Index _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W _ __init__() (AncestralSamplingInference method) (Annotations method) (AutoencoderTrainer method) (AwA2Dataset method) (AxisAnnotation method) (BaseConceptLayer method) (BaseConstructor method) (BaseEncoder method) (BaseGraphLearner method) (BaseInference method) (BaseIntervention method) (BaseModel method) (BasePredictor method) (BipartiteModel method) (BlackBox method) (BnLearnDataModule method) (BnLearnDataset method) (CallableCC method) (CEBaBDataset method) (CelebADataset method) (ColoringSplitter method) (ColorMNISTDataset method) (CompletenessDataset method) (ConceptBottleneckModel method) (ConceptBottleneckModel_Joint method) (ConceptDataModule method) (ConceptDataset method) (ConceptLoss method) (ConceptMetrics method), [1] (CUBDataset method) (Delta method) (Dense method) (DeterministicInference method) (DistributionIntervention method) (DoIntervention method) (DownloadProgressBar method) (EndogenousVariable method) (ExogenousVariable method) (ForwardInference method) (GraphModel method) (GroundTruthIntervention method) (HyperLinearCUC method) (InputVariable method) (LinearCC method) (LinearUC method) (LinearZC method) (LinearZU method) (MixCUC method) (MLP method) (MNISTAddition method) (MNISTEvenOdd method) (ParametricCPD method) (PartialMNISTAddition method) (ProbabilisticModel method) (RandomPolicy method) (RandomSplitter method) (ResidualMLP method) (RewiringIntervention method) (Scaler method) (SelectorZU method) (SimpleAutoencoder method) (Splitter method) (StandardScaler method) (StochasticZC method) (ToyDataset method) (TrafficLights method) (UncertaintyInterventionPolicy method) (UniformPolicy method) (Variable method) (WANDAGraphLearner method) (WeightedConceptLoss method) __repr__() (ConceptMetrics method) _axis_annotations (Annotations attribute), [1] A add_exogenous() (ConceptDataset method) add_scaler() (ConceptDataset method) affine_transform() (in module torch_concepts.data.utils), [1] allow_zero_length_dataloader_with_multiple_devices (BlackBox attribute) (BnLearnDataModule attribute) (ConceptBottleneckModel attribute) (ConceptBottleneckModel_Joint attribute) (ConceptDataModule attribute) AncestralSamplingInference (class in torch_concepts.nn), [1] annotate_axis() (Annotations method) annotated_axes (Annotations property) annotations (BaseConstructor attribute), [1] Annotations (class in torch_concepts.annotations), [1] annotations (CompletenessDataset attribute), [1] (ConceptDataset attribute), [1] (ConceptDataset property) (ToyDataset attribute), [1] arg_constraints (Delta attribute), [1], [2] assign_random_values() (in module torch_concepts.data.utils), [1] assign_values_based_on_intervals() (in module torch_concepts.data.utils), [1] AutoencoderTrainer (class in torch_concepts.data.preprocessing.autoencoder), [1] available_query_vars (ForwardInference property) AwA2Dataset (class in torch_concepts.data.datasets.awa2), [1] axis_annotations (Annotations property) AxisAnnotation (class in torch_concepts.annotations), [1] B backbone (BaseModel attribute), [1] (BaseModel property) (ConceptDataModule attribute), [1] (ConceptDataModule property) BaseConceptLayer (class in torch_concepts.nn), [1] BaseConstructor (class in torch_concepts.nn), [1] BaseEncoder (class in torch_concepts.nn), [1] BaseGraphLearner (class in torch_concepts.nn), [1] BaseInference (class in torch_concepts.nn), [1] BaseIntervention (class in torch_concepts.nn), [1] BaseModel (class in torch_concepts.nn), [1] BasePredictor (class in torch_concepts.nn), [1] BipartiteModel (class in torch_concepts.nn), [1], [2] BlackBox (class in torch_concepts.nn), [1] BnLearnDataModule (class in torch_concepts.data.datamodules), [1] BnLearnDataset (class in