abacusai.api_class.batch_prediction

Module Contents

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

_BatchPredictionArgsFactory

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

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.

  • 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) – 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
automate_monitoring: 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.

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

for_eval: bool
verbose_predictions: 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.

  • automate_monitoring (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
automate_monitoring: 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.

  • automate_monitoring (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
automate_monitoring: bool
__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