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yohou.testing

Testing utilities for Yohou estimators.

Functions

Name Description
check_class_proba_classes_attribute Check classes_ and n_classes_ attributes are populated correctly after fit.
check_class_proba_predict_returns_labels Check predict() returns argmax class labels, not probabilities.
check_class_proba_prediction_bounds Check all probability values are in [0, 1].
check_class_proba_prediction_structure Check class-probability predictions have correct column structure.
check_class_proba_prediction_sums Check probabilities sum to 1.0 per row per target (tolerance: 1e-6).
check_class_proba_prediction_types Check class-proba forecaster has 'class_proba' in forecaster_type tag.
check_metadata_routing_default_request Check that by default metadata routing request is empty.
check_metadata_routing_get_metadata_routing Check that get_metadata_routing() is implemented correctly.
check_composite_combination_validation Check an unknown combination raises.
check_composite_rejects_bare_list Check a bare [estimator, ...] list (no names) is rejected.
check_composite_weights_length_validation Check a weights list of the wrong length raises.
check_clone_preserves_params Check clone() preserves shallow params and freshens nested estimators.
check_composition_clone_deep_clones_components Check clone(compositor) produces fresh same-type components.
check_composition_nested_param_addressable Check nested <name>__<param> get/set on a _BaseComposition.
check_get_set_params_round_trip Check set_params(**get_params(deep=True)) is a no-op.
check_init_no_param_mutation Check __init__ stores constructor arguments verbatim.
check_clone_preserves_forecaster_params Check sklearn's clone() preserves init parameters.
check_fit_predict_with_X_forecast Check fit + predict works with X_forecast provided.
check_fit_predict_with_X_future Check fit + predict works with X_future provided.
check_fit_predict_without_exogenous Check forecaster behavior when X_actual=None at fit time.
check_fit_sets_forecaster_attributes Check fit() sets required forecaster attributes.
check_forecaster_methods_call_check_is_fitted Check all forecaster methods (except fit) raise NotFittedError when unfitted.
check_forecaster_not_fitted_error Check accessing fitted attributes before fit() raises NotFittedError.
check_forecaster_tags_accessible_before_fit Check sklearn_tags() is accessible before fit().
check_forecaster_tags_match_capabilities Check forecaster tags accurately reflect capabilities.
check_forecaster_tags_static_after_fit Check forecaster tags remain static after fit().
check_forecasting_horizon_validation Check forecasting_horizon < 1 raises ValueError.
check_mixed_cadence_X_forecast_resolves Check that a channel on a slower schedule survives step-column derivation.
check_observe_auto_rederives_step_columns Check observe() re-derives step columns from stored raws.
check_observe_extends_observations Check observe() extends observation buffers correctly.
check_observe_predict_interval_with_step_columns Check observe_predict_interval works with step columns (lightweight).
check_observe_predict_with_step_columns Check observe_predict works with step columns (lightweight).
check_predict_time_columns Check predictions have vintage_time and time columns.
check_predict_X_forecast_override Check predict with X_forecast override produces different results.
check_prediction_types_property Check forecaster_type tag is None or a frozenset of known prediction types.
check_requires_exogenous_warns_on_X_future_X_forecast Check that a forecaster with requires_exogenous=False warns when X_future/X_forecast provided.
check_rewind_propagates_to_transformers Check rewind() propagates to transformers in forecaster.
check_rewind_replaces_observations Check rewind() replaces observation buffers correctly.
check_coverage_rates_parameter Check fit_coverage_rates_ fitted attribute is a non-empty list of floats in [0, 1].
check_coverage_rates_validation Check invalid coverage_rates raise ValueError during fit and predict.
check_interval_bounds Check upper >= lower for all coverage rates and time steps.
check_interval_prediction_columns Check interval predictions have {col}lower} and {colupper format.
check_interval_prediction_types Check interval forecaster has 'interval' in forecaster_type tag.
assert_request_equal Assert metadata request matches expected dictionary.
assert_request_is_empty Check if a metadata request dict is empty.
check_recorded_metadata Check whether the expected metadata is passed to the object's method.
record_metadata Store passed metadata to a method of obj.
check_panel_data Check cross-learning with panel data predicts all groups by default.
check_panel_invalid_group_raises Check that an invalid group name raises ValueError.
check_panel_single_group Check cross-learning filters to specified panel group.
check_point_prediction_structure Check point predictions have correct column structure.
check_point_prediction_types Check point forecaster has 'point' in forecaster_type tag.
check_estimator_parameter Check estimator parameter is sklearn BaseEstimator.
check_reduction_strategy Check reduction_strategy is one of 'direct', 'dir-rec', 'multi-output'.
check_scorer_aggregation_methods Check all aggregation_method combinations produce valid output.
check_scorer_component_subselection Check components filtering works correctly.
check_scorer_coverage_rate_subselection Check coverage parameter filters interval predictions correctly.
check_scorer_lower_is_better Check the lower_is_better tag is a boolean.
check_scorer_methods_call_check_is_fitted Check all scorer methods (except fit) raise NotFittedError when unfitted.
check_scorer_multi_vintage Check that scorer produces a finite result on multi-vintage input.
check_scorer_panel_subselection Check groups filtering works correctly.
check_scorer_parameter_validation Check parameter validation raises ValueError for invalid inputs.
check_scorer_prediction_type_compatibility Check scorer works with correct forecaster output type.
