abacusai.api_class.deployment

Classes

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

Module Contents

class abacusai.api_class.deployment.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.deployment.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.deployment.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.deployment.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.deployment.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.deployment.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.deployment.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.deployment.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.deployment.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.deployment._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