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

class actableai.models.autogluon.base.AAIAutogluonTabularInference(model: actableai.models.inference.ModelType)

Bases: actableai.models.inference.AAIBaseModelInference[actableai.models.autogluon.base.AutogluonModelType, actableai.models.autogluon.base.AutogluonMetadataType], Generic[actableai.models.autogluon.base.AutogluonModelType, actableai.models.autogluon.base.AutogluonMetadataType], abc.ABC

class actableai.models.autogluon.base.AAIAutogluonTabularMetadata(*, features: List[str], feature_parameters: Dict[str, Any], problem_type: Literal['regression', 'quantile', 'binary', 'multiclass'], prediction_target: str, is_explainer_available: bool, intervened_column: Optional[str] = None, discrete_treatment: Optional[str] = None)

Bases: actableai.models.inference.AAIBaseModelMetadata, abc.ABC

discrete_treatment: Optional[str]

feature_parameters: Dict[str, Any]

features: List[str]

intervened_column: Optional[str]

is_explainer_available: bool

prediction_target: str

problem_type: Literal['regression', 'quantile', 'binary', 'multiclass']

class actableai.models.autogluon.base.AAIAutogluonTabularModel(autogluon_model: TabularPredictor, df_training: pd.DataFrame, has_predict_proba: bool, explanation_model=None, intervention_model=None)

Bases: actableai.models.base.AAIBaseModel[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame], abc.ABC

can_run_intervention(data: pandas.core.frame.DataFrame) → bool

has_fit: bool = False

has_predict: bool = True

run_intervention(data: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

run_intervention_prediction(data: pandas.core.frame.DataFrame, predictions: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

actableai.models.autogluon.polynomial_linear module

Module contents