torch_concepts.data.datasets.bnlearn), [1] build() (BnLearnDataset method) (CelebADataset method) (CompletenessDataset method) (ConceptDataset method) (ToyDataset method) build_cpt() (ParametricCPD method) build_cpts() (ProbabilisticModel method) build_potential() (ParametricCPD method) build_potentials() (ProbabilisticModel method) C cace_score() (in module torch_concepts.nn.functional), [1], [2] CallableCC (class in torch_concepts.nn), [1] cardinalities (AxisAnnotation attribute), [1], [2] (ConceptMetrics attribute), [1] (MixCUC attribute), [1] CEBaBDataset (class in torch_concepts.data.datasets.cebab), [1] CelebADataset (class in torch_concepts.data.datasets.celeba), [1] col_labels (BaseGraphLearner attribute), [1] collator() (CEBaBDataset method) ColoringSplitter (class in torch_concepts.data.splitters.coloring), [1] colorize() (in module torch_concepts.data.utils), [1] colorize_and_transform() (in module torch_concepts.data.utils), [1] ColorMNISTDataset (class in torch_concepts.data.datasets.mnist), [1] completeness_score() (in module torch_concepts.nn.functional), [1], [2] CompletenessDataset (class in torch_concepts.data.datasets.toy), [1] compute() (ConceptMetrics method) concept_annotations (BaseModel attribute), [1] concept_map (ForwardInference attribute), [1] concept_names (BaseModel attribute), [1] (BipartiteModel attribute), [1], [2] (CompletenessDataset attribute), [1] (ConceptDataset property) (ConceptMetrics attribute), [1] (MNISTAddition attribute), [1], [2] (MNISTEvenOdd attribute), [1], [2] (PartialMNISTAddition attribute) (ToyDataset attribute), [1] concept_to_variable (ProbabilisticModel attribute), [1], [2] concept_weights() (CUBDataset method) ConceptBottleneckModel (class in torch_concepts.nn), [1] ConceptBottleneckModel_Joint (class in torch_concepts.nn), [1] ConceptDataModule (class in torch_concepts.data.base.datamodule), [1] ConceptDataset (class in torch_concepts.data.base.dataset), [1] ConceptLoss (class in torch_concepts.nn.modules.loss), [1] ConceptMetrics (class in torch_concepts.nn.modules.metrics), [1] concepts (CompletenessDataset attribute), [1] (ConceptDataset attribute), [1] (EndogenousVariable attribute), [1] (ExogenousVariable attribute), [1] (InputVariable attribute), [1] (ParametricCPD attribute), [1] (ToyDataset attribute), [1] (Variable attribute), [1] confidence_selection() (in module torch_concepts.nn.functional), [1] convert_precision() (in module torch_concepts.data.utils), [1] criterion (AutoencoderTrainer attribute), [1] CUBDataset (class in torch_concepts.data.datasets.cub), [1] D dataset (ConceptDataModule attribute), [1] decoder (SimpleAutoencoder attribute), [1] Delta (class in torch_concepts.distributions), [1] Dense (class in torch_concepts.nn), [1] DeterministicInference (class in torch_concepts.nn), [1] device (AutoencoderTrainer attribute), [1] distribution (EndogenousVariable attribute), [1] (ExogenousVariable attribute), [1] (InputVariable attribute), [1] (Variable attribute), [1] DistributionIntervention (class in torch_concepts.nn), [1] DoIntervention (class in torch_concepts.nn), [1] download (ColorMNISTDataset attribute), [1] (MNISTAddition attribute), [1] (MNISTEvenOdd attribute), [1] download() (BnLearnDataset method) (CelebADataset method) (CompletenessDataset method) (ConceptDataset method) (ToyDataset method) download_url() (in module torch_concepts.data.io), [1] DownloadProgressBar (class in torch_concepts.data.io), [1] E edge_type() (in module torch_concepts.nn.functional), [1] embedding_size (HyperLinearCUC attribute), [1] encoder (LinearUC attribute), [1] (LinearZC attribute), [1] (LinearZU attribute), [1] (SimpleAutoencoder attribute), [1] endogenous_var (ExogenousVariable attribute), [1] EndogenousVariable (class in torch_concepts), [1] ensure_list() (in module