abacusai.api_class.batch_prediction

Classes

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

AIAgentBatchPredictionArgs

Batch Prediction Config for the AIAgents problem type

_BatchPredictionArgsFactory

Module Contents

class abacusai.api_class.batch_prediction.BatchPredictionArgs

Bases: abacusai.api_class.abstract.ApiClass

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

_support_kwargs: bool = True
kwargs: dict
problem_type: abacusai.api_class.enums.ProblemType = None
classmethod _get_builder()
class abacusai.api_class.batch_prediction.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. Accepts timestamp integers and strings in many standard formats such as YYYY-MM-DD, YYYY-MM-DD HH:MM:SS, or YYYY-MM-DDTHH:MM:SS. If not specified, the prediction start date will be automatically defined.

  • 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.

  • create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool = None
predictions_start_date: str = None
use_prediction_offset: bool = None
start_date_offset: int = None
forecasting_horizon: int = None
item_attributes_to_include_in_the_result: list = None
explain_predictions: bool = None
create_monitor: bool = None
__post_init__()
class abacusai.api_class.batch_prediction.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.

for_eval: bool = None
__post_init__()
class abacusai.api_class.batch_prediction.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 = None
number_of_items: int = None
item_attributes_to_include_in_the_result: list = None
score_field: str = None
__post_init__()
class abacusai.api_class.batch_prediction.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.

  • create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool = None
explainer_type: abacusai.api_class.enums.ExplainerType = None
number_of_samples_to_use_for_explainer: int = None
include_multi_class_explanations: bool = None
features_considered_constant_for_explanations: str = None
importance_of_records_in_nested_columns: str = None
explanation_filter_lower_bound: float = None
explanation_filter_upper_bound: float = None
explanation_filter_label: str = None
output_columns: list = None
explain_predictions: bool = None
create_monitor: bool = None
__post_init__()
class abacusai.api_class.batch_prediction.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_input_location (str) – The input location for the files.

  • 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 = None
files_input_location: str = None
files_output_location_prefix: str = None
channel_id_to_label_map: str = None
__post_init__()
class abacusai.api_class.batch_prediction.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 = None
explode_output: bool = None
__post_init__()
class abacusai.api_class.batch_prediction.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 = None
analysis_frequency: str = None
start_date: str = None
analysis_days: int = None
__post_init__()
class abacusai.api_class.batch_prediction.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 = None
__post_init__()
class abacusai.api_class.batch_prediction.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.

  • create_monitor (bool) – Controls whether to automatically create a monitor to calculate the drift each time the batch prediction is run. Defaults to true if not specified.

for_eval: bool = None
create_monitor: bool = None
__post_init__()
class abacusai.api_class.batch_prediction.AIAgentBatchPredictionArgs

Bases: BatchPredictionArgs

Batch Prediction Config for the AIAgents problem type

__post_init__()
class abacusai.api_class.batch_prediction._BatchPredictionArgsFactory

Bases: abacusai.api_class.abstract._ApiClassFactory

config_abstract_class
config_class_key = 'problem_type'
config_class_map