tempor.models.clairvoyance2.interface.requirements module

class tempor.models.clairvoyance2.interface.requirements.DataStructureOpts(value)[source]

Bases: Enum

An enumeration.

TIME_SERIES = 1
STATIC = 2
EVENT = 3
class tempor.models.clairvoyance2.interface.requirements.DataValueOpts(value)[source]

Bases: Enum

An enumeration.

ANY = 1
NUMERIC = 2
NUMERIC_CATEGORICAL = 3
NUMERIC_BINARY = 4
class tempor.models.clairvoyance2.interface.requirements.DatasetRequirements(requires_static_covariates_present: bool = False, requires_no_missing_data: bool = False, static_covariates_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, temporal_covariates_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, temporal_targets_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, temporal_treatments_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, event_covariates_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, event_targets_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, event_treatments_value_type: tempor.models.clairvoyance2.interface.requirements.DataValueOpts = <DataValueOpts.ANY: 1>, requires_all_temporal_data_samples_aligned: bool = False, requires_all_temporal_data_regular: bool = False, requires_all_temporal_data_index_numeric: bool = False, requires_all_temporal_containers_shares_index: bool = True)[source]

Bases: object

requires_static_covariates_present : bool = False
requires_no_missing_data : bool = False
static_covariates_value_type : DataValueOpts = 1
temporal_covariates_value_type : DataValueOpts = 1
temporal_targets_value_type : DataValueOpts = 1
temporal_treatments_value_type : DataValueOpts = 1
event_covariates_value_type : DataValueOpts = 1
event_targets_value_type : DataValueOpts = 1
event_treatments_value_type : DataValueOpts = 1
requires_all_temporal_data_samples_aligned : bool = False
requires_all_temporal_data_regular : bool = False
requires_all_temporal_data_index_numeric : bool = False
requires_all_temporal_containers_shares_index : bool = True
class tempor.models.clairvoyance2.interface.requirements.PredictionRequirements(target_data_structure: tempor.models.clairvoyance2.interface.requirements.DataStructureOpts = <DataStructureOpts.TIME_SERIES: 1>, horizon_type: tempor.models.clairvoyance2.interface.horizon.HorizonOpts = <HorizonOpts.N_STEP_AHEAD: 1>, min_timesteps_target_when_fit: int = 1, min_timesteps_target_when_predict: int = 1)[source]

Bases: object

target_data_structure : DataStructureOpts = 1
horizon_type : HorizonOpts = 1
min_timesteps_target_when_fit : int = 1
min_timesteps_target_when_predict : int = 1
class tempor.models.clairvoyance2.interface.requirements.TreatmentEffectsRequirements(treatment_data_structure: tempor.models.clairvoyance2.interface.requirements.DataStructureOpts = <DataStructureOpts.TIME_SERIES: 1>, min_timesteps_treatment_when_fit: int = 1, min_timesteps_treatment_when_predict: int = 1, min_timesteps_treatment_when_predict_counterfactual: int = 1)[source]

Bases: object

treatment_data_structure : DataStructureOpts = 1
min_timesteps_treatment_when_fit : int = 1
min_timesteps_treatment_when_predict : int = 1
min_timesteps_treatment_when_predict_counterfactual : int = 1
class tempor.models.clairvoyance2.interface.requirements.Requirements(dataset_requirements: tempor.models.clairvoyance2.interface.requirements.DatasetRequirements = 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: Union[tempor.models.clairvoyance2.interface.requirements.PredictionRequirements, NoneType] = None, treatment_effects_requirements: Union[tempor.models.clairvoyance2.interface.requirements.TreatmentEffectsRequirements, NoneType] = None)[source]

Bases: object

dataset_requirements : DatasetRequirements = 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 : PredictionRequirements | None = None
treatment_effects_requirements : TreatmentEffectsRequirements | None = None
tempor.models.clairvoyance2.interface.requirements.raise_requirements_mismatch_error(requirement_name: str, explanation_text: str) → NoReturn[source]
tempor.models.clairvoyance2.interface.requirements.get_container_friendly_name(container_name: str) → str[source]
class tempor.models.clairvoyance2.interface.requirements.RequirementsChecker[source]

Bases: object

static check_data_requirements_general(called_at_fit_time: bool, requirements: Requirements, data: Dataset, **kwargs)[source]
static check_data_requirements_transform(requirements: Requirements, data: Dataset, **kwargs)[source]
static check_data_requirements_predict(requirements: Requirements, data: Dataset, horizon: Horizon | None, **kwargs)[source]
static check_data_requirements_predict_counterfactuals(requirements: Requirements, data: Dataset, sample_index: int, treatment_scenarios: TTreatmentScenarios, horizon: Horizon | None, **kwargs)[source]
static check_predictor_model_requirements(predictor)[source]
static check_treatment_effects_model_requirements(treatment_effects_model)[source]