torch_concepts.data.utils), [1] exogenous (ConceptDataset property) exogenous_size (LinearZU attribute), [1] (SelectorZU attribute), [1] ExogenousVariable (class in torch_concepts), [1] extract_embs_from_autoencoder() (in module torch_concepts.data.preprocessing.autoencoder), [1] extract_latent() (AutoencoderTrainer method) extract_tar() (in module torch_concepts.data.io), [1] extract_zip() (in module torch_concepts.data.io), [1] F files_exist() (in module torch_concepts.data.utils), [1] filter_output_for_loss() (BaseModel method) (BlackBox method) (ConceptBottleneckModel_Joint method) filter_output_for_metrics() (BaseModel method) (BlackBox method) (ConceptBottleneckModel_Joint method) fit() (ColoringSplitter method) (RandomSplitter method) (Scaler method) (Splitter method) (StandardScaler method) fit_transform() (Scaler method) fitted (Splitter property) forward() (BaseConceptLayer method) (BaseInference method) (BlackBox method) (CallableCC method) (ConceptBottleneckModel_Joint method) (ConceptLoss method) (Dense method) (HyperLinearCUC method) (LinearCC method) (LinearUC method) (LinearZC method) (LinearZU method) (MixCUC method) (MLP method) (ParametricCPD method) (RandomPolicy method) (ResidualMLP method) (SelectorZU method) (SimpleAutoencoder method) (StochasticZC method) (UncertaintyInterventionPolicy method) (UniformPolicy method) (WeightedConceptLoss method) ForwardInference (class in torch_concepts.nn), [1] from_dict() (Annotations class method) (AxisAnnotation class method) G get() (ConceptMetrics method) get_axis_annotation() (Annotations method) get_axis_cardinalities() (Annotations method) get_axis_labels() (Annotations method) get_by_distribution() (ProbabilisticModel method) get_dataloader() (ConceptDataModule method) get_endogenous_idx() (AxisAnnotation method) get_index() (Annotations method) (AxisAnnotation method) get_label() (Annotations method) (AxisAnnotation method) get_label_state() (Annotations method) get_label_states() (Annotations method) get_module_of_concept() (ProbabilisticModel method) get_parent_kwargs() (ForwardInference method) get_results() (AncestralSamplingInference method) (DeterministicInference method) (ForwardInference method) get_state_index() (Annotations method) get_states() (Annotations method) get_total_cardinality() (AxisAnnotation method) get_variable_parents() (ProbabilisticModel method) graph (CompletenessDataset attribute), [1] (ConceptDataset property) (ToyDataset attribute), [1] graph_learner (ForwardInference attribute), [1] graph_order (GraphModel attribute), [1], [2] GraphModel (class in torch_concepts.nn), [1], [2] GroundTruthIntervention (class in torch_concepts.nn), [1] groupby_metadata() (AxisAnnotation method) grouped_concept_exogenous_mixture() (in module torch_concepts.nn.functional), [1] H hard_threshold (WANDAGraphLearner attribute), [1] has_axis() (Annotations method) has_concepts (ConceptDataset property) has_exogenous (ConceptDataset property) has_metadata() (AxisAnnotation method) has_rsample (Delta attribute), [1], [2] HyperLinearCUC (class in torch_concepts.nn), [1] hypernet (HyperLinearCUC attribute), [1] I in_activation (BasePredictor attribute), [1] (LinearCC attribute), [1] in_features (BaseConceptLayer attribute), [1] (LinearZC attribute), [1] (Variable property) in_features_endogenous (BaseConceptLayer attribute), [1] (HyperLinearCUC attribute), [1] (LinearCC attribute), [1] (MixCUC attribute), [1] in_features_exogenous (BaseConceptLayer attribute), [1] (HyperLinearCUC attribute), [1] (LinearUC attribute), [1] (MixCUC attribute), [1] indices (Splitter property) input_data (CompletenessDataset attribute), [1] (ConceptDataset attribute), [1] (ToyDataset attribute), [1] input_dim (MNISTAddition attribute) (MNISTEvenOdd attribute) input_shape (MNISTAddition attribute) (MNISTEvenOdd