector_output(output_format=None, output_location=None)

Updates the file connector output configuration of the batch prediction

Parameters:

output_format (str) – The format of the batch prediction output (CSV or JSON). If not specified, the default format will be used.
output_location (str) – The location to write the prediction results. If not specified, results will be stored in Abacus.AI.

Returns:

The batch prediction description.

Return type:

BatchPrediction

set_database_connector_output(database_connector_id=None, database_output_config=None)

Updates the database connector output configuration of the batch prediction

Parameters:

database_connector_id (str) – Unique string identifier of an Database Connection to write predictions to.
database_output_config (dict) – Key-value pair of columns/values to write to the database connector.

Returns:

Description of the batch prediction.

Return type:

BatchPrediction

set_feature_group_output(table_name)

Creates a feature group and sets it as the batch prediction output.

Parameters:: table_name (str) – Name of the feature group table to create.
Returns:: Batch prediction after the output has been applied.
Return type:: BatchPrediction

set_output_to_console()

Sets the batch prediction output to the console, clearing both the file connector and database connector configurations.

Parameters:: batch_prediction_id (str) – The unique identifier of the batch prediction.
Returns:: The batch prediction description.
Return type:: BatchPrediction

set_feature_group(feature_group_type, feature_group_id=None)

Sets the batch prediction input feature group.

Parameters:

feature_group_type (str) – Enum string representing the feature group type to set. The type is based on the use case under which the feature group is being created (e.g. Catalog Attributes for personalized recommendation use case).
feature_group_id (str) – Unique identifier of the feature group to set as input to the batch prediction.

Returns:

Description of the batch prediction.

Return type:

BatchPrediction

set_dataset_remap(dataset_id_remap)

For the purpose of this batch prediction, will swap out datasets in the training feature groups

Parameters:: dataset_id_remap (dict) – Key/value pairs of dataset ids to be replaced during the batch prediction.
Returns:: Batch prediction object.
Return type:: BatchPrediction

delete()

Deletes a batch prediction and associated data, such as associated monitors.

Parameters:: batch_prediction_id (str) – Unique string identifier of the batch prediction.

wait_for_predictions(timeout=86400)

A waiting call until batch predictions are ready.

Parameters:: timeout (int) – 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_drift_monitor(timeout=86400)

A waiting call until batch prediction drift monitor calculations are ready.

Parameters:: timeout (int) – 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 latest batch prediction version.

Returns:: A string describing the status of the latest batch prediction version e.g., pending, complete, etc.
Return type:: str

create_refresh_policy(cron)

To create a refresh policy for a batch prediction.

Parameters:: cron (str) – A cron style string to set the refresh time.
Returns:: The refresh policy object.
Return type:: RefreshPolicy

list_refresh_policies()

Gets the refresh policies in a list.

Returns:: A list of refresh policy objects.
Return type:: List[RefreshPolicy]

describe_output_feature_group()

Gets the results feature group for this batch prediction

Returns:: A feature group object.
Return type:: FeatureGroup

load_results_as_pandas()

Loads the output feature groups into a python pandas dataframe.

Returns:: A pandas dataframe with annotations and text_snippet columns.
Return type:: DataFrame