abacusai.api_class

Submodules

Package Contents

Classes

ApiClass

Helper class that provides a standard way to create an ABC using

FieldDescriptor

Configs for vector store indexing.

ApiClass

Helper class that provides a standard way to create an ABC using

HotkeyPrompt

A config class for a Data Science Co-Pilot Hotkey

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

BatchPredictionArgs

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

ForecastingBatchPredictionArgs

Batch Prediction Config for the FORECASTING problem type

NamedEntityExtractionBatchPredictionArgs

Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type

PersonalizationBatchPredictionArgs

Batch Prediction Config for the PERSONALIZATION problem type

PredictiveModelingBatchPredictionArgs

Batch Prediction Config for the PREDICTIVE_MODELING problem type

PretrainedModelsBatchPredictionArgs

Batch Prediction Config for the PRETRAINED_MODELS problem type

SentenceBoundaryDetectionBatchPredictionArgs

Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type

ThemeAnalysisBatchPredictionArgs

Batch Prediction Config for the THEME_ANALYSIS problem type

ChatLLMBatchPredictionArgs

Batch Prediction Config for the ChatLLM problem type

TrainablePlugAndPlayBatchPredictionArgs

Batch Prediction Config for the TrainablePlugAndPlay problem type

_BatchPredictionArgsFactory

Helper class that provides a standard way to create an ABC using

ApiClass

Helper class that provides a standard way to create an ABC using

BlobInput

Binary large object input data.

ApiClass

Helper class that provides a standard way to create an ABC using

OcrMode

Generic enumeration.

ParsingConfig

Custom config for dataset parsing.

DocumentProcessingConfig

Document processing configuration.

DatasetDocumentProcessingConfig

Document processing configuration for dataset imports.

IncrementalDatabaseConnectorConfig

Config information for incremental datasets from database connectors

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

DatasetConfig

An abstract class for dataset configs specific to application connectors.

ConfluenceDatasetConfig

Dataset config for Confluence Application Connector

GoogleAnalyticsDatasetConfig

Dataset config for Google Analytics Application Connector

GoogleDriveDatasetConfig

Dataset config for Google Drive Application Connector

JiraDatasetConfig

Dataset config for Jira Application Connector

OneDriveDatasetConfig

Dataset config for OneDrive Application Connector

SharepointDatasetConfig

Dataset config for Sharepoint Application Connector

ZendeskDatasetConfig

Dataset config for Zendesk Application Connector

AbacusUsageMetricsDatasetConfig

Dataset config for Abacus Usage Metrics Application Connector

_DatasetConfigFactory

Helper class that provides a standard way to create an ABC using

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

PredictionArguments

An abstract class for prediction arguments specific to problem type.

OptimizationPredictionArguments

Prediction arguments for the OPTIMIZATION problem type

TimeseriesAnomalyPredictionArguments

Prediction arguments for the TS_ANOMALY problem type

ChatLLMPredictionArguments

Prediction arguments for the CHAT_LLM problem type

RegressionPredictionArguments

Prediction arguments for the PREDICTIVE_MODELING problem type

ForecastingPredictionArguments

Prediction arguments for the FORECASTING problem type

CumulativeForecastingPredictionArguments

Prediction arguments for the CUMULATIVE_FORECASTING problem type

NaturalLanguageSearchPredictionArguments

Prediction arguments for the NATURAL_LANGUAGE_SEARCH problem type

FeatureStorePredictionArguments

Prediction arguments for the FEATURE_STORE problem type

_PredictionArgumentsFactory

Helper class that provides a standard way to create an ABC using

ApiClass

Helper class that provides a standard way to create an ABC using

VectorStoreTextEncoder

Generic enumeration.

VectorStoreConfig

Configs for vector store indexing.

DocumentRetrieverConfig

Configs for document retriever. If any configuration value is not explicitly provided, Abacus will automatically infer default values based on the data.

ApiEnum

Generic enumeration.

ProblemType

Generic enumeration.

RegressionObjective

Generic enumeration.

RegressionTreeHPOMode

Generic enumeration.

PartialDependenceAnalysis

Generic enumeration.

RegressionAugmentationStrategy

Generic enumeration.

RegressionTargetTransform

Generic enumeration.

RegressionTypeOfSplit

Generic enumeration.

RegressionTimeSplitMethod

Generic enumeration.

RegressionLossFunction

Generic enumeration.

ExplainerType

Generic enumeration.

SamplingMethodType

Generic enumeration.

MergeMode

Generic enumeration.

FillLogic

Generic enumeration.

BatchSize

Generic enumeration.

HolidayCalendars

Generic enumeration.

FileFormat

Generic enumeration.

ExperimentationMode

Generic enumeration.

PersonalizationTrainingMode

Generic enumeration.

PersonalizationObjective

Generic enumeration.

ForecastingObjective

Generic enumeration.

ForecastingFrequency

Generic enumeration.

ForecastingDataSplitType

Generic enumeration.

ForecastingLossFunction

Generic enumeration.

ForecastingLocalScaling

Generic enumeration.

ForecastingFillMethod

Generic enumeration.

ForecastingQuanitlesExtensionMethod

Generic enumeration.

TimeseriesAnomalyDataSplitType

Generic enumeration.

TimeseriesAnomalyTypeOfAnomaly

Generic enumeration.

TimeseriesAnomalyUseHeuristic

Generic enumeration.

NERObjective

Generic enumeration.

NERModelType

Generic enumeration.

NLPDocumentFormat

Generic enumeration.

SentimentType

Generic enumeration.

ClusteringImputationMethod

Generic enumeration.

ConnectorType

Generic enumeration.

ApplicationConnectorType

Generic enumeration.

PythonFunctionArgumentType

Generic enumeration.

PythonFunctionOutputArgumentType

Generic enumeration.

VectorStoreTextEncoder

Generic enumeration.

LLMName

Generic enumeration.

MonitorAlertType

Generic enumeration.

FeatureDriftType

Generic enumeration.

DataIntegrityViolationType

Generic enumeration.

BiasType

Generic enumeration.

AlertActionType

Generic enumeration.

PythonFunctionType

Generic enumeration.

EvalArtifactType

Generic enumeration.

FieldDescriptorType

Generic enumeration.

OcrMode

Generic enumeration.

StdDevThresholdType

Generic enumeration.

DataType

Generic enumeration.

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

SamplingConfig

An abstract class for the sampling config of a feature group

NSamplingConfig

The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified.

PercentSamplingConfig

The fraction of distinct values of the feature group to include in the sample.

_SamplingConfigFactory

Helper class that provides a standard way to create an ABC using

MergeConfig

An abstract class for the merge config of a feature group

LastNMergeConfig

Merge LAST N chunks/versions of an incremental dataset.

TimeWindowMergeConfig

Merge rows within a given timewindow of the most recent timestamp

_MergeConfigFactory

Helper class that provides a standard way to create an ABC using

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

TrainingConfig

An abstract class for the training config options used to train the model.

PersonalizationTrainingConfig

Training config for the PERSONALIZATION problem type

RegressionTrainingConfig

Training config for the PREDICTIVE_MODELING problem type

ForecastingTrainingConfig

Training config for the FORECASTING problem type

NamedEntityExtractionTrainingConfig

Training config for the NAMED_ENTITY_EXTRACTION problem type

NaturalLanguageSearchTrainingConfig

Training config for the NATURAL_LANGUAGE_SEARCH problem type

ChatLLMTrainingConfig

Training config for the CHAT_LLM problem type

SentenceBoundaryDetectionTrainingConfig

Training config for the SENTENCE_BOUNDARY_DETECTION problem type

SentimentDetectionTrainingConfig

Training config for the SENTIMENT_DETECTION problem type

DocumentClassificationTrainingConfig

Training config for the DOCUMENT_CLASSIFICATION problem type

DocumentSummarizationTrainingConfig

Training config for the DOCUMENT_SUMMARIZATION problem type

DocumentVisualizationTrainingConfig

Training config for the DOCUMENT_VISUALIZATION problem type

ClusteringTrainingConfig

Training config for the CLUSTERING problem type

ClusteringTimeseriesTrainingConfig

Training config for the CLUSTERING_TIMESERIES problem type

EventAnomalyTrainingConfig

Training config for the EVENT_ANOMALY problem type

TimeseriesAnomalyTrainingConfig

Training config for the TS_ANOMALY problem type

CumulativeForecastingTrainingConfig

Training config for the CUMULATIVE_FORECASTING problem type

ThemeAnalysisTrainingConfig

Training config for the THEME ANALYSIS problem type

AIAgentTrainingConfig

Training config for the AI_AGENT problem type

CustomTrainedModelTrainingConfig

Training config for the CUSTOM_TRAINED_MODEL problem type

CustomAlgorithmTrainingConfig

Training config for the CUSTOM_ALGORITHM problem type

OptimizationTrainingConfig

Training config for the OPTIMIZATION problem type

_TrainingConfigFactory

Helper class that provides a standard way to create an ABC using

DeployableAlgorithm

Algorithm that can be deployed to a model.

ApiClass

Helper class that provides a standard way to create an ABC using

StdDevThresholdType

Generic enumeration.

TimeWindowConfig

Time Window Configuration

ForecastingMonitorConfig

Forecasting Monitor Configuration

StdDevThreshold

Std Dev Threshold types

ItemAttributesStdDevThreshold

Item Attributes Std Dev Threshold for Monitor Alerts

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

AlertConditionConfig

An abstract class for alert condition configs

AccuracyBelowThresholdConditionConfig

Accuracy Below Threshold Condition Config for Monitor Alerts

FeatureDriftConditionConfig

Feature Drift Condition Config for Monitor Alerts

TargetDriftConditionConfig

Target Drift Condition Config for Monitor Alerts

HistoryLengthDriftConditionConfig

History Length Drift Condition Config for Monitor Alerts

DataIntegrityViolationConditionConfig

Data Integrity Violation Condition Config for Monitor Alerts

BiasViolationConditionConfig

Bias Violation Condition Config for Monitor Alerts

PredictionCountConditionConfig

Deployment Prediction Condition Config for Deployment Alerts. By default we monitor if predictions made over a time window has reduced significantly.

_AlertConditionConfigFactory

Helper class that provides a standard way to create an ABC using

AlertActionConfig

An abstract class for alert action configs

EmailActionConfig

Email Action Config for Monitor Alerts

_AlertActionConfigFactory

Helper class that provides a standard way to create an ABC using

MonitorThresholdConfig

Monitor Threshold Config for Monitor Alerts

ApiClass

Helper class that provides a standard way to create an ABC using

FeatureMappingConfig

Feature mapping configuration for a feature group type.

ProjectFeatureGroupTypeMappingsConfig

Project feature group type mappings.

ApiClass

Helper class that provides a standard way to create an ABC using

PythonFunctionArgument

A config class for python function arguments

OutputVariableMapping

A config class for python function arguments

ApiClass

Helper class that provides a standard way to create an ABC using

_ApiClassFactory

Helper class that provides a standard way to create an ABC using

FeatureGroupExportConfig

An abstract class for feature group exports.

FileConnectorExportConfig

File connector export config for feature groups

DatabaseConnectorExportConfig

Database connector export config for feature groups

_FeatureGroupExportConfigFactory

Helper class that provides a standard way to create an ABC using

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.FieldDescriptor

Bases: abacusai.api_class.abstract.ApiClass

Configs for vector store indexing.