attribute) input_size (BaseConstructor attribute), [1] InputVariable (class in torch_concepts), [1] internal_nodes (GraphModel attribute), [1], [2] intervention() (in module torch_concepts.nn), [1] intervention_score() (in module torch_concepts.nn.functional), [1], [2] inverse_transform() (Scaler method) (StandardScaler method) is_axis_nested() (Annotations method) is_nested (AxisAnnotation attribute), [1] items() (Annotations method) J join_union() (Annotations method) K keys() (Annotations method) L label_names (BipartiteModel attribute), [1], [2] labels (AxisAnnotation attribute), [1], [2] (BaseConstructor attribute), [1] latent_encoder (BaseModel attribute), [1] (BaseModel property) latent_size (BaseModel attribute), [1] levels (ForwardInference attribute), [1] linear_equation_eval() (in module torch_concepts.nn.functional), [1] linear_equation_expl() (in module torch_concepts.nn.functional), [1] LinearCC (class in torch_concepts.nn), [1] LinearUC (class in torch_concepts.nn), [1] LinearZC (class in torch_concepts.nn), [1] LinearZU (class in torch_concepts.nn), [1] load() (BnLearnDataset method) (CelebADataset method) (CompletenessDataset method) (ConceptDataset method) (ToyDataset method) load_pickle() (in module torch_concepts.data.io), [1] load_raw() (BnLearnDataset method) (CelebADataset method) (CompletenessDataset method) (ConceptDataset method) (ToyDataset method) log_prob() (Delta method) logic_memory_reconstruction() (in module torch_concepts.nn.functional), [1] logic_rule_eval() (in module torch_concepts.nn.functional), [1] logic_rule_explanations() (in module torch_concepts.nn.functional), [1] M max_uncertainty_point (UncertaintyInterventionPolicy attribute), [1] maybe_apply_backbone() (BaseModel method) maybe_build() (ConceptDataset method) maybe_download() (CelebADataset method) (ConceptDataset method) maybe_extract() (CelebADataset method) maybe_reduce_annotations() (ConceptDataset method) mean (Delta property) (StandardScaler attribute), [1] memory (SelectorZU attribute), [1] memory_size (SelectorZU attribute), [1] metadata (AxisAnnotation attribute), [1], [2] (EndogenousVariable attribute), [1] (ExogenousVariable attribute), [1] (InputVariable attribute), [1] (Variable attribute), [1] MixCUC (class in torch_concepts.nn), [1] MLP (class in torch_concepts.nn), [1] MNISTAddition (class in torch_concepts.data.datasets.mnist), [1] MNISTEvenOdd (class in torch_concepts.data.datasets.mnist), [1] model (AutoencoderTrainer attribute), [1] (BaseIntervention attribute), [1] model_graph (GraphModel attribute), [1], [2] module (ParametricCPD attribute), [1] mu (StochasticZC attribute), [1] N n_concepts (CompletenessDataset attribute), [1] (ConceptDataset property) (ConceptMetrics attribute), [1] (MNISTAddition attribute) (MNISTEvenOdd attribute) (PartialMNISTAddition attribute) (ToyDataset attribute), [1] n_exogenous (ConceptDataset property) n_exogenous_per_concept (LinearUC attribute), [1] n_features (CelebADataset property) (CompletenessDataset attribute), [1] (ConceptDataset property) (ToyDataset attribute), [1] n_labels (BaseGraphLearner attribute), [1] n_samples (CelebADataset property) (ConceptDataModule property) (ConceptDataset property) n_tasks (MNISTAddition attribute) (MNISTEvenOdd attribute) name (ConceptDataset attribute), [1] (MNISTAddition attribute) (MNISTEvenOdd attribute) (PartialMNISTAddition attribute) name2id (BaseConstructor attribute), [1] np_params (WANDAGraphLearner attribute), [1] num_annotated_axes (Annotations property) num_monte_carlo (StochasticZC attribute), [1] O optimizer (AutoencoderTrainer attribute), [1] out_endogenous_dim (LinearZU attribute), [1] out_features (BaseConceptLayer attribute), [1] (HyperLinearCUC attribute), [1] (LinearCC attribute), [1] (LinearZC attribute), [1] (MixCUC