abacusai.api_class.deployment

Classes

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

Bases: abc.ABC

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

_upper_snake_case_keys: bool

_support_kwargs: bool

__post_init__()

classmethod _get_builder()

__str__()

Return str(self).

_repr_html_()

__getitem__(item)

Parameters:

item (str)

__setitem__(item, value)

Parameters:

• item (str)

• value (Any)

_unset_item(item)

Parameters:

item (str)

get(item, default=None)

Parameters:

• item (str)

• default (Any)

pop(item, default=NotImplemented)

Parameters:

• item (str)

• default (Any)

to_dict()

Standardizes converting an ApiClass to dictionary. Keys of response dictionary are converted to camel case. This also validates the fields ( type, value, etc ) received in the dictionary.

classmethod from_dict(input_dict)

Parameters:

input_dict (dict)

class abacusai.api_class.deployment._ApiClassFactory

Bases: abc.ABC

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

config_abstract_class = None

config_class_key = None

config_class_map

classmethod from_dict(config)

Parameters:

config (dict)

Return type:

ApiClass

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