Parameters:
  • field (str) – The field to be extracted. This will be used as the key in the response.

  • description (str) – The description of this field. If not included, the response_field will be used.

  • example_extraction (Union[str, int, bool, float]) – An example of this extracted field.

  • type (enums.FieldDescriptorType) – The type of this field. If not provided, the default type is STRING.

field: str
description: str
example_extraction: str | int | bool | float | list | dict
type: abacusai.api_class.enums.FieldDescriptorType
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.HotkeyPrompt

Bases: abacusai.api_class.abstract.ApiClass

A config class for a Data Science Co-Pilot Hotkey

Parameters:
  • prompt (str) – The prompt to send to Data Science Co-Pilot

  • title (str) – A short, descriptive title for the prompt. If not provided, one will be automatically generated.

prompt: str
title: str
disable_problem_type_context: bool
ignore_history: bool
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.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.api_class.ForecastingBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the FORECASTING problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation

  • predictions_start_date (str) – The start date for predictions.

  • use_prediction_offset (bool) – If True, use prediction offset.

  • start_date_offset (int) – Sets prediction start date as this offset relative to the prediction start date.

  • forecasting_horizon (int) – The number of timestamps to predict in the future. Range: [1, 1000].

  • item_attributes_to_include_in_the_result (list) – List of columns to include in the prediction output.

  • explain_predictions (bool) – If True, calculates explanations for the forecasted values along with predictions.

  • automate_monitoring (bool) – If True, creates a monitor to calculate the drift for the batch prediction.

for_eval: bool
predictions_start_date: str
use_prediction_offset: bool
start_date_offset: int
forecasting_horizon: int
item_attributes_to_include_in_the_result: list
explain_predictions: bool
automate_monitoring: bool
__post_init__()
class abacusai.api_class.NamedEntityExtractionBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • verbose_predictions (bool) – Return prediction inputs, predicted annotations and token label probabilities.

for_eval: bool
verbose_predictions: bool
__post_init__()
class abacusai.api_class.PersonalizationBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PERSONALIZATION problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • number_of_items (int) – Number of items to recommend.

  • item_attributes_to_include_in_the_result (list) – List of columns to include in the prediction output.

  • score_field (str) – If specified, relative item scores will be returned using a field with this name

for_eval: bool
number_of_items: int
item_attributes_to_include_in_the_result: list
score_field: str
__post_init__()
class abacusai.api_class.PredictiveModelingBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PREDICTIVE_MODELING problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • explainer_type (enums.ExplainerType) – The type of explainer to use to generate explanations on the batch prediction.

  • number_of_samples_to_use_for_explainer (int) – Number Of Samples To Use For Kernel Explainer.

  • include_multi_class_explanations (bool) – If True, Includes explanations for all classes in multi-class classification.

  • features_considered_constant_for_explanations (str) – Comma separate list of fields to treat as constant in SHAP explanations.

  • importance_of_records_in_nested_columns (str) – Returns importance of each index in the specified nested column instead of SHAP column explanations.

  • explanation_filter_lower_bound (float) – If set explanations will be limited to predictions above this value, Range: [0, 1].

  • explanation_filter_upper_bound (float) – If set explanations will be limited to predictions below this value, Range: [0, 1].

  • explanation_filter_label (str) – For classification problems specifies the label to which the explanation bounds are applied.

  • output_columns (list) – A list of column names to include in the prediction result.

  • explain_predictions (bool) – If True, calculates explanations for the predicted values along with predictions.

  • automate_monitoring (bool) – If True, creates a monitor to calculate the drift for the batch prediction.

for_eval: bool
explainer_type: abacusai.api_class.enums.ExplainerType
number_of_samples_to_use_for_explainer: int
include_multi_class_explanations: bool
features_considered_constant_for_explanations: str
importance_of_records_in_nested_columns: str
explanation_filter_lower_bound: float
explanation_filter_upper_bound: float
explanation_filter_label: str
output_columns: list
explain_predictions: bool
automate_monitoring: bool
__post_init__()
class abacusai.api_class.PretrainedModelsBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the PRETRAINED_MODELS problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • files_output_location_prefix (str) – The output location prefix for the files.

  • channel_id_to_label_map (str) – JSON string for the map from channel ids to their labels.

for_eval: bool
files_output_location_prefix: str
channel_id_to_label_map: str
__post_init__()
class abacusai.api_class.SentenceBoundaryDetectionBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation

  • explode_output (bool) – Explode data so there is one sentence per row.

for_eval: bool
explode_output: bool
__post_init__()
class abacusai.api_class.ThemeAnalysisBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the THEME_ANALYSIS problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • analysis_frequency (str) – The length of each analysis interval.

  • start_date (str) – The end point for predictions.

  • analysis_days (int) – How many days to analyze.

for_eval: bool
analysis_frequency: str
start_date: str
analysis_days: int
__post_init__()
class abacusai.api_class.ChatLLMBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the ChatLLM problem type

Parameters:

for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

for_eval: bool
__post_init__()
class abacusai.api_class.TrainablePlugAndPlayBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the TrainablePlugAndPlay problem type

Parameters:
  • for_eval (bool) – If True, the test fold which was created during training and used for metrics calculation will be used as input data. These predictions are hence, used for model evaluation.

  • automate_monitoring (bool) – If True, creates a monitor to calculate the drift for the batch prediction.

for_eval: bool
automate_monitoring: bool
__post_init__()
class abacusai.api_class._BatchPredictionArgsFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'problem_type'
config_class_map
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.BlobInput

Bases: abacusai.api_class.abstract.ApiClass

Binary large object input data.

Parameters:
  • filename (str) – The original filename of the blob.

  • contents (bytes) – The binary contents of the blob.

  • mime_type (str) – The mime type of the blob.

  • size (int) – The size of the blob in bytes.

filename: str
contents: bytes
mime_type: str
size: int
classmethod from_local_file(file_path)
Parameters:

file_path (str) –

Return type:

BlobInput

classmethod from_contents(contents, filename=None, mime_type=None)
Parameters:
  • contents (bytes) –

  • filename (str) –

  • mime_type (str) –

Return type:

BlobInput

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.OcrMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DEFAULT = 'DEFAULT'
LAYOUT = 'LAYOUT'
SCANNED = 'SCANNED'
COMPREHENSIVE = 'COMPREHENSIVE'
COMPREHENSIVE_V2 = 'COMPREHENSIVE_V2'
COMPREHENSIVE_TABLE_MD = 'COMPREHENSIVE_TABLE_MD'
class abacusai.api_class.ParsingConfig

Bases: abacusai.api_class.abstract.ApiClass

Custom config for dataset parsing.

Parameters:
  • escape (str) – Escape character for CSV files. Defaults to ‘”’.

  • csv_delimiter (str) – Delimiter for CSV files. Defaults to None.

  • file_path_with_schema (str) – Path to the file with schema. Defaults to None.

escape: str
csv_delimiter: str
file_path_with_schema: str
class abacusai.api_class.DocumentProcessingConfig

Bases: abacusai.api_class.abstract.ApiClass

Document processing configuration.

Parameters:
  • extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.

  • ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.

  • use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.

  • remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.

  • remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.

  • convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.

extract_bounding_boxes: bool = False
ocr_mode: abacusai.api_class.enums.OcrMode
use_full_ocr: bool
remove_watermarks: bool = True
convert_to_markdown: bool = False
class abacusai.api_class.DatasetDocumentProcessingConfig

Bases: DocumentProcessingConfig

Document processing configuration for dataset imports.

Parameters:
  • extract_bounding_boxes (bool) – Whether to perform OCR and extract bounding boxes. If False, no OCR will be done but only the embedded text from digital documents will be extracted. Defaults to False.

  • ocr_mode (OcrMode) – OCR mode. There are different OCR modes available for different kinds of documents and use cases. This option only takes effect when extract_bounding_boxes is True.

  • use_full_ocr (bool) – Whether to perform full OCR. If True, OCR will be performed on the full page. If False, OCR will be performed on the non-text regions only. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.

  • remove_header_footer (bool) – Whether to remove headers and footers. Defaults to False. This option only takes effect when extract_bounding_boxes is True.

  • remove_watermarks (bool) – Whether to remove watermarks. By default, it will be decided automatically based on the OCR mode and the document type. This option only takes effect when extract_bounding_boxes is True.

  • convert_to_markdown (bool) – Whether to convert extracted text to markdown. Defaults to False. This option only takes effect when extract_bounding_boxes is True.

  • page_text_column (str) – Name of the output column which contains the extracted text for each page. If not provided, no column will be created.

page_text_column: str
class abacusai.api_class.IncrementalDatabaseConnectorConfig

Bases: abacusai.api_class.abstract.ApiClass

Config information for incremental datasets from database connectors

Parameters:

timestamp_column (str) – If dataset is incremental, this is the column name of the required column in the dataset. This column must contain timestamps in descending order which are used to determine the increments of the incremental dataset.

timestamp_column: str
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.DatasetConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for dataset configs specific to application connectors.

application_connector_type: abacusai.api_class.enums.ApplicationConnectorType
classmethod _get_builder()
class abacusai.api_class.ConfluenceDatasetConfig

Bases: DatasetConfig

Dataset config for Confluence Application Connector :param pull_attachments: Whether to pull attachments for each page :type pull_attachments: bool, optional :param space_key: The space key to fetch pages from :type space_key: str, optional

pull_attachments: bool
space_key: str
__post_init__()
class abacusai.api_class.GoogleAnalyticsDatasetConfig

Bases: DatasetConfig

Dataset config for Google Analytics Application Connector

Parameters:
  • location (str) – The view id of the report in the connector to fetch

  • start_timestamp (int, optional) – Unix timestamp of the start of the period that will be queried

  • end_timestamp (int, optional) – Unix timestamp of the end of the period that will be queried

location: str
start_timestamp: int
end_timestamp: int
__post_init__()
class abacusai.api_class.GoogleDriveDatasetConfig

Bases: DatasetConfig

Dataset config for Google Drive Application Connector

Parameters:
  • location (str) – The regex location of the files to fetch

  • is_documentset (bool) – Whether the dataset is a document set

  • csv_delimiter (str, optional) – If the file format is CSV, use a specific csv delimiter

  • extract_bounding_boxes (bool, optional) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True

  • merge_file_schemas (bool, optional) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str
is_documentset: bool
csv_delimiter: str
extract_bounding_boxes: bool
merge_file_schemas: bool
__post_init__()
class abacusai.api_class.JiraDatasetConfig

Bases: DatasetConfig

Dataset config for Jira Application Connector

Parameters:
  • jql (str) – The JQL query for fetching issues

  • custom_fields (list, optional) – A list of custom fields to include in the dataset

  • include_comments (bool, optional) – Fetch comments for each issue

  • include_watchers (bool, optional) – Fetch watchers for each issue

jql: str
custom_fields: list
include_comments: bool
include_watchers: bool
__post_init__()
class abacusai.api_class.OneDriveDatasetConfig

Bases: DatasetConfig

Dataset config for OneDrive Application Connector

Parameters:
  • location (str) – The regex location of the files to fetch

  • is_documentset (bool) – Whether the dataset is a document set

  • csv_delimiter (str, optional) – If the file format is CSV, use a specific csv delimiter

  • extract_bounding_boxes (bool, optional) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True

  • merge_file_schemas (bool, optional) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str
is_documentset: bool
csv_delimiter: str
extract_bounding_boxes: bool
merge_file_schemas: bool
__post_init__()
class abacusai.api_class.SharepointDatasetConfig

Bases: DatasetConfig

Dataset config for Sharepoint Application Connector

Parameters:
  • location (str) – The regex location of the files to fetch

  • is_documentset (bool) – Whether the dataset is a document set

  • csv_delimiter (str, optional) – If the file format is CSV, use a specific csv delimiter

  • extract_bounding_boxes (bool, optional) – Signifies whether to extract bounding boxes out of the documents. Only valid if is_documentset if True

  • merge_file_schemas (bool, optional) – Signifies if the merge file schema policy is enabled. Not applicable if is_documentset is True

location: str
is_documentset: bool
csv_delimiter: str
extract_bounding_boxes: bool
merge_file_schemas: bool
__post_init__()
class abacusai.api_class.ZendeskDatasetConfig

Bases: DatasetConfig

Dataset config for Zendesk Application Connector

__post_init__()
class abacusai.api_class.AbacusUsageMetricsDatasetConfig

Bases: DatasetConfig

Dataset config for Abacus Usage Metrics Application Connector

Parameters:

include_entire_conversation_history (bool) – Whether to show the entire history for this deployment conversation

include_entire_conversation_history: bool
__post_init__()
class abacusai.api_class._DatasetConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'application_connector_type'
config_class_map
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.PredictionArguments

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for prediction arguments specific to problem type.