attribute), [1] (RandomPolicy attribute), [1] (UncertaintyInterventionPolicy attribute), [1] (UniformPolicy attribute), [1] (Variable property) P parametric_cpds (ProbabilisticModel attribute), [1] ParametricCPD (class in torch_concepts.nn), [1] parents (EndogenousVariable attribute), [1] (ExogenousVariable attribute), [1] (InputVariable attribute), [1] (ParametricCPD attribute), [1], [2] (Variable attribute), [1] parse_tensor() (in module torch_concepts.data.utils), [1] PartialMNISTAddition (class in torch_concepts.data.datasets.mnist), [1] perconcept_metrics (ConceptMetrics attribute), [1] precision (ConceptDataset attribute), [1] predict() (ForwardInference method) predictor (LinearCC attribute), [1] (MixCUC attribute), [1] prepare_data_per_node (BlackBox attribute) (BnLearnDataModule attribute) (ConceptBottleneckModel attribute) (ConceptBottleneckModel_Joint attribute) (ConceptDataModule attribute) preprocess_function() (CEBaBDataset method) priority_var (WANDAGraphLearner attribute), [1] probabilistic_model (ForwardInference attribute), [1] (GraphModel attribute), [1], [2] ProbabilisticModel (class in torch_concepts.nn), [1] processed_filenames (BnLearnDataset property) (CelebADataset property) (CompletenessDataset property) (ConceptDataset property) (ToyDataset property) processed_paths (ConceptDataset property) prune() (BasePredictor method) (HyperLinearCUC method) (LinearCC method) prune_linear_layer() (in module torch_concepts.nn.functional), [1] Q query() (BaseInference method) (ForwardInference method) (RewiringIntervention method) R random (ColorMNISTDataset attribute), [1] RandomPolicy (class in torch_concepts.nn), [1] RandomSplitter (class in torch_concepts.data.splitters.random), [1] raw_filenames (BnLearnDataset property) (CelebADataset property) (CompletenessDataset property) (ConceptDataset property) (ToyDataset property) raw_paths (ConceptDataset property) remove_exogenous() (ConceptDataset method) reset() (ConceptMetrics method) (Splitter method) reset_parameters() (Dense method) (MLP method) residual_concept_causal_effect() (in module torch_concepts.nn.functional), [1] ResidualMLP (class in torch_concepts.nn), [1] RewiringIntervention (class in torch_concepts.nn), [1] root (ColorMNISTDataset attribute), [1] (MNISTAddition attribute), [1] (MNISTEvenOdd attribute), [1] root_dir (ConceptDataset property) root_nodes (GraphModel attribute), [1], [2] row_labels (BaseGraphLearner attribute), [1] rsample() (Delta method) S sample() (Delta method) sample_array() (TrafficLights method) save_pickle() (in module torch_concepts.data.io), [1] scale (RandomPolicy attribute), [1] Scaler (class in torch_concepts.data.base.scaler), [1] scalers (ConceptDataModule attribute), [1] select() (Annotations method) select_many() (Annotations method) selection_eval() (in module torch_concepts.nn.functional), [1] selective_calibration() (in module torch_concepts.nn.functional), [1] selector (SelectorZU attribute), [1] SelectorZU (class in torch_concepts.nn), [1] set_concepts() (ConceptDataset method) set_graph() (ConceptDataset method) set_indices() (Splitter method) setup() (ConceptDataModule method) shape (Annotations property) (AxisAnnotation property) (CelebADataset property) (ConceptDataset property) sigma (StochasticZC attribute), [1] SimpleAutoencoder (class in torch_concepts.data.preprocessing.autoencoder), [1] size (EndogenousVariable attribute), [1] (ExogenousVariable attribute), [1] (InputVariable attribute), [1] (Variable attribute), [1] soft_select() (in module torch_concepts.nn.functional), [1] sorted_variables (ForwardInference attribute), [1] split() (Splitter method) Splitter (class in torch_concepts.data.base.splitter), [1] splitter (ConceptDataModule attribute), [1] StandardScaler (class in