abacusai.batch_prediction_version

Classes

`BatchPredictionArgs`	An abstract class for Batch Prediction args specific to problem type.
`FileFormat`	Generic enumeration.
`PredictionFeatureGroup`	Batch Input Feature Group
`PredictionInput`	Batch inputs
`AbstractApiClass`
`BatchPredictionVersion`	Batch Prediction Version

Module Contents

class abacusai.batch_prediction_version.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_version.FileFormat

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AVRO = 'AVRO'

PARQUET = 'PARQUET'

TFRECORD = 'TFRECORD'

TSV = 'TSV'

CSV = 'CSV'

ORC = 'ORC'

JSON = 'JSON'

ODS = 'ODS'

XLS = 'XLS'

GZ = 'GZ'

ZIP = 'ZIP'

TAR = 'TAR'

DOCX = 'DOCX'

PDF = 'PDF'

RAR = 'RAR'

JPEG = 'JPG'

PNG = 'PNG'

TIF = 'TIFF'

NUMBERS = 'NUMBERS'

PPTX = 'PPTX'

PPT = 'PPT'

HTML = 'HTML'

TXT = 'txt'

class abacusai.batch_prediction_version.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_version.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_version.AbstractApiClass(client, id)

__eq__(other): Return self==value.

_get_attribute_as_dict(attribute)

class abacusai.batch_prediction_version.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