tempor.models.clairvoyance2.interface.model module¶

class tempor.models.clairvoyance2.interface.model.BaseModel(params: dict[str, Any] | None = None)[source]¶

Bases: ABC

requirements : Requirements = Requirements(dataset_requirements=DatasetRequirements(requires_static_covariates_present=False, requires_no_missing_data=False, static_covariates_value_type=<DataValueOpts.ANY: 1>, temporal_covariates_value_type=<DataValueOpts.ANY: 1>, temporal_targets_value_type=<DataValueOpts.ANY: 1>, temporal_treatments_value_type=<DataValueOpts.ANY: 1>, event_covariates_value_type=<DataValueOpts.ANY: 1>, event_targets_value_type=<DataValueOpts.ANY: 1>, event_treatments_value_type=<DataValueOpts.ANY: 1>, requires_all_temporal_data_samples_aligned=False, requires_all_temporal_data_regular=False, requires_all_temporal_data_index_numeric=False, requires_all_temporal_containers_shares_index=True), prediction_requirements=None, treatment_effects_requirements=None)¶

DEFAULT_PARAMS : dict[str, Any] | NamedTuple = {}¶

check_unknown_params : bool = True¶

params : DotMap¶

inferred_params : DotMap¶

abstract check_model_requirements() → None[source]¶

check_data_requirements_general(called_at_fit_time: bool, data: Dataset, **kwargs)[source]¶

fit(data: Dataset, **kwargs) → BaseModel[source]¶

class tempor.models.clairvoyance2.interface.model.TransformerModel(params: dict[str, Any] | None = None)[source]¶

Bases: BaseModel, ABC

check_data_requirements_transform(data: Dataset, **kwargs)[source]¶

transform(data: Dataset, **kwargs) → Dataset[source]¶

inverse_transform(data: Dataset, **kwargs) → Dataset[source]¶

fit_transform(data: Dataset, **kwargs) → Dataset[source]¶

check_model_requirements() → None[source]¶

params : DotMap¶

inferred_params : DotMap¶

class tempor.models.clairvoyance2.interface.model.PredictorModel(params: dict[str, Any] | None = None)[source]¶

Bases: BaseModel, ABC

check_data_requirements_predict(data: Dataset, horizon: Horizon | None, **kwargs)[source]¶

predict(data: Dataset, horizon: Horizon | None, **kwargs) → TimeSeriesSamples | StaticSamples[source]¶

fit(data: Dataset, horizon: Horizon | None = None, **kwargs) → PredictorModel[source]¶

fit_predict(data: Dataset, horizon: Horizon | None, **kwargs) → TimeSeriesSamples | StaticSamples[source]¶

check_model_requirements() → None[source]¶

params : DotMap¶

inferred_params : DotMap¶

class tempor.models.clairvoyance2.interface.model.TreatmentEffectsModel(params: dict[str, Any] | None = None)[source]¶

Bases: PredictorModel, ABC

check_data_requirements_predict_counterfactuals(data: Dataset, sample_index: int, treatment_scenarios: Sequence[TimeSeries | EventSamples], horizon: Horizon | None, **kwargs)[source]¶

predict_counterfactuals(data: Dataset, sample_index: int, treatment_scenarios: Sequence[DataFrame | ndarray | TimeSeries | EventSamples], horizon: Horizon | None, **kwargs) → Sequence[TimeSeries | EventSamples][source]¶

fit(data: Dataset, horizon: Horizon | None = None, **kwargs) → TreatmentEffectsModel[source]¶

check_model_requirements() → None[source]¶

params : DotMap¶

inferred_params : DotMap¶