_support_kwargs: bool
kwargs: dict
problem_type: abacusai.api_class.enums.ProblemType
classmethod _get_builder()
class abacusai.api_class.OptimizationPredictionArguments

Bases: PredictionArguments

Prediction arguments for the OPTIMIZATION problem type

Parameters:
  • forced_assignments (dict) – Set of assignments to force and resolve before returning query results.

  • solve_time_limit_seconds (float) – Maximum time in seconds to spend solving the query.

  • include_all_assignments (bool) – If True, will return all assignments, including assignments with value 0. Default is False.

forced_assignments: dict
solve_time_limit_seconds: float
include_all_assignments: bool
__post_init__()
class abacusai.api_class.TimeseriesAnomalyPredictionArguments

Bases: PredictionArguments

Prediction arguments for the TS_ANOMALY problem type

Parameters:
  • start_timestamp (str) – Timestamp from which anomalies have to be detected in the training data

  • end_timestamp (str) – Timestamp to which anomalies have to be detected in the training data

  • get_all_item_data (bool) – If True, anomaly detection has to be performed on all the data related to input ids

start_timestamp: str
end_timestamp: str
get_all_item_data: bool
__post_init__()
class abacusai.api_class.ChatLLMPredictionArguments

Bases: PredictionArguments

Prediction arguments for the CHAT_LLM problem type

Parameters:
  • llm_name (str) – Name of the specific LLM backend to use to power the chat experience.

  • num_completion_tokens (int) – Default for maximum number of tokens for chat answers.

  • system_message (str) – The generative LLM system message.

  • temperature (float) – The generative LLM temperature.

  • search_score_cutoff (float) – Cutoff for the document retriever score. Matching search results below this score will be ignored.

  • ignore_documents (bool) – If True, will ignore any documents and search results, and only use the messages to generate a response.

llm_name: str
num_completion_tokens: int
system_message: str
temperature: float
search_score_cutoff: float
ignore_documents: bool
__post_init__()
class abacusai.api_class.RegressionPredictionArguments

Bases: PredictionArguments

Prediction arguments for the PREDICTIVE_MODELING problem type

Parameters:
  • explain_predictions (bool) – If true, will explain predictions.

  • explainer_type (str) – Type of explainer to use for explanations.

explain_predictions: bool
explainer_type: str
__post_init__()
class abacusai.api_class.ForecastingPredictionArguments

Bases: PredictionArguments

Prediction arguments for the FORECASTING problem type

Parameters:
  • num_predictions (int) – The number of timestamps to predict in the future.

  • prediction_start (str) – The start date for predictions (e.g., “2015-08-01T00:00:00” as input for mid-night of 2015-08-01).

  • explain_predictions (bool) – If True, explain predictions for forecasting.

  • explainer_type (str) – Type of explainer to use for explanations.

  • get_item_data (bool) – If True, will return the data corresponding to items as well.

num_predictions: int
prediction_start: str
explain_predictions: bool
explainer_type: str
get_item_data: bool
__post_init__()
class abacusai.api_class.CumulativeForecastingPredictionArguments

Bases: PredictionArguments

Prediction arguments for the CUMULATIVE_FORECASTING problem type

Parameters:
  • num_predictions (int) – The number of timestamps to predict in the future.

  • prediction_start (str) – The start date for predictions (e.g., “2015-08-01T00:00:00” as input for mid-night of 2015-08-01).

  • explain_predictions (bool) – If True, explain predictions for forecasting.

  • explainer_type (str) – Type of explainer to use for explanations.

  • get_item_data (bool) – If True, will return the data corresponding to items as well.

num_predictions: int
prediction_start: str
explain_predictions: bool
explainer_type: str
get_item_data: bool
__post_init__()
class abacusai.api_class.NaturalLanguageSearchPredictionArguments

Bases: PredictionArguments

Prediction arguments for the NATURAL_LANGUAGE_SEARCH problem type

Parameters:
  • llm_name (str) – Name of the specific LLM backend to use to power the chat experience.

  • num_completion_tokens (int) – Default for maximum number of tokens for chat answers.

  • system_message (str) – The generative LLM system message.

  • temperature (float) – The generative LLM temperature.

  • search_score_cutoff (float) – Cutoff for the document retriever score. Matching search results below this score will be ignored.

  • ignore_documents (bool) – If True, will ignore any documents and search results, and only use the messages to generate a response.

llm_name: str
num_completion_tokens: int
system_message: str
temperature: float
search_score_cutoff: float
ignore_documents: bool
__post_init__()
class abacusai.api_class.FeatureStorePredictionArguments

Bases: PredictionArguments

Prediction arguments for the FEATURE_STORE problem type

Parameters:

limit_results (int) – If provided, will limit the number of results to the value specified.

limit_results: int
__post_init__()
class abacusai.api_class._PredictionArgumentsFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'problem_type'
config_class_map
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.VectorStoreTextEncoder

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

E5 = 'E5'
OPENAI = 'OPENAI'
SENTENCE_BERT = 'SENTENCE_BERT'
E5_SMALL = 'E5_SMALL'
class abacusai.api_class.VectorStoreConfig

Bases: abacusai.api_class.abstract.ApiClass

Configs for vector store indexing.

Parameters:
  • chunk_size (int) – The size of text chunks in the vector store.

  • chunk_overlap_fraction (float) – The fraction of overlap between chunks.

  • text_encoder (VectorStoreTextEncoder) – Encoder used to index texts from the documents.

  • chunk_size_factors (list) – Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to chunk_size.

  • score_multiplier_column (str) – If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.

  • prune_vectors (bool) – Transform vectors using SVD so that the average component of vectors in the corpus are removed.

chunk_size: int
chunk_overlap_fraction: float
text_encoder: abacusai.api_class.enums.VectorStoreTextEncoder
chunk_size_factors: list
score_multiplier_column: str
prune_vectors: bool
class abacusai.api_class.DocumentRetrieverConfig

Bases: VectorStoreConfig

Configs for document retriever. If any configuration value is not explicitly provided, Abacus will automatically infer default values based on the data.

class abacusai.api_class.ApiEnum

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

__eq__(other)

Return self==value.

__hash__()

Return hash(self).

class abacusai.api_class.ProblemType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AI_AGENT = 'ai_agent'
EVENT_ANOMALY = 'event_anomaly'
CLUSTERING = 'clustering'
CLUSTERING_TIMESERIES = 'clustering_timeseries'
CUMULATIVE_FORECASTING = 'cumulative_forecasting'
NAMED_ENTITY_EXTRACTION = 'nlp_ner'
CHAT_LLM = 'chat_llm'
SENTENCE_BOUNDARY_DETECTION = 'nlp_sentence_boundary_detection'
SENTIMENT_DETECTION = 'nlp_sentiment'
DOCUMENT_CLASSIFICATION = 'nlp_classification'
DOCUMENT_SUMMARIZATION = 'nlp_summarization'
DOCUMENT_VISUALIZATION = 'nlp_document_visualization'
PERSONALIZATION = 'personalization'
PREDICTIVE_MODELING = 'regression'
FINETUNED_LLM = 'finetuned_llm'
FORECASTING = 'forecasting'
CUSTOM_TRAINED_MODEL = 'plug_and_play'
CUSTOM_ALGORITHM = 'trainable_plug_and_play'
FEATURE_STORE = 'feature_store'
IMAGE_CLASSIFICATION = 'vision_classification'
OBJECT_DETECTION = 'vision_object_detection'
IMAGE_VALUE_PREDICTION = 'vision_regression'
MODEL_MONITORING = 'model_monitoring'
LANGUAGE_DETECTION = 'language_detection'
OPTIMIZATION = 'optimization'
PRETRAINED_MODELS = 'pretrained'
THEME_ANALYSIS = 'theme_analysis'
TS_ANOMALY = 'ts_anomaly'
class abacusai.api_class.RegressionObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUC = 'auc'
ACCURACY = 'acc'
LOG_LOSS = 'log_loss'
PRECISION = 'precision'
RECALL = 'recall'
F1_SCORE = 'fscore'
MAE = 'mae'
MAPE = 'mape'
WAPE = 'wape'
RMSE = 'rmse'
R_SQUARED_COEFFICIENT_OF_DETERMINATION = 'r^2'
class abacusai.api_class.RegressionTreeHPOMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'
THOROUGH = 'thorough'
class abacusai.api_class.PartialDependenceAnalysis

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'
THOROUGH = 'thorough'
class abacusai.api_class.RegressionAugmentationStrategy

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

SMOTE = 'smote'
RESAMPLE = 'resample'
class abacusai.api_class.RegressionTargetTransform

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LOG = 'log'
QUANTILE = 'quantile'
YEO_JOHNSON = 'yeo-johnson'
BOX_COX = 'box-cox'
class abacusai.api_class.RegressionTypeOfSplit

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RANDOM = 'Random Sampling'
TIMESTAMP_BASED = 'Timestamp Based'
ROW_INDICATOR_BASED = 'Row Indicator Based'
STRATIFIED_RANDOM_SAMPLING = 'Stratified Random Sampling'
class abacusai.api_class.RegressionTimeSplitMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

TEST_SPLIT_PERCENTAGE_BASED = 'Test Split Percentage Based'
TEST_START_TIMESTAMP_BASED = 'Test Start Timestamp Based'
class abacusai.api_class.RegressionLossFunction

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HUBER = 'Huber'
MSE = 'Mean Squared Error'
MAE = 'Mean Absolute Error'
MAPE = 'Mean Absolute Percentage Error'
MSLE = 'Mean Squared Logarithmic Error'
TWEEDIE = 'Tweedie'
CROSS_ENTROPY = 'Cross Entropy'
FOCAL_CROSS_ENTROPY = 'Focal Cross Entropy'
AUTOMATIC = 'Automatic'
CUSTOM = 'Custom'
class abacusai.api_class.ExplainerType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

