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

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

kwargs: dict

problem_type: abacusai.api_class.enums.ProblemType

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

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

create_monitor: bool

__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

__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

number_of_items: int

item_attributes_to_include_in_the_result: list

score_field: str

__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

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

create_monitor: bool

__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_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.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

explode_output: bool

__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

analysis_frequency: str

start_date: str

analysis_days: int

__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

__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

create_monitor: bool

__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

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

config_abstract_class

config_class_key = 'problem_type'

config_class_map