check_scorer_tags_accessible_before_fit Check sklearn_tags() is callable on scorer instance.
check_scorer_tags_match_capabilities Check tag values match actual scorer behavior.
check_scorer_tags_static_after_fit Check tags remain unchanged after fit.
check_grid_search_exhaustive Check GridSearchCV evaluates all parameter combinations.
check_grid_search_param_grid_validation Check param_grid format is validated (dict or list of dicts).
check_randomized_search_distributions Check scipy.stats distributions work for parameter sampling.
check_randomized_search_n_iter Check n_iter controls number of parameter combinations evaluated.
check_randomized_search_reproducibility Check random_state produces same parameter samples.
check_search_clone_preserves_params Check sklearn clone() preserves search CV parameters.
check_search_cv_results_structure Check cv_results_ has required structure.
check_search_error_score_handling Check error_score parameter handles failing fits correctly.
check_search_fit_sets_attributes Check fit() sets required search CV attributes.
check_search_interval_predict_delegates Check predict_interval() works after interval search with refit.
check_search_method_availability Check @available_if decorator logic with refit=True/False.
check_search_multimetric_scoring Check multi-metric scoring with dict scorer works correctly.
check_search_not_fitted_error Check fitted attributes before fit() raise NotFittedError.
check_search_observe_delegates Check observe() delegates to best_forecaster_.observe() correctly.
check_search_panel_data Check groups parameter propagates correctly.
check_search_predict_delegates Check predict() delegates to best_forecaster_.predict() correctly.
check_search_refit_false_no_forecaster Check refit=False doesn't create best_forecaster_.
check_search_return_train_score Check return_train_score=True adds train score keys to cv_results_.
check_search_rewind_delegates Check rewind() delegates to best_forecaster_.rewind() correctly.
check_similarity_methods_call_check_is_fitted Check predict, observe, and rewind raise NotFittedError before fit.
check_similarity_metric_params_verbatim Check metric_params=None is stored verbatim (no init-mutation).
check_similarity_predict_matrix_shape Check predict returns an (n_pred, n_calib) weight matrix.
check_similarity_to_weights_rows_reserve_mass Check each predicted weight row is non-negative and sums below 1.
check_splitter_n_splits_consistency Check get_n_splits() matches actual split count.
check_splitter_non_overlapping_tests Check test sets don't overlap if produces_non_overlapping_tests=True.
check_splitter_panel_data_support Check splitter handles panel data if supports_panel_data=True.
check_splitter_parameter_constraints Check parameter constraints are enforced via sklearn validation.
check_splitter_produces_valid_indices Check all train/test indices are valid row positions.
check_splitter_tags_accessible_before_fit Check sklearn_tags() is callable on splitter instance.
check_splitter_tags_match_capabilities Check tag values match actual splitter behavior.
check_splitter_tags_static_after_fit Check tags remain unchanged after fit.
check_feature_names_out_match Check get_feature_names_out() matches transform() output columns.
check_fit_idempotent Check that fit(X).fit(X) equals fit(X).
check_fit_sets_attributes Check fit() sets required attributes.
check_fit_transform_equivalence Check fit_transform(X) == fit(X).transform(X).
check_insufficient_data_raises Check behavior when data length < observation_horizon.
check_inverse_observe_transform_identity Check inverse_transform(observe_transform(X)) ≈ X.
check_inverse_transform_identity Check inverse_transform(transform(X)) ≈ X.
check_inverse_transform_round_trip Check inverse_transform(transform(X)) ≈ X with shape validation.
check_memory_bounded Check memory doesn't grow unbounded with sequential updates.
check_observation_horizon_after_fit Check observation_horizon is valid after fit().
check_observation_horizon_not_fitted Check observation_horizon behavior before fit().
check_observe_concatenates_memory Check observe() appends new data and maintains horizon size.
check_observe_transform_equivalence Check observe() does not change transform() output for a fitted transformer.
check_observe_transform_sequential_consistency Check observe_transform(A) then observe_transform(B) == observe_transform(A+B).
check_panel_data_support Check transformer handles panel columns (panel data) correctly.
check_panel_group_preservation Check that transformers preserve panel group names after transformation.
check_rewind_transform_behavior Check rewind_transform() behavior and contract.
check_rewind_updates_memory Check rewind(X) sets _X_observed to X.tail(observation_horizon).
check_tags_accessible_before_fit Check sklearn_tags() is accessible before fit().
check_tags_match_capabilities Check tags accurately reflect transformer capabilities.
check_tags_static_after_fit Check tags remain static (don't change) after fit().
check_transform_drops_warmup_rows Check stateful transformers drop exactly observation_horizon rows.
check_transform_output_structure Check transform() output has "time" column and valid structure.
check_transformer_methods_call_check_is_fitted Check all transformer methods (except fit) raise NotFittedError when unfitted.
check_transformer_preserve_dtypes Check transformer preserves input dtypes.
check_transformers_unfitted_stateless Check stateless transformers transform deterministically across fits.
check_weighter_compute_weights_alignment Check compute_weights returns one weight per key element.
check_weighter_default_constructible Check the weighter class is constructible with no arguments.
check_weighter_fit_noop_returns_self Check fit is a no-op returning self that leaves params unchanged.
check_weighter_resolved_array_validation Check resolved-array validation rejects NaN/negative/inf/all-zero.
check_weighter_tags_accessible_before_fit Check __sklearn_tags__() is callable on an unfitted weighter.
check_weighter_tags_static_after_fit Check tags are unchanged by the no-op fit.