KERNEL_EXPLAINER = 'KERNEL_EXPLAINER'
LIME_EXPLAINER = 'LIME_EXPLAINER'
TREE_EXPLAINER = 'TREE_EXPLAINER'
EBM_EXPLAINER = 'EBM_EXPLAINER'
class abacusai.api_class.SamplingMethodType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

N_SAMPLING = 'N_SAMPLING'
PERCENT_SAMPLING = 'PERCENT_SAMPLING'
class abacusai.api_class.MergeMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LAST_N = 'LAST_N'
TIME_WINDOW = 'TIME_WINDOW'
class abacusai.api_class.FillLogic

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AVERAGE = 'average'
MAX = 'max'
MEDIAN = 'median'
MIN = 'min'
CUSTOM = 'custom'
BACKFILL = 'bfill'
FORWARDFILL = 'ffill'
LINEAR = 'linear'
NEAREST = 'nearest'
class abacusai.api_class.BatchSize

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

BATCH_8 = 8
BATCH_16 = 16
BATCH_32 = 32
BATCH_64 = 64
BATCH_128 = 128
BATCH_256 = 256
BATCH_384 = 384
BATCH_512 = 512
BATCH_740 = 740
BATCH_1024 = 1024
class abacusai.api_class.HolidayCalendars

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AU = 'AU'
UK = 'UK'
US = 'US'
class abacusai.api_class.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'
class abacusai.api_class.ExperimentationMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

RAPID = 'rapid'
THOROUGH = 'thorough'
class abacusai.api_class.PersonalizationTrainingMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

EXPERIMENTAL = 'EXP'
PRODUCTION = 'PROD'
class abacusai.api_class.PersonalizationObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NDCG = 'ndcg'
NDCG_5 = 'ndcg@5'
NDCG_10 = 'ndcg@10'
MAP = 'map'
MAP_5 = 'map@5'
MAP_10 = 'map@10'
MRR = 'mrr'
PERSONALIZATION = 'personalization@10'
COVERAGE = 'coverage'
class abacusai.api_class.ForecastingObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ACCURACY = 'w_c_accuracy'
WAPE = 'wape'
MAPE = 'mape'
CMAPE = 'cmape'
RMSE = 'rmse'
CV = 'coefficient_of_variation'
BIAS = 'bias'
SRMSE = 'srmse'
class abacusai.api_class.ForecastingFrequency

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HOURLY = '1H'
DAILY = '1D'
WEEKLY_SUNDAY_START = '1W'
WEEKLY_MONDAY_START = 'W-MON'
WEEKLY_SATURDAY_START = 'W-SAT'
MONTH_START = 'MS'
MONTH_END = '1M'
QUARTER_START = 'QS'
QUARTER_END = '1Q'
YEARLY = '1Y'
class abacusai.api_class.ForecastingDataSplitType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'Automatic Time Based'
TIMESTAMP = 'Timestamp Based'
ITEM = 'Item Based'
PREDICTION_LENGTH = 'Force Prediction Length'
class abacusai.api_class.ForecastingLossFunction

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

CUSTOM = 'Custom'
MEAN_ABSOLUTE_ERROR = 'mae'
NORMALIZED_MEAN_ABSOLUTE_ERROR = 'nmae'
PEAKS_MEAN_ABSOLUTE_ERROR = 'peaks_mae'
MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'stable_mape'
POINTWISE_ACCURACY = 'accuracy'
ROOT_MEAN_SQUARE_ERROR = 'rmse'
NORMALIZED_ROOT_MEAN_SQUARE_ERROR = 'nrmse'
ASYMMETRIC_MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'asymmetric_mape'
STABLE_STANDARDIZED_MEAN_ABSOLUTE_PERCENTAGE_ERROR = 'stable_standardized_mape_with_cmape'
GAUSSIAN = 'mle_gaussian_local'
GAUSSIAN_FULL_COVARIANCE = 'mle_gaussfullcov'
GUASSIAN_EXPONENTIAL = 'mle_gaussexp'
MIX_GAUSSIANS = 'mle_gaussmix'
WEIBULL = 'mle_weibull'
NEGATIVE_BINOMIAL = 'mle_negbinom'
LOG_ROOT_MEAN_SQUARE_ERROR = 'log_rmse'
class abacusai.api_class.ForecastingLocalScaling

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ZSCORE = 'zscore'
SLIDING_ZSCORE = 'sliding_zscore'
LAST_POINT = 'lastpoint'
MIN_MAX = 'minmax'
MIN_STD = 'minstd'
ROBUST = 'robust'
ITEM = 'item'
class abacusai.api_class.ForecastingFillMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

BACK = 'BACK'
MIDDLE = 'MIDDLE'
FUTURE = 'FUTURE'
class abacusai.api_class.ForecastingQuanitlesExtensionMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DIRECT = 'direct'
QUADRATIC = 'quadratic'
ANCESTRAL_SIMULATION = 'simulation'
class abacusai.api_class.TimeseriesAnomalyDataSplitType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'Automatic Time Based'
TIMESTAMP = 'Fixed Timestamp Based'
class abacusai.api_class.TimeseriesAnomalyTypeOfAnomaly

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

HIGH_PEAK = 'high_peak'
LOW_PEAK = 'low_peak'
class abacusai.api_class.TimeseriesAnomalyUseHeuristic

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ENABLE = 'enable'
DISABLE = 'disable'
AUTOMATIC = 'automatic'
class abacusai.api_class.NERObjective

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

LOG_LOSS = 'log_loss'
AUC = 'auc'
PRECISION = 'precision'
RECALL = 'recall'
ANNOTATIONS_PRECISION = 'annotations_precision'
ANNOTATIONS_RECALL = 'annotations_recall'
class abacusai.api_class.NERModelType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

PRETRAINED_BERT = 'pretrained_bert'
PRETRAINED_ROBERTA_27 = 'pretrained_roberta_27'
PRETRAINED_ROBERTA_43 = 'pretrained_roberta_43'
PRETRAINED_MULTILINGUAL = 'pretrained_multilingual'
LEARNED = 'learned'
class abacusai.api_class.NLPDocumentFormat

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTO = 'auto'
TEXT = 'text'
DOC = 'doc'
TOKENS = 'tokens'
class abacusai.api_class.SentimentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

VALENCE = 'valence'
EMOTION = 'emotion'
class abacusai.api_class.ClusteringImputationMethod

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

AUTOMATIC = 'Automatic'
ZEROS = 'Zeros'
INTERPOLATE = 'Interpolate'
class abacusai.api_class.ConnectorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FILE = 'FILE'
DATABASE = 'DATABASE'
STREAMING = 'STREAMING'
APPLICATION = 'APPLICATION'
class abacusai.api_class.ApplicationConnectorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

GOOGLEANALYTICS = 'GOOGLEANALYTICS'
GOOGLEDRIVE = 'GOOGLEDRIVE'
GIT = 'GIT'
CONFLUENCE = 'CONFLUENCE'
JIRA = 'JIRA'
ONEDRIVE = 'ONEDRIVE'
ZENDESK = 'ZENDESK'
SLACK = 'SLACK'
SHAREPOINT = 'SHAREPOINT'
TEAMS = 'TEAMS'
ABACUSUSAGEMETRICS = 'ABACUSUSAGEMETRICS'
MICROSOFTAUTH = 'MICROSOFTAUTH'
class abacusai.api_class.PythonFunctionArgumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FEATURE_GROUP = 'FEATURE_GROUP'
INTEGER = 'INTEGER'
STRING = 'STRING'
BOOLEAN = 'BOOLEAN'
FLOAT = 'FLOAT'
JSON = 'JSON'
LIST = 'LIST'
DATASET_ID = 'DATASET_ID'
MODEL_ID = 'MODEL_ID'
FEATURE_GROUP_ID = 'FEATURE_GROUP_ID'
MONITOR_ID = 'MONITOR_ID'
BATCH_PREDICTION_ID = 'BATCH_PREDICTION_ID'
DEPLOYMENT_ID = 'DEPLOYMENT_ID'
class abacusai.api_class.PythonFunctionOutputArgumentType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NTEGER = 'INTEGER'
STRING = 'STRING'
BOOLEAN = 'BOOLEAN'
FLOAT = 'FLOAT'
JSON = 'JSON'
LIST = 'LIST'
DATASET_ID = 'DATASET_ID'
MODEL_ID = 'MODEL_ID'
FEATURE_GROUP_ID = 'FEATURE_GROUP_ID'
MONITOR_ID = 'MONITOR_ID'
BATCH_PREDICTION_ID = 'BATCH_PREDICTION_ID'
DEPLOYMENT_ID = 'DEPLOYMENT_ID'
ANY = 'ANY'
class abacusai.api_class.VectorStoreTextEncoder

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

E5 = 'E5'
OPENAI = 'OPENAI'
SENTENCE_BERT = 'SENTENCE_BERT'
E5_SMALL = 'E5_SMALL'
class abacusai.api_class.LLMName

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

OPENAI_GPT4 = 'OPENAI_GPT4'
OPENAI_GPT4_32K = 'OPENAI_GPT4_32K'
OPENAI_GPT4_128K = 'OPENAI_GPT4_128K'
OPENAI_GPT4_VISION = 'OPENAI_GPT4_VISION'
OPENAI_GPT3_5 = 'OPENAI_GPT3_5'
OPENAI_GPT3_5_TEXT = 'OPENAI_GPT3_5_TEXT'
CLAUDE_V2_1 = 'CLAUDE_V2_1'
CLAUDE_V3_OPUS = 'CLAUDE_V3_OPUS'
CLAUDE_V3_SONNET = 'CLAUDE_V3_SONNET'
ABACUS_GIRAFFE = 'ABACUS_GIRAFFE'
ABACUS_GIRAFFE_LARGE = 'ABACUS_GIRAFFE_LARGE'
LLAMA2_CHAT = 'LLAMA2_CHAT'
PALM = 'PALM'
PALM_TEXT = 'PALM_TEXT'
GEMINI_PRO = 'GEMINI_PRO'
MIXTRAL_CHAT = 'MIXTRAL_CHAT'
MISTRAL_MEDIUM = 'MISTRAL_MEDIUM'
class abacusai.api_class.MonitorAlertType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ACCURACY_BELOW_THRESHOLD = 'AccuracyBelowThreshold'
FEATURE_DRIFT = 'FeatureDrift'
DATA_INTEGRITY_VIOLATIONS = 'DataIntegrityViolations'
BIAS_VIOLATIONS = 'BiasViolations'
HISTORY_LENGTH_DRIFT = 'HistoryLengthDrift'
TARGET_DRIFT = 'TargetDrift'
PREDICTION_COUNT = 'PredictionCount'
class abacusai.api_class.FeatureDriftType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

KL = 'kl'
KS = 'ks'
WS = 'ws'
JS = 'js'
PSI = 'psi'
CHI_SQUARE = 'chi_square'
CSI = 'csi'
class abacusai.api_class.DataIntegrityViolationType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