torch_concepts.data.scalers.standard), [1] states (AxisAnnotation attribute), [1], [2] std (StandardScaler attribute), [1] StochasticZC (class in torch_concepts.nn), [1] subset() (AxisAnnotation method) summary_metrics (ConceptMetrics attribute), [1] support (Delta attribute), [1], [2] T target_transform (ColorMNISTDataset attribute), [1] (MNISTAddition attribute), [1] (MNISTEvenOdd attribute), [1] task_names (BipartiteModel attribute), [1], [2] (MNISTAddition attribute), [1], [2] (MNISTEvenOdd attribute), [1], [2] temperature (SelectorZU attribute), [1] test_dataloader() (ConceptDataModule method) test_idxs (Splitter attribute), [1] (Splitter property) test_len (ConceptDataModule property) (Splitter property) test_metrics (ConceptMetrics attribute), [1] testset (ConceptDataModule attribute), [1] (ConceptDataModule property) threshold (WANDAGraphLearner attribute), [1] to_dict() (Annotations method) (AxisAnnotation method) ToyDataset (class in torch_concepts.data.datasets.toy), [1] TrafficLights (class in torch_concepts.data.datasets.traffic), [1] train (ColorMNISTDataset attribute), [1] (MNISTAddition attribute), [1] (MNISTEvenOdd attribute), [1] train() (AutoencoderTrainer method) train_dataloader() (ConceptDataModule method) train_idxs (Splitter attribute), [1] (Splitter property) train_len (ConceptDataModule property) (Splitter property) train_metrics (ConceptMetrics attribute), [1] training (AncestralSamplingInference attribute) (BaseConceptLayer attribute) (BaseConstructor attribute) (BaseEncoder attribute) (BaseGraphLearner attribute) (BaseInference attribute) (BaseIntervention attribute) (BaseModel attribute) (BasePredictor attribute) (BipartiteModel attribute), [1] (BlackBox attribute) (CallableCC attribute) (ConceptBottleneckModel attribute) (ConceptBottleneckModel_Joint attribute) (ConceptLoss attribute) (ConceptMetrics attribute) (Dense attribute) (DeterministicInference attribute) (DistributionIntervention attribute) (DoIntervention attribute) (ForwardInference attribute) (GraphModel attribute), [1] (GroundTruthIntervention attribute) (HyperLinearCUC attribute) (LinearCC attribute) (LinearUC attribute) (LinearZC attribute) (LinearZU attribute) (MixCUC attribute) (MLP attribute) (ParametricCPD attribute) (ProbabilisticModel attribute) (RandomPolicy attribute) (ResidualMLP attribute) (RewiringIntervention attribute) (SelectorZU attribute) (StochasticZC attribute) (UncertaintyInterventionPolicy attribute) (UniformPolicy attribute) (WANDAGraphLearner attribute) (WeightedConceptLoss attribute) trainset (ConceptDataModule attribute), [1] (ConceptDataModule property) transform (ColorMNISTDataset attribute), [1] (MNISTAddition attribute), [1], [2] (MNISTEvenOdd attribute), [1], [2] transform() (Scaler method) (StandardScaler method) transform_images() (in module torch_concepts.data.utils), [1] U UncertaintyInterventionPolicy (class in torch_concepts.nn), [1] UniformPolicy (class in torch_concepts.nn), [1] union_with() (AxisAnnotation method) unrolled_probabilistic_model() (ForwardInference method) update() (ConceptMetrics method) update_to() (DownloadProgressBar method) use_bias (HyperLinearCUC attribute), [1] V val_dataloader() (ConceptDataModule method) val_idxs (Splitter attribute), [1] (Splitter property) val_len (ConceptDataModule property) (Splitter property) val_metrics (ConceptMetrics attribute), [1] valset (ConceptDataModule attribute), [1] (ConceptDataModule property) values() (Annotations method) Variable (class in torch_concepts), [1] variable (ParametricCPD attribute), [1], [2] variables (ProbabilisticModel attribute), [1] W WANDAGraphLearner (class in torch_concepts.nn), [1] weighted_adj (WANDAGraphLearner property) weighted_adj() (BaseGraphLearner method) WeightedConceptLoss (class in torch_concepts.nn.modules.loss), [1]