tempor.models.clairvoyance2.preprocessing.convenience module¶
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class tempor.models.clairvoyance2.preprocessing.convenience.ExtractTC(params: dict[str, Any] | None =
None)[source]¶ Bases:
TransformerModel-
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)¶
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requirements : Requirements =
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class tempor.models.clairvoyance2.preprocessing.convenience.TemporalTargetsExtractor(params: dict[str, Any] | None =
None)[source]¶ Bases:
ExtractTC-
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)¶
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DEFAULT_PARAMS : _ExtractTargetsTCParams =
_ExtractTargetsTCParams(targets=())¶
- params : DotMap¶
- inferred_params : DotMap¶
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requirements : Requirements =
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class tempor.models.clairvoyance2.preprocessing.convenience.TemporalTreatmentsExtractor(params: dict[str, Any] | None =
None)[source]¶ Bases:
ExtractTC-
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)¶
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DEFAULT_PARAMS : _ExtractTreatmentsTCParams =
_ExtractTreatmentsTCParams(treatments=())¶
- params : DotMap¶
- inferred_params : DotMap¶
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requirements : Requirements =
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class tempor.models.clairvoyance2.preprocessing.convenience.TimeIndexFeatureConcatenator(params: dict[str, Any] | None =
None)[source]¶ Bases:
TransformerModel-
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=True, requires_all_temporal_containers_shares_index=True), prediction_requirements=None, treatment_effects_requirements=None)¶
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DEFAULT_PARAMS : _AddTimeIndexFeatureTCParams =
_AddTimeIndexFeatureTCParams(add_time_index=False, add_time_delta=True, time_delta_pad_at_back=False, time_delta_pad_value=0.0)¶
- params : DotMap¶
- inferred_params : DotMap¶
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requirements : Requirements =
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class tempor.models.clairvoyance2.preprocessing.convenience.StaticFeaturesConcatenator(params: dict[str, Any] | None =
None)[source]¶ Bases:
TransformerModel-
requirements : Requirements =
Requirements(dataset_requirements=DatasetRequirements(requires_static_covariates_present=True, 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)¶
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DEFAULT_PARAMS : _AddStaticCovariatesTCParams =
_AddStaticCovariatesTCParams(feature_name_prefix='static', append_at_beginning=False, drop_static_covariates=False)¶
- params : DotMap¶
- inferred_params : DotMap¶
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requirements : Requirements =