NULL_VIOLATIONS = 'null_violations'
TYPE_MISMATCH_VIOLATIONS = 'type_mismatch_violations'
RANGE_VIOLATIONS = 'range_violations'
CATEGORICAL_RANGE_VIOLATION = 'categorical_range_violations'
TOTAL_VIOLATIONS = 'total_violations'
class abacusai.api_class.BiasType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DEMOGRAPHIC_PARITY = 'demographic_parity'
EQUAL_OPPORTUNITY = 'equal_opportunity'
GROUP_BENEFIT_EQUALITY = 'group_benefit'
TOTAL = 'total'
class abacusai.api_class.AlertActionType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

EMAIL = 'Email'
class abacusai.api_class.PythonFunctionType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FEATURE_GROUP = 'FEATURE_GROUP'
PLOTLY_FIG = 'PLOTLY_FIG'
STEP_FUNCTION = 'STEP_FUNCTION'
class abacusai.api_class.EvalArtifactType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

FORECASTING_ACCURACY = 'bar_chart'
FORECASTING_VOLUME = 'bar_chart_volume'
FORECASTING_HISTORY_LENGTH_ACCURACY = 'bar_chart_accuracy_by_history'
class abacusai.api_class.FieldDescriptorType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

STRING = 'STRING'
INTEGER = 'INTEGER'
FLOAT = 'FLOAT'
BOOLEAN = 'BOOLEAN'
DATETIME = 'DATETIME'
DATE = 'DATE'
class abacusai.api_class.OcrMode

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

DEFAULT = 'DEFAULT'
LAYOUT = 'LAYOUT'
SCANNED = 'SCANNED'
COMPREHENSIVE = 'COMPREHENSIVE'
COMPREHENSIVE_V2 = 'COMPREHENSIVE_V2'
COMPREHENSIVE_TABLE_MD = 'COMPREHENSIVE_TABLE_MD'
class abacusai.api_class.StdDevThresholdType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ABSOLUTE = 'ABSOLUTE'
PERCENTILE = 'PERCENTILE'
STDDEV = 'STDDEV'
class abacusai.api_class.DataType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

INTEGER = 'integer'
FLOAT = 'float'
STRING = 'string'
DATE = 'date'
DATETIME = 'datetime'
BOOLEAN = 'boolean'
LIST = 'list'
STRUCT = 'struct'
NULL = 'null'
classmethod from_str(value)
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.SamplingConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the sampling config of a feature group

sampling_method: abacusai.api_class.enums.SamplingMethodType
classmethod _get_builder()
__post_init__()
class abacusai.api_class.NSamplingConfig

Bases: SamplingConfig

The number of distinct values of the key columns to include in the sample, or number of rows if key columns not specified.

Parameters:
  • sampling_method (SamplingMethodType) – N_SAMPLING

  • sample_count (int) – The number of rows to include in the sample

  • key_columns (List[str]) – The feature(s) to use as the key(s) when sampling

sample_count: int
key_columns: List[str]
__post_init__()
class abacusai.api_class.PercentSamplingConfig

Bases: SamplingConfig

The fraction of distinct values of the feature group to include in the sample.

Parameters:
  • sampling_method (SamplingMethodType) – PERCENT_SAMPLING

  • sample_percent (float) – The percentage of the rows to sample

  • key_columns (List[str]) – The feature(s) to use as the key(s) when sampling

sample_percent: float
key_columns: List[str]
__post_init__()
class abacusai.api_class._SamplingConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_class_key = 'sampling_method'
config_abstract_class
config_class_map
class abacusai.api_class.MergeConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the merge config of a feature group

merge_mode: abacusai.api_class.enums.MergeMode
classmethod _get_builder()
__post_init__()
class abacusai.api_class.LastNMergeConfig

Bases: MergeConfig

Merge LAST N chunks/versions of an incremental dataset.

Parameters:
  • merge_mode (MergeMode) – LAST_N

  • num_versions (int) – The number of versions to merge. num_versions == 0 means merge all versions.

  • include_version_timestamp_column (bool) – If set, include a column with the creation timestamp of source FG versions.

num_versions: int
include_version_timestamp_column: bool
__post_init__()
class abacusai.api_class.TimeWindowMergeConfig

Bases: MergeConfig

Merge rows within a given timewindow of the most recent timestamp

Parameters:
  • merge_mode (MergeMode) – TIME_WINDOW

  • feature_name (str) – Time based column to index on

  • time_window_size_ms (int) – Range of merged rows will be [MAX_TIME - time_window_size_ms, MAX_TIME]

  • include_version_timestamp_column (bool) – If set, include a column with the creation timestamp of source FG versions.

feature_name: str
time_window_size_ms: int
include_version_timestamp_column: bool
__post_init__()
class abacusai.api_class._MergeConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_class_key = 'merge_mode'
config_abstract_class
config_class_map
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.TrainingConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for the training config options used to train the model.

_upper_snake_case_keys: bool
_support_kwargs: bool
kwargs: dict
problem_type: abacusai.api_class.enums.ProblemType
algorithm: str
classmethod _get_builder()
class abacusai.api_class.PersonalizationTrainingConfig

Bases: TrainingConfig

Training config for the PERSONALIZATION problem type

Parameters:
  • objective (PersonalizationObjective) – Ranking scheme used to select final best model.

  • sort_objective (PersonalizationObjective) – Ranking scheme used to sort models on the metrics page.

  • training_mode (PersonalizationTrainingMode) – whether to train in production or experimental mode. Defaults to EXP.

  • target_action_types (List[str]) – List of action types to use as targets for training.

  • target_action_weights (Dict[str, float]) – Dictionary of action types to weights for training.

  • session_event_types (List[str]) – List of event types to treat as occurrences of sessions.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 6% to 20% of your dataset to use as test data.

  • recent_days_for_training (int) – Limit training data to a certain latest number of days.

  • training_start_date (str) – Only consider training interaction data after this date. Specified in the timezone of the dataset.

  • test_on_user_split (bool) – Use user splits instead of using time splits, when validating and testing the model.

  • test_split_on_last_k_items (bool) – Use last k items instead of global timestamp splits, when validating and testing the model.

  • test_last_items_length (int) – Number of items to leave out for each user when using leave k out folds.

  • test_window_length_hours (int) – Duration (in hours) of most recent time window to use when validating and testing the model.

  • explicit_time_split (bool) – Sets an explicit time-based test boundary.

  • test_row_indicator (str) – Column indicating which rows to use for training (TRAIN), validation (VAL) and testing (TEST).

  • full_data_retraining (bool) – Train models separately with all the data.

  • sequential_training (bool) – Train a mode sequentially through time.

  • data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.

  • optimized_event_type (str) – The final event type to optimize for and compute metrics on.

  • dropout_rate (int) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

  • disable_transformer (bool) – Disable training the transformer algorithm.

  • disable_gpu (boo) – Disable training on GPU.

  • filter_history (bool) – Do not recommend items the user has already interacted with.

  • action_types_exclusion_days (Dict[str, float]) – Mapping from action type to number of days for which we exclude previously interacted items from prediction

  • session_dedupe_mins (float) – Minimum number of minutes between two sessions for a user.

  • max_history_length (int) – Maximum length of user-item history to include user in training examples.

  • compute_rerank_metrics (bool) – Compute metrics based on rerank results.

  • add_time_features (bool) – Include interaction time as a feature.

  • disable_timestamp_scalar_features (bool) – Exclude timestamp scalar features.

  • compute_session_metrics (bool) – Evaluate models based on how well they are able to predict the next session of interactions.

  • max_user_history_len_percentile (int) – Filter out users with history length above this percentile.

  • downsample_item_popularity_percentile (float) – Downsample items more popular than this percentile.

  • use_user_id_feature (bool) – Use user id as a feature in CTR models.

  • min_item_history (int) – Minimum number of interactions an item must have to be included in training.

  • query_column (str) – Name of column in the interactions table that represents a natural language query, e.g. ‘blue t-shirt’.

  • item_query_column (str) – Name of column in the item catalog that will be matched to the query column in the interactions table.

objective: abacusai.api_class.enums.PersonalizationObjective
sort_objective: abacusai.api_class.enums.PersonalizationObjective
training_mode: abacusai.api_class.enums.PersonalizationTrainingMode
target_action_types: List[str]
target_action_weights: Dict[str, float]
session_event_types: List[str]
test_split: int
recent_days_for_training: int
training_start_date: str
test_on_user_split: bool
test_split_on_last_k_items: bool
test_last_items_length: int
test_window_length_hours: int
explicit_time_split: bool
test_row_indicator: str
full_data_retraining: bool
sequential_training: bool
data_split_feature_group_table_name: str
optimized_event_type: str
dropout_rate: int
batch_size: abacusai.api_class.enums.BatchSize
disable_transformer: bool
disable_gpu: bool
filter_history: bool
action_types_exclusion_days: Dict[str, float]
max_history_length: int
compute_rerank_metrics: bool
add_time_features: bool
disable_timestamp_scalar_features: bool
compute_session_metrics: bool
query_column: str
item_query_column: str
use_user_id_feature: bool
session_dedupe_mins: float
max_user_history_len_percentile: int
downsample_item_popularity_percentile: float
min_item_history: int
__post_init__()
class abacusai.api_class.RegressionTrainingConfig

Bases: TrainingConfig

Training config for the PREDICTIVE_MODELING problem type

Parameters:
  • objective (RegressionObjective) – Ranking scheme used to select final best model.

  • sort_objective (RegressionObjective) – Ranking scheme used to sort models on the metrics page.

  • tree_hpo_mode – (RegressionTreeHPOMode): Turning off Rapid Experimentation will take longer to train.

  • type_of_split (RegressionTypeOfSplit) – Type of data splitting into train/test (validation also).

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5% to 20% of your dataset to use as test data.

  • disable_test_val_fold (bool) – Do not create a TEST_VAL set. All records which would be part of the TEST_VAL fold otherwise, remain in the TEST fold.

  • k_fold_cross_validation (bool) – Use this to force k-fold cross validation bagging on or off.

  • num_cv_folds (int) – Specify the value of k in k-fold cross validation.

  • timestamp_based_splitting_column (str) – Timestamp column selected for splitting into test and train.

  • timestamp_based_splitting_method (RegressionTimeSplitMethod) – Method of selecting TEST set, top percentile wise or after a given timestamp.

  • test_splitting_timestamp (str) – Rows with timestamp greater than this will be considered to be in the test set.

  • sampling_unit_keys (List[str]) – Constrain train/test separation to partition a column.

  • test_row_indicator (str) – Column indicating which rows to use for training (TRAIN) and testing (TEST). Validation (VAL) can also be specified.

  • full_data_retraining (bool) – Train models separately with all the data.

  • rebalance_classes (bool) – Class weights are computed as the inverse of the class frequency from the training dataset when this option is selected as “Yes”. It is useful when the classes in the dataset are unbalanced. Re-balancing classes generally boosts recall at the cost of precision on rare classes.

  • rare_class_augmentation_threshold (float) – Augments any rare class whose relative frequency with respect to the most frequent class is less than this threshold. Default = 0.1 for classification problems with rare classes.

  • augmentation_strategy (RegressionAugmentationStrategy) – Strategy to deal with class imbalance and data augmentation.

  • training_rows_downsample_ratio (float) – Uses this ratio to train on a sample of the dataset provided.

