actableai.models package

Subpackages

• actableai.models.autogluon package
  • Subpackages
    • actableai.models.autogluon.params package
      • Submodules
      • actableai.models.autogluon.params.ag_automm module
      • actableai.models.autogluon.params.base module
      • actableai.models.autogluon.params.cat module
      • actableai.models.autogluon.params.fastainn module
      • actableai.models.autogluon.params.fasttext module
      • actableai.models.autogluon.params.gbm module
      • actableai.models.autogluon.params.knn module
      • actableai.models.autogluon.params.lr module
      • actableai.models.autogluon.params.nn_base module
      • actableai.models.autogluon.params.nn_mxnet module
      • actableai.models.autogluon.params.nn_torch module
      • actableai.models.autogluon.params.polynomial_linear module
      • actableai.models.autogluon.params.rf module
      • actableai.models.autogluon.params.tabpfn module
      • actableai.models.autogluon.params.xgboost_base module
      • actableai.models.autogluon.params.xgboost_linear module
      • actableai.models.autogluon.params.xgboost_tree module
      • actableai.models.autogluon.params.xt module
      • Module contents
  • Submodules
  • actableai.models.autogluon.base module
  • actableai.models.autogluon.polynomial_linear module
  • Module contents
• actableai.models.causal_inference_estimators package
  • Submodules
  • actableai.models.causal_inference_estimators.base module
  • actableai.models.causal_inference_estimators.causal_forest_dml module
  • actableai.models.causal_inference_estimators.distance_matching module
  • actableai.models.causal_inference_estimators.kernel_dml module
  • actableai.models.causal_inference_estimators.linear_dml module
  • actableai.models.causal_inference_estimators.propensity_score_weighting module
  • actableai.models.causal_inference_estimators.sparse_linear_dml module
  • Module contents
• actableai.models.legacy package
  • Submodules
  • actableai.models.legacy.inference module
  • actableai.models.legacy.intervention module
  • Module contents
• actableai.models.serve package
  • Submodules
  • actableai.models.serve.inference module
  • actableai.models.serve.inference_head module
  • Module contents

Submodules

actableai.models.aai_predictor module

class actableai.models.aai_predictor.AAIInterventionalModel(intervention_predictor)

Bases: actableai.models.aai_predictor.AAIModel

class actableai.models.aai_predictor.AAIModel

Bases: object

model_version = 4

class actableai.models.aai_predictor.AAITabularModel(predictor, df_training, explainer=None)

Bases: actableai.models.aai_predictor.AAIModel

class actableai.models.aai_predictor.AAITabularModelInterventional(predictor, intervention_model, df_training)

Bases: actableai.models.aai_predictor.AAITabularModel

actableai.models.base module

class actableai.models.base.AAIBaseModel

Bases: Generic[actableai.models.base.InputDataType, actableai.models.base.OutputDataType], abc.ABC

explain(data: actableai.models.base.InputDataType) → actableai.models.base.OutputDataType

fit(data: actableai.models.base.InputDataType, target: Optional[actableai.models.base.OutputDataType] = None, **fit_params) → AAIBaseModel

fit_predict(data: actableai.models.base.InputDataType, target: Optional[actableai.models.base.OutputDataType] = None) → actableai.models.base.OutputDataType

fit_transform(data: actableai.models.base.InputDataType, target: Optional[actableai.models.base.OutputDataType] = None) → actableai.models.base.OutputDataType

has_explanations: bool = False

has_fit: bool = False

has_predict: bool = False

has_predict_proba: bool = False

has_transform: bool = False

predict(data: actableai.models.base.InputDataType) → actableai.models.base.OutputDataType

predict_proba(data: actableai.models.base.InputDataType) → actableai.models.base.OutputDataType

transform(data: actableai.models.base.InputDataType) → actableai.models.base.OutputDataType

version = 1

class actableai.models.base.AAIParametersModel(parameters: Optional[Dict[str, Any]] = None, process_parameters: bool = True)

Bases: actableai.models.base.AAIBaseModel[actableai.models.base.InputDataType, actableai.models.base.OutputDataType], Generic[actableai.models.base.InputDataType, actableai.models.base.OutputDataType], abc.ABC

abstract static get_parameters() → actableai.parameters.parameters.Parameters

Returns the parameters of the model.

Returns

The parameters.

actableai.models.inference module

class actableai.models.inference.AAIBaseModelInference(model: actableai.models.inference.ModelType)

Bases: Generic[actableai.models.inference.ModelType, actableai.models.inference.MetadataType], abc.ABC

classmethod get_inference_class(model_name: str) → Type[actableai.models.inference.AAIBaseModelInference]

get_parameters_definition() → actableai.parameters.parameters.Parameters

infer(data: pandas.core.frame.DataFrame, options: Dict[str, Any]) → Dict[str, Any]

class actableai.models.inference.AAIBaseModelMetadata

Bases: pydantic.main.BaseModel, abc.ABC

classmethod from_model(model: actableai.models.base.AAIBaseModel) → actableai.models.inference.AAIBaseModelMetadata

Module contents

actableai.models.feature2param(feature_name, feature_type, df_training)

Convert feature to parameter