abacusai.api_class.batch_prediction
Module Contents
Classes
Helper class that provides a standard way to create an ABC using |
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Batch Prediction Config for the ANOMALY_DETECTION problem type |
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Batch Prediction Config for the ANOMALY_OUTLIERS problem type |
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Batch Prediction Config for the FORECASTING problem type |
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Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type |
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Batch Prediction Config for the PERSONALIZATION problem type |
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Batch Prediction Config for the PREDICTIVE_MODELING problem type |
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Batch Prediction Config for the PRETRAINED_MODELS problem type |
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Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type |
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Batch Prediction Config for the THEME_ANALYSIS problem type |
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Batch Prediction Config for the ChatLLM problem type |
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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
Helper class that provides a standard way to create an ABC using inheritance.
- problem_type: abacusai.api_class.enums.ProblemType
- classmethod _get_builder()
- class abacusai.api_class.batch_prediction.AnomalyDetectionBatchPredictionArgs
Bases:
BatchPredictionArgs
Batch Prediction Config for the ANOMALY_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.
prediction_time_endpoint (str) – The end point for predictions.
prediction_time_range (int) – Over what period of time should we make predictions (in seconds).
minimum_anomaly_score (int) – Exclude results with an anomaly score (1 in x event) below this threshold. Range: [1, 1_000_000_000_000].
summary_mode (bool) – Only show top anomalies per ID.
attach_raw_data (bool) – Return raw data along with anomalies.
small_batch (bool) – Size of batch data guaranteed to be small.
- __post_init__()
- class abacusai.api_class.batch_prediction.AnomalyOutliersBatchPredictionArgs
Bases:
BatchPredictionArgs
Batch Prediction Config for the ANOMALY_OUTLIERS problem type
- Parameters:
- __post_init__()
- 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, explain predictions for the forecast.
- __post_init__()
- class abacusai.api_class.batch_prediction.NamedEntityExtractionBatchPredictionArgs
Bases:
BatchPredictionArgs
Batch Prediction Config for the NAMED_ENTITY_EXTRACTION problem type
- Parameters:
- __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.
result_columns (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
- __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].
bound_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.
- explainer_type: abacusai.api_class.enums.ExplainerType
- __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.
- __post_init__()
- class abacusai.api_class.batch_prediction.SentenceBoundaryDetectionBatchPredictionArgs
Bases:
BatchPredictionArgs
Batch Prediction Config for the SENTENCE_BOUNDARY_DETECTION problem type
- Parameters:
- __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.
- __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.
- __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