  • active_labels_column (str) – Specify a column to use as the active columns in a multi label setting.

  • min_categorical_count (int) – Minimum threshold to consider a value different from the unknown placeholder.

  • sample_weight (str) – Specify a column to use as the weight of a sample for training and eval.

  • numeric_clipping_percentile (float) – Uses this option to clip the top and bottom x percentile of numeric feature columns where x is the value of this option.

  • target_transform (RegressionTargetTransform) – Specify a transform (e.g. log, quantile) to apply to the target variable.

  • ignore_datetime_features (bool) – Remove all datetime features from the model. Useful while generalizing to different time periods.

  • max_text_words (int) – Maximum number of words to use from text fields.

  • perform_feature_selection (bool) – If enabled, additional algorithms which support feature selection as a pretraining step will be trained separately with the selected subset of features. The details about their selected features can be found in their respective logs.

  • feature_selection_intensity (int) – This determines the strictness with which features will be filtered out. 1 being very lenient (more features kept), 100 being very strict.

  • batch_size (BatchSize) – Batch size.

  • dropout_rate (int) – Dropout percentage rate.

  • pretrained_model_name (str) – Enable algorithms which process text using pretrained multilingual NLP models.

  • is_multilingual (bool) – Enable algorithms which process text using pretrained multilingual NLP models.

  • loss_function (RegressionLossFunction) – Loss function to be used as objective for model training.

  • loss_parameters (str) – Loss function params in format <key>=<value>;<key>=<value>;…..

  • target_encode_categoricals (bool) – Use this to turn target encoding on categorical features on or off.

  • drop_original_categoricals (bool) – This option helps us choose whether to also feed the original label encoded categorical columns to the mdoels along with their target encoded versions.

  • monotonically_increasing_features (List[str]) – Constrain the model such that it behaves as if the target feature is monotonically increasing with the selected features

  • monotonically_decreasing_features (List[str]) – Constrain the model such that it behaves as if the target feature is monotonically decreasing with the selected features

  • data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.

  • custom_loss_functions (List[str]) – Registered custom losses available for selection.

  • custom_metrics (List[str]) – Registered custom metrics available for selection.

  • partial_dependence_analysis (PartialDependenceAnalysis) – Specify whether to run partial dependence plots for all features or only some features.

objective: abacusai.api_class.enums.RegressionObjective
sort_objective: abacusai.api_class.enums.RegressionObjective
tree_hpo_mode: abacusai.api_class.enums.RegressionTreeHPOMode
partial_dependence_analysis: abacusai.api_class.enums.PartialDependenceAnalysis
type_of_split: abacusai.api_class.enums.RegressionTypeOfSplit
test_split: int
disable_test_val_fold: bool
k_fold_cross_validation: bool
num_cv_folds: int
timestamp_based_splitting_column: str
timestamp_based_splitting_method: abacusai.api_class.enums.RegressionTimeSplitMethod
test_splitting_timestamp: str
sampling_unit_keys: List[str]
test_row_indicator: str
full_data_retraining: bool
rebalance_classes: bool
rare_class_augmentation_threshold: float
augmentation_strategy: abacusai.api_class.enums.RegressionAugmentationStrategy
training_rows_downsample_ratio: float
active_labels_column: str
min_categorical_count: int
sample_weight: str
numeric_clipping_percentile: float
target_transform: abacusai.api_class.enums.RegressionTargetTransform
ignore_datetime_features: bool
max_text_words: int
perform_feature_selection: bool
feature_selection_intensity: int
batch_size: abacusai.api_class.enums.BatchSize
dropout_rate: int
pretrained_model_name: str
is_multilingual: bool
loss_function: abacusai.api_class.enums.RegressionLossFunction
loss_parameters: str
target_encode_categoricals: bool
drop_original_categoricals: bool
monotonically_increasing_features: List[str]
monotonically_decreasing_features: List[str]
data_split_feature_group_table_name: str
custom_loss_functions: List[str]
custom_metrics: List[str]
__post_init__()
class abacusai.api_class.ForecastingTrainingConfig

Bases: TrainingConfig

Training config for the FORECASTING problem type

Parameters:
  • prediction_length (int) – How many timesteps in the future to predict.

  • objective (ForecastingObjective) – Ranking scheme used to select final best model.

  • sort_objective (ForecastingObjective) – Ranking scheme used to sort models on the metrics page.

  • forecast_frequency (ForecastingFrequency) – Forecast frequency.

  • probability_quantiles (List[float]) – Prediction quantiles.

  • force_prediction_length (int) – Force length of test window to be the same as prediction length.

  • filter_items (bool) – Filter items with small history and volume.

  • enable_feature_selection (bool) – Enable feature selection.

  • enable_padding (bool) – Pad series to the max_date of the dataset

  • enable_cold_start (bool) – Enable cold start forecasting by training/predicting for zero history items.

  • enable_multiple_backtests (bool) – Whether to enable multiple backtesting or not.

  • num_backtesting_windows (int) – Total backtesting windows to use for the training.

  • backtesting_window_step_size (int) – Use this step size to shift backtesting windows for model training.

  • full_data_retraining (bool) – Train models separately with all the data.

  • additional_forecast_keys – List[str]: List of categoricals in timeseries that can act as multi-identifier.

  • experimentation_mode (ExperimentationMode) – Selecting Thorough Experimentation will take longer to train.

  • type_of_split (ForecastingDataSplitType) – Type of data splitting into train/test.

  • test_by_item (bool) – Partition train/test data by item rather than time if true.

  • test_start (str) – Limit training data to dates before the given test start.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5% to 20% of your dataset to use as test data.

  • loss_function (ForecastingLossFunction) – Loss function for training neural network.

  • underprediction_weight (float) – Weight for underpredictions

  • disable_networks_without_analytic_quantiles (bool) – Disable neural networks, which quantile functions do not have analytic expressions (e.g, mixture models)

  • initial_learning_rate (float) – Initial learning rate.

  • l2_regularization_factor (float) – L2 regularization factor.

  • dropout_rate (int) – Dropout percentage rate.

  • recurrent_layers (int) – Number of recurrent layers to stack in network.

  • recurrent_units (int) – Number of units in each recurrent layer.

  • convolutional_layers (int) – Number of convolutional layers to stack on top of recurrent layers in network.

  • convolution_filters (int) – Number of filters in each convolution.

  • local_scaling_mode (ForecastingLocalScaling) – Options to make NN inputs stationary in high dynamic range datasets.

  • zero_predictor (bool) – Include subnetwork to classify points where target equals zero.

  • skip_missing (bool) – Make the RNN ignore missing entries rather instead of processing them.

  • batch_size (ForecastingBatchSize) – Batch size.

  • batch_renormalization (bool) – Enable batch renormalization between layers.

  • history_length (int) – While training, how much history to consider.

  • prediction_step_size (int) – Number of future periods to include in objective for each training sample.

  • training_point_overlap (float) – Amount of overlap to allow between training samples.

  • max_scale_context (int) – Maximum context to use for local scaling.

  • quantiles_extension_method (ForecastingQuanitlesExtensionMethod) – Quantile extension method

  • number_of_samples (int) – Number of samples for ancestral simulation

  • symmetrize_quantiles (bool) – Force symmetric quantiles (like in Gaussian distribution)

  • use_log_transforms (bool) – Apply logarithmic transformations to input data.

  • smooth_history (float) – Smooth (low pass filter) the timeseries.

  • local_scale_target (bool) – Using per training/prediction window target scaling.

  • use_clipping (bool) – Apply clipping to input data to stabilize the training.

  • timeseries_weight_column (str) – If set, we use the values in this column from timeseries data to assign time dependent item weights during training and evaluation.

  • item_attributes_weight_column (str) – If set, we use the values in this column from item attributes data to assign weights to items during training and evaluation.

  • use_timeseries_weights_in_objective (bool) – If True, we include weights from column set as “TIMESERIES WEIGHT COLUMN” in objective functions.

  • use_item_weights_in_objective (bool) – If True, we include weights from column set as “ITEM ATTRIBUTES WEIGHT COLUMN” in objective functions.

  • skip_timeseries_weight_scaling (bool) – If True, we will avoid normalizing the weights.

  • timeseries_loss_weight_column (str) – Use value in this column to weight the loss while training.

  • use_item_id (bool) – Include a feature to indicate the item being forecast.

  • use_all_item_totals (bool) – Include as input total target across items.

  • handle_zeros_as_missing_values (bool) – If True, handle zero values in demand as missing data.

  • datetime_holiday_calendars (List[HolidayCalendars]) – Holiday calendars to augment training with.

  • fill_missing_values (List[dict]) – Strategy for filling in missing values.

  • enable_clustering (bool) – Enable clustering in forecasting.

  • data_split_feature_group_table_name (str) – Specify the table name of the feature group to export training data with the fold column.

  • custom_loss_functions (List[str]) – Registered custom losses available for selection.

  • custom_metrics (List[str]) – Registered custom metrics available for selection.

  • return_fractional_forecasts – Use this to return fractional forecast values while prediction

prediction_length: int
objective: abacusai.api_class.enums.ForecastingObjective
sort_objective: abacusai.api_class.enums.ForecastingObjective
forecast_frequency: abacusai.api_class.enums.ForecastingFrequency
probability_quantiles: List[float]
force_prediction_length: bool
filter_items: bool
enable_feature_selection: bool
enable_padding: bool
enable_cold_start: bool
enable_multiple_backtests: bool
num_backtesting_windows: int
backtesting_window_step_size: int
full_data_retraining: bool
additional_forecast_keys: List[str]
experimentation_mode: abacusai.api_class.enums.ExperimentationMode
type_of_split: abacusai.api_class.enums.ForecastingDataSplitType
test_by_item: bool
test_start: str
test_split: int
loss_function: abacusai.api_class.enums.ForecastingLossFunction
underprediction_weight: float
disable_networks_without_analytic_quantiles: bool
initial_learning_rate: float
l2_regularization_factor: float
dropout_rate: int
recurrent_layers: int
recurrent_units: int
convolutional_layers: int
convolution_filters: int
local_scaling_mode: abacusai.api_class.enums.ForecastingLocalScaling
zero_predictor: bool
skip_missing: bool
batch_size: abacusai.api_class.enums.BatchSize
batch_renormalization: bool
history_length: int
prediction_step_size: int
training_point_overlap: float
max_scale_context: int
quantiles_extension_method: abacusai.api_class.enums.ForecastingQuanitlesExtensionMethod
number_of_samples: int
symmetrize_quantiles: bool
use_log_transforms: bool
smooth_history: float
local_scale_target: bool
use_clipping: bool
timeseries_weight_column: str
item_attributes_weight_column: str
use_timeseries_weights_in_objective: bool
use_item_weights_in_objective: bool
skip_timeseries_weight_scaling: bool
timeseries_loss_weight_column: str
use_item_id: bool
use_all_item_totals: bool
handle_zeros_as_missing_values: bool
datetime_holiday_calendars: List[abacusai.api_class.enums.HolidayCalendars]
fill_missing_values: List[dict]
enable_clustering: bool
data_split_feature_group_table_name: str
custom_loss_functions: List[str]
custom_metrics: List[str]
return_fractional_forecasts: bool
__post_init__()
class abacusai.api_class.NamedEntityExtractionTrainingConfig

Bases: TrainingConfig

Training config for the NAMED_ENTITY_EXTRACTION problem type

Parameters:
  • objective (NERObjective) – Ranking scheme used to select final best model.

  • sort_objective (NERObjective) – Ranking scheme used to sort models on the metrics page.

  • llm_for_ner (NERForLLM) – LLM to use for NER from among available LLM

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • test_row_indicator (str) – Column indicating which rows to use for training (TRAIN) and testing (TEST).

  • active_labels_column (str) – Entities that have been marked in a particular text

  • document_format (NLPDocumentFormat) – Format of the input documents.

  • include_longformer (bool) – Whether to include the longformer model.

  • minimum_bounding_box_overlap_ratio (float) – Tokens are considered to belong to annotation if the user bounding box is provided and ratio of (token_bounding_box ∩ annotation_bounding_box) / token_bounding_area is greater than the provided value.

  • save_predicted_pdf (bool) – Whether to save predicted PDF documents

  • enhanced_ocr (bool) – Enhanced text extraction from predicted digital documents

objective: abacusai.api_class.enums.NERObjective
sort_objective: abacusai.api_class.enums.NERObjective
llm_for_ner: abacusai.api_class.enums.LLMName
test_split: int
test_row_indicator: str
active_labels_column: str
document_format: abacusai.api_class.enums.NLPDocumentFormat
minimum_bounding_box_overlap_ratio: float = 0.0
save_predicted_pdf: bool = True
enhanced_ocr: bool = False
__post_init__()
class abacusai.api_class.NaturalLanguageSearchTrainingConfig

Bases: TrainingConfig

Training config for the NATURAL_LANGUAGE_SEARCH problem type

Parameters:
  • abacus_internal_model (bool) – Use a Abacus.AI LLM to answer questions about your data without using any external APIs

  • num_completion_tokens (int) – Default for maximum number of tokens for chat answers. Reducing this will get faster responses which are more succinct

  • larger_embeddings (bool) – Use a higher dimension embedding model.

  • search_chunk_size (int) – Chunk size for indexing the documents.

  • chunk_overlap_fraction (float) – Overlap in chunks while indexing the documents.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • index_fraction (float) – Fraction of the chunk to use for indexing.

abacus_internal_model: bool
num_completion_tokens: int
larger_embeddings: bool
search_chunk_size: int
index_fraction: float
chunk_overlap_fraction: float
__post_init__()
class abacusai.api_class.ChatLLMTrainingConfig

Bases: TrainingConfig

Training config for the CHAT_LLM problem type

Parameters:
  • document_retrievers (List[str]) – List of document retriever names to use for the feature stores this model was trained with.

  • num_completion_tokens (int) – Default for maximum number of tokens for chat answers. Reducing this will get faster responses which are more succinct

  • temperature (float) – The generative LLM temperature

  • metadata_columns (list) – Include the metadata column values in the retrieved search results.

  • include_general_knowledge (bool) – Allow the LLM to rely not just on search results, but to fall back on general knowledge.

  • behavior_instructions (str) – Customize the overall role instructions for the LLM.

  • response_instructions (str) – Customize instructions for what the LLM responses should look like.

  • lookup_rewrite_instructions (str) – Instructions for a LLM call to automatically generate filter expressions on document metadata to retrieve relevant documents for the conversation.

  • max_search_results (int) – Maximum number of search results in the retrieval augmentation step. If we know that the questions are likely to have snippets which are easily matched in the documents, then a lower number will help with accuracy.

  • data_feature_group_ids – (List[str]): List of feature group IDs to use to possibly query for the ChatLLM. The created ChatLLM is commonly referred to as DataLLM.

  • data_prompt_context (str) – Prompt context for the data feature group IDs.

  • hide_generated_sql (bool) – When running data queries, hides the generated SQL in the response and will just return the table.

  • disable_data_summarization (bool) – After executing a query summarize the reponse and reply back with only the table and query run.

  • search_score_cutoff (float) – Minimum search score to consider a document as a valid search result.

  • database_connector_id (str) – Database connector ID to use for the ChatLLM.

  • database_connector_tables (List[str]) – List of tables to use from the database connector for the ChatLLM.

  • enable_code_execution (bool) – Enable code execution in the ChatLLM.

document_retrievers: List[str]
num_completion_tokens: int
temperature: float
metadata_columns: list
include_general_knowledge: bool
behavior_instructions: str
response_instructions: str
lookup_rewrite_instructions: str
max_search_results: int
data_feature_group_ids: List[str]
data_prompt_context: str
hide_generated_sql: bool
disable_data_summarization: bool
search_score_cutoff: float
database_connector_id: str
database_connector_tables: List[str]
enable_code_execution: bool
__post_init__()
class abacusai.api_class.SentenceBoundaryDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTENCE_BOUNDARY_DETECTION problem type

Parameters:
  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • dropout_rate (float) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
__post_init__()
class abacusai.api_class.SentimentDetectionTrainingConfig

Bases: TrainingConfig

Training config for the SENTIMENT_DETECTION problem type

Parameters:
  • sentiment_type (SentimentType) – Type of sentiment to detect.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • dropout_rate (float) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

  • compute_metrics (bool) – Whether to compute metrics.

sentiment_type: abacusai.api_class.enums.SentimentType
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
compute_metrics: bool
__post_init__()
class abacusai.api_class.DocumentClassificationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_CLASSIFICATION problem type

Parameters:
  • zero_shot_hypotheses (List[str]) – Zero shot hypotheses. Example text: ‘This text is about pricing’.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • dropout_rate (float) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

zero_shot_hypotheses: List[str]
test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
__post_init__()
class abacusai.api_class.DocumentSummarizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_SUMMARIZATION problem type

Parameters:
  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • dropout_rate (float) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
__post_init__()
class abacusai.api_class.DocumentVisualizationTrainingConfig

Bases: TrainingConfig

Training config for the DOCUMENT_VISUALIZATION problem type

Parameters:
  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • dropout_rate (float) – Dropout rate for neural network.

  • batch_size (BatchSize) – Batch size for neural network.

test_split: int
dropout_rate: float
batch_size: abacusai.api_class.enums.BatchSize
__post_init__()
class abacusai.api_class.ClusteringTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING problem type

Parameters:

num_clusters_selection (int) – Number of clusters. If None, will be selected automatically.

num_clusters_selection: int
__post_init__()
class abacusai.api_class.ClusteringTimeseriesTrainingConfig

Bases: TrainingConfig

Training config for the CLUSTERING_TIMESERIES problem type

Parameters:
  • num_clusters_selection (int) – Number of clusters. If None, will be selected automatically.

  • imputation (ClusteringImputationMethod) – Imputation method for missing values.

num_clusters_selection: int
imputation: abacusai.api_class.enums.ClusteringImputationMethod
__post_init__()
class abacusai.api_class.EventAnomalyTrainingConfig

Bases: TrainingConfig

Training config for the EVENT_ANOMALY problem type

Parameters:

anomaly_fraction (float) – The fraction of the dataset to classify as anomalous, between 0 and 0.5

anomaly_fraction: float
__post_init__()
class abacusai.api_class.TimeseriesAnomalyTrainingConfig

Bases: TrainingConfig

Training config for the TS_ANOMALY problem type

Parameters:
  • type_of_split (TimeseriesAnomalyDataSplitType) – Type of data splitting into train/test.

  • test_start (str) – Limit training data to dates before the given test start.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • fill_missing_values (List[dict]) – strategies to fill missing values and missing timestamps

  • handle_zeros_as_missing_values (bool) – If True, handle zero values in numeric columns as missing data

  • timeseries_frequency (str) – set this to control frequency of filling missing values

  • min_samples_in_normal_region (int) – Adjust this to fine-tune the number of anomalies to be identified.

  • anomaly_type (TimeseriesAnomalyTypeOfAnomaly) – select what kind of peaks to detect as anomalies

  • hyperparameter_calculation_with_heuristics (TimeseriesAnomalyUseHeuristic) – Enable heuristic calculation to get hyperparameters for the model

  • threshold_score (float) – Threshold score for anomaly detection

type_of_split: abacusai.api_class.enums.TimeseriesAnomalyDataSplitType
test_start: str
test_split: int
fill_missing_values: List[dict]
handle_zeros_as_missing_values: bool
timeseries_frequency: str
min_samples_in_normal_region: int
anomaly_type: abacusai.api_class.enums.TimeseriesAnomalyTypeOfAnomaly
hyperparameter_calculation_with_heuristics: abacusai.api_class.enums.TimeseriesAnomalyUseHeuristic
threshold_score: float
__post_init__()
class abacusai.api_class.CumulativeForecastingTrainingConfig

Bases: TrainingConfig

Training config for the CUMULATIVE_FORECASTING problem type

Parameters:
  • test_split (int) – Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.

  • historical_frequency (str) – Forecast frequency

  • cumulative_prediction_lengths (List[int]) – List of Cumulative Prediction Frequencies. Each prediction length must be between 1 and 365.

  • skip_input_transform (bool) – Avoid doing numeric scaling transformations on the input.

  • skip_target_transform (bool) – Avoid doing numeric scaling transformations on the target.

  • predict_residuals (bool) – Predict residuals instead of totals at each prediction step.

test_split: int
historical_frequency: str
cumulative_prediction_lengths: List[int]
skip_input_transform: bool
skip_target_transform: bool
predict_residuals: bool
__post_init__()
class abacusai.api_class.ThemeAnalysisTrainingConfig

Bases: TrainingConfig

Training config for the THEME ANALYSIS problem type

__post_init__()
class abacusai.api_class.AIAgentTrainingConfig

Bases: TrainingConfig

Training config for the AI_AGENT problem type

Parameters:
  • description (str) – Description of the agent function.

  • enable_binary_input (bool) – If True, the agent will be able to accept binary data as inputs.

  • agent_input_schema (dict) – Schema for the agent input.

  • agent_output_schema (dict) – Schema for the agent output.

description: str
enable_binary_input: bool
agent_input_schema: dict
agent_output_schema: dict
__post_init__()
class abacusai.api_class.CustomTrainedModelTrainingConfig

Bases: TrainingConfig

Training config for the CUSTOM_TRAINED_MODEL problem type

Parameters:
  • max_catalog_size (int) – Maximum expected catalog size.

  • max_dimension (int) – Maximum expected dimension of the catalog.

  • index_output_path (str) – Fully qualified cloud location (GCS, S3, etc) to export snapshots of the embedding to.

  • docker_image_uri (str) – Docker image URI.

  • service_port (int) – Service port.

max_catalog_size: int
max_dimension: int
index_output_path: str
docker_image_uri: str
service_port: int
__post_init__()
class abacusai.api_class.CustomAlgorithmTrainingConfig

Bases: TrainingConfig

Training config for the CUSTOM_ALGORITHM problem type

Parameters:
  • train_function_name (str) – The name of the train function.

  • predict_many_function_name (str) – The name of the predict many function.

  • training_input_tables (List[str]) – List of tables to use for training.

  • predict_function_name (str) – Optional name of the predict function if the predict many function is not given.

  • train_module_name (str) – The name of the train module - only relevant if model is being uploaded from a zip file or github repositoty.

  • predict_module_name (str) – The name of the predict module - only relevant if model is being uploaded from a zip file or github repositoty.

  • test_split (int) – Percent of dataset to use for test data. We support using a range between 6% to 20% of your dataset to use as test data.

train_function_name: str
predict_many_function_name: str
training_input_tables: List[str]
predict_function_name: str
train_module_name: str
predict_module_name: str
test_split: int
__post_init__()
class abacusai.api_class.OptimizationTrainingConfig

Bases: TrainingConfig

Training config for the OPTIMIZATION problem type

Parameters:
  • solve_time_limit (float) – The maximum time in seconds to spend solving the problem. Accepts values between 0 and 86400.

  • optimality_gap_limit (float) – The stopping optimality gap limit. Optimality gap is fractional difference between the best known solution and the best possible solution. Accepts values between 0 and 1.

solve_time_limit: float
optimality_gap_limit: float
__post_init__()
class abacusai.api_class._TrainingConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'problem_type'
config_class_map
class abacusai.api_class.DeployableAlgorithm

Bases: abacusai.api_class.abstract.ApiClass

Algorithm that can be deployed to a model.

Parameters:
  • algorithm (str) – ID of the algorithm.

  • name (str) – Name of the algorithm.

  • only_offline_deployable (bool) – Whether the algorithm can only be deployed offline.

  • trained_model_types (List[dict]) – List of trained model types.

algorithm: str
name: str
only_offline_deployable: bool
trained_model_types: List[dict]
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.StdDevThresholdType

Bases: ApiEnum

Generic enumeration.

Derive from this class to define new enumerations.

ABSOLUTE = 'ABSOLUTE'
PERCENTILE = 'PERCENTILE'
STDDEV = 'STDDEV'
class abacusai.api_class.TimeWindowConfig

Bases: abacusai.api_class.abstract.ApiClass

Time Window Configuration

Parameters:
  • window_duration (int) – The duration of the window.

  • window_from_start (bool) – Whether the window should be from the start of the time series.

window_duration: int
window_from_start: bool
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ForecastingMonitorConfig

Bases: abacusai.api_class.abstract.ApiClass

Forecasting Monitor Configuration

Parameters:
  • id_column (str) – The name of the column that contains the unique identifier for the time series.

  • timestamp_column (str) – The name of the column that contains the timestamp for the time series.

  • target_column (str) – The name of the column that contains the target value for the time series.

  • start_time (str) – The start time of the time series data.

  • end_time (str) – The end time of the time series data.

  • window_config (TimeWindowConfig) – The windowing configuration for the time series data.

id_column: str
timestamp_column: str
target_column: str
start_time: str
end_time: str
window_config: TimeWindowConfig
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.StdDevThreshold

Bases: abacusai.api_class.abstract.ApiClass

Std Dev Threshold types

Parameters:
  • threshold_type (StdDevThresholdType) – Type of threshold to apply to the item attributes.

  • value (float) – Value to use for the threshold.

threshold_type: abacusai.api_class.enums.StdDevThresholdType
value: float
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ItemAttributesStdDevThreshold

Bases: abacusai.api_class.abstract.ApiClass

Item Attributes Std Dev Threshold for Monitor Alerts

Parameters:
lower_bound: StdDevThreshold
upper_bound: StdDevThreshold
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.AlertConditionConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for alert condition configs

alert_type: abacusai.api_class.enums.MonitorAlertType
classmethod _get_builder()
class abacusai.api_class.AccuracyBelowThresholdConditionConfig

Bases: AlertConditionConfig

Accuracy Below Threshold Condition Config for Monitor Alerts

Parameters:

threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

threshold: float
__post_init__()
class abacusai.api_class.FeatureDriftConditionConfig

Bases: AlertConditionConfig

Feature Drift Condition Config for Monitor Alerts

Parameters:
  • feature_drift_type (FeatureDriftType) – Feature drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.

  • threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

  • minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.

  • feature_names (List[str]) – List of feature names to monitor for this alert.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType
threshold: float
minimum_violations: int
feature_names: List[str]
__post_init__()
class abacusai.api_class.TargetDriftConditionConfig

Bases: AlertConditionConfig

Target Drift Condition Config for Monitor Alerts

Parameters:
  • feature_drift_type (FeatureDriftType) – Target drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.

  • threshold (float) – Threshold for when to consider the target column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType
threshold: float
__post_init__()
class abacusai.api_class.HistoryLengthDriftConditionConfig

Bases: AlertConditionConfig

History Length Drift Condition Config for Monitor Alerts

Parameters:
  • feature_drift_type (FeatureDriftType) – History length drift type to apply the threshold on to determine whether the history length has drifted significantly enough to be a violation.

  • threshold (float) – Threshold for when to consider the history length to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

feature_drift_type: abacusai.api_class.enums.FeatureDriftType
threshold: float
__post_init__()
class abacusai.api_class.DataIntegrityViolationConditionConfig

Bases: AlertConditionConfig

Data Integrity Violation Condition Config for Monitor Alerts

Parameters:
  • data_integrity_type (DataIntegrityViolationType) – This option selects the data integrity violations to monitor for this alert.

  • minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.

data_integrity_type: abacusai.api_class.enums.DataIntegrityViolationType
minimum_violations: int
__post_init__()
class abacusai.api_class.BiasViolationConditionConfig

Bases: AlertConditionConfig

Bias Violation Condition Config for Monitor Alerts

Parameters:
  • bias_type (BiasType) – This option selects the bias metric to monitor for this alert.

  • threshold (float) – Threshold for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

  • minimum_violations (int) – Number of columns that must exceed the specified threshold to trigger an alert.

bias_type: abacusai.api_class.enums.BiasType
threshold: float
minimum_violations: int
__post_init__()
class abacusai.api_class.PredictionCountConditionConfig

Bases: AlertConditionConfig

Deployment Prediction Condition Config for Deployment Alerts. By default we monitor if predictions made over a time window has reduced significantly. :param threshold: Threshold for when to consider to be a violation. Negative means alert on reduction, positive means alert on increase. :type threshold: float :param aggregation_window: Time window to aggregate the predictions over, e.g. 1h, 10m. Only h(hour), m(minute) and s(second) are supported. :type aggregation_window: str :param aggregation_type: Aggregation type to use for the aggregation window, e.g. sum, avg. :type aggregation_type: str

threshold: float
aggregation_window: str
aggregation_type: str
__post_init__()
class abacusai.api_class._AlertConditionConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'alert_type'
config_class_key_value_camel_case = True
config_class_map
class abacusai.api_class.AlertActionConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for alert action configs

action_type: abacusai.api_class.enums.AlertActionType
classmethod _get_builder()
class abacusai.api_class.EmailActionConfig

Bases: AlertActionConfig

Email Action Config for Monitor Alerts

Parameters:
  • email_recipients (List[str]) – List of email addresses to send the alert to.

  • email_body (str) – Body of the email to send.

email_recipients: List[str]
email_body: str
__post_init__()
class abacusai.api_class._AlertActionConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'action_type'
config_class_map
class abacusai.api_class.MonitorThresholdConfig

Bases: abacusai.api_class.abstract.ApiClass

Monitor Threshold Config for Monitor Alerts

Parameters:
  • drift_type (FeatureDriftType) – Feature drift type to apply the threshold on to determine whether a column has drifted significantly enough to be a violation.

  • threshold_config (ThresholdConfigs) – Thresholds for when to consider a column to be in violation. The alert will only fire when the drift value is strictly greater than the threshold.

drift_type: abacusai.api_class.enums.FeatureDriftType
at_risk_threshold: float
severely_drifting_threshold: float
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.FeatureMappingConfig

Bases: abacusai.api_class.abstract.ApiClass

Feature mapping configuration for a feature group type.

Parameters:
  • feature_name (str) – The name of the feature in the feature group.

  • feature_mapping (str) – The desired feature mapping for the feature.

  • nested_feature_name (str) – The name of the nested feature in the feature group.

feature_name: str
feature_mapping: str
nested_feature_name: str
class abacusai.api_class.ProjectFeatureGroupTypeMappingsConfig

Bases: abacusai.api_class.abstract.ApiClass

Project feature group type mappings.

Parameters:
  • feature_group_id (str) – The unique identifier for the feature group.

  • feature_group_type (str) – The feature group type.

  • feature_mappings (List[FeatureMappingConfig]) – The feature mappings for the feature group.

feature_group_id: str
feature_group_type: str
feature_mappings: List[FeatureMappingConfig]
classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class.PythonFunctionArgument

Bases: abacusai.api_class.abstract.ApiClass

A config class for python function arguments

Parameters:
  • variable_type (PythonFunctionArgumentType) – The type of the python function argument

  • name (str) – The name of the python function variable

  • is_required (bool) – Whether the argument is required

  • value (Any) – The value of the argument

  • pipeline_variable (str) – The name of the pipeline variable to use as the value

variable_type: abacusai.api_class.enums.PythonFunctionArgumentType
name: str
is_required: bool
value: Any
pipeline_variable: str
class abacusai.api_class.OutputVariableMapping

Bases: abacusai.api_class.abstract.ApiClass

A config class for python function arguments

Parameters:
variable_type: abacusai.api_class.enums.PythonFunctionOutputArgumentType
name: str
class abacusai.api_class.ApiClass

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

_upper_snake_case_keys: bool
_support_kwargs: bool
__post_init__()
classmethod _get_builder()
__str__()

Return str(self).

_repr_html_()
__getitem__(item)
Parameters:

item (str) –

__setitem__(item, value)
Parameters:
  • item (str) –

  • value (Any) –

_unset_item(item)
Parameters:

item (str) –

get(item, default=None)
Parameters:
  • item (str) –

  • default (Any) –

pop(item, default=NotImplemented)
Parameters:
  • item (str) –

  • default (Any) –

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)
Parameters:

input_dict (dict) –

class abacusai.api_class._ApiClassFactory

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key
config_class_map
classmethod from_dict(config)
Parameters:

config (dict) –

Return type:

ApiClass

class abacusai.api_class.FeatureGroupExportConfig

Bases: abacusai.api_class.abstract.ApiClass

An abstract class for feature group exports.

connector_type: abacusai.api_class.enums.ConnectorType
classmethod _get_builder()
class abacusai.api_class.FileConnectorExportConfig

Bases: FeatureGroupExportConfig

File connector export config for feature groups

Parameters:
  • location (str) – The location to export the feature group to

  • export_file_format (str) – The file format to export the feature group to

location: str
export_file_format: str
__post_init__()
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class.DatabaseConnectorExportConfig

Bases: FeatureGroupExportConfig

Database connector export config for feature groups

Parameters:
  • database_connector_id (str) – The ID of the database connector to export the feature group to

  • mode (str) – The mode to export the feature group in

  • object_name (str) – The name of the object to export the feature group to

  • id_column (str) – The name of the ID column

  • additional_id_columns (List[str]) – Additional ID columns

  • data_columns (Dict[str, str]) – The data columns to export the feature group to

database_connector_id: str
mode: str
object_name: str
id_column: str
additional_id_columns: List[str]
data_columns: Dict[str, str]
__post_init__()
to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

class abacusai.api_class._FeatureGroupExportConfigFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

Helper class that provides a standard way to create an ABC using inheritance.

config_abstract_class
config_class_key = 'connector_type'
config_class_map