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probly 0.9.0 documentation
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Table of Contents

  • Introduction
  • Installation
  • User Guide
    • 1. Transformation
    • 2. Representation
    • 3. Quantification
    • 4. Evaluation
    • 5. Visualization
  • Examples and Tutorials
    • Examples
      • Active Learning
        • Active Learning with sklearn - Margin vs Random
        • Active Learning with PyTorch - BADGE Selection
      • Conformal Prediction
        • Regression Conformal Prediction — sklearn
        • Classification Conformal Prediction — sklearn
        • Quantile Regression Conformal Prediction — sklearn
        • Classification Conformal Prediction — PyTorch
        • Regression Conformal Prediction — PyTorch
        • Conformalized Credal Set Prediction — PyTorch.
        • Quantile Regression Conformal Prediction — PyTorch
      • Integrations
        • Mixing torch-uncertainty and probly
      • Method
        • DARE on Two Moons
        • Laplace Approximation on Two Moons
        • Mahalanobis OOD on Two Moons
        • DUQ on Two Moons
        • DDU on Two Moons
        • Het-Net on Two Moons
        • Credal Net Visualization
        • Evidential on Two Moons
        • Credal Wrapper Output Visualization
        • Credal Ensembling Visualization
        • Credal BNN Visualization
        • DARE on MNIST
        • SNGP Distance Awareness on 2D Toys
        • Laplace on MNIST
        • Credal BNN on MNIST
        • Credal Wrapper on MNIST
        • Credal Ensembling on MNIST
        • Credal Net on MNIST
        • SNGP on MNIST
        • Mahalanobis OOD on MNIST
        • Het-Net on MNIST
        • DUQ on MNIST
        • DDU on MNIST
        • Credal Relative Likelihood Visualization
        • Evidential on MNIST
        • Credal Relative Likelihood on MNIST
        • DEUP on Two Moons
        • DEUP on MNIST
      • Plot
        • Visualising OOD detection results
        • Plotting credal sets on the simplex
        • Plotting binary credal sets on an interval
        • Plotting credal sets on a spider (radar) chart
      • Pytraverse
        • A Brief Introduction to PyTraverse
      • Quantification
        • Uncertainty Quantification
        • Ensemble Regression Uncertainty
        • Ensemble Ordinal Classification Uncertainty
      • Release Highlights
      • Representation
        • Singleton credal set
        • Convex credal set
        • Discrete credal set
        • Distance-based credal set
        • Probability-intervals credal set
        • Working with ArraySample
      • Streaming
        • Streaming uncertainty with ARFRegressor
        • Streaming uncertainty with ARFClassifier
        • MC-Dropout uncertainty on a 2-D stream
      • Transformation
        • DropConnect on Two Moons
        • Deep Ensemble on Two Moons
        • MC Dropout on Two Moons
        • Natural Posterior Network on Two Moons
        • Bayesian Neural Network on Two Moons
        • Posterior Network on Two Moons
        • Sub-Ensemble on Two Moons
        • Bayesian Ensemble on Two Moons
        • BatchEnsemble on Two Moons
        • MC Dropout on MNIST
        • DropConnect on MNIST
        • Deep Ensemble on MNIST
        • Bayesian Neural Network on MNIST
        • Natural Posterior Network on MNIST
        • Bayesian Ensemble on MNIST
        • Posterior Network on MNIST
        • Sub-Ensemble on MNIST
        • BatchEnsemble on MNIST
  • API Reference
    • probly
      • calibrator
      • conformal_scores
        • absolute_error
          • absolute_error.jax
          • absolute_error.torch
            • compute_absolute_error_score_torch
            • compute_absolute_error_score_torch_sample
        • aps
          • aps.flax
          • aps.torch
            • compute_aps_score_torch
        • cqr
          • cqr.jax
          • cqr.torch
            • compute_cqr_score_torch
        • cqr_r
          • cqr_r.jax
          • cqr_r.torch
            • compute_cqr_r_score_torch
        • dirichlet_relative_likelihood
          • dirichlet_relative_likelihood.torch
            • compute_dirichlet_rl_score_torch
            • compute_dirichlet_rl_score_torch_dirichlet
        • inner_product
          • inner_product.torch
            • compute_inner_product_score_torch
        • kullback_leibler
          • kullback_leibler.torch
            • compute_kl_divergence_score_torch
        • lac
          • lac.jax
            • compute_lac_score_jax
          • lac.torch
            • compute_lac_score_torch
            • compute_lac_score_torch_categorical
            • compute_lac_score_torch_sample
        • raps
          • raps.jax
          • raps.torch
            • compute_raps_score_torch
        • saps
          • saps.jax
          • saps.torch
            • compute_saps_score_torch
        • total_variation
          • total_variation.torch
            • compute_tv_score_torch
        • uacqr
          • uacqr.jax
          • uacqr.torch
            • compute_uacqr_score_func_torch
        • wasserstein_distance
          • wasserstein_distance.torch
            • compute_wasserstein_distance_score_torch
      • datasets
        • torch
          • Benthic
          • CIFAR10C
          • CIFAR10H
          • CIFAR10HDCIC
          • DCICDataset
          • ImageNetReaL
          • MiceBone
          • Pig
          • Plankton
          • QualityMRI
          • Synthetic
          • Treeversity1
          • Treeversity6
          • Turkey
      • decider
        • categorical_distribution
          • categorical_distribution.array
          • categorical_distribution.torch
      • evaluation
        • active_learning
          • active_learning.loop
            • ALState
            • active_learning_steps
          • active_learning.metrics
            • compute_nauc
          • active_learning.pool
            • active_learning.pool.array
              • NumpyActiveLearningPool
            • active_learning.pool.torch
              • TorchActiveLearningPool
          • active_learning.strategies
            • active_learning.strategies.array
            • active_learning.strategies.torch
        • ood
          • evaluate_ood
          • out_of_distribution_detection_aupr
          • out_of_distribution_detection_auroc
          • out_of_distribution_detection_fnr_at_x_tpr
          • out_of_distribution_detection_fpr_at_x_tpr
          • parse_dynamic_metric
        • tasks
          • selective_prediction
      • integrations
        • torch_uncertainty
          • TorchUncertaintyCalibratedLogitRepresenter
          • TorchUncertaintyConformalRepresenter
          • TorchUncertaintySampleRepresenter
      • layers
        • flax
          • BatchEnsembleConv
          • BatchEnsembleLinear
          • DropConnectLinear
        • torch
          • BatchEnsembleConv2d
          • BatchEnsembleLinear
          • BayesConv2d
          • BayesLinear
          • DropConnectLinear
          • GaussianMixtureHead
          • HeteroscedasticLayer
          • IRDHead
          • IntBatchNorm1d
          • IntBatchNorm2d
          • IntConv2d
          • IntLinear
          • IntSoftmax
          • MahalanobisHead
          • NatPNClassHead
          • NatPNRegHead
          • NormalInverseGammaLinear
          • RadialNormalizingFlow
          • RadialNormalizingFlowStack
          • RegressionHead
          • SNCoeffParametrization
          • SNGPLayer
          • SharedMaskDropout
          • apply_spectral_norm_to_encoder
          • pack_interval
          • unpack_interval
      • lazy_types
      • method
        • batchensemble
        • bayesian
        • calibration
        • cast
        • conformal
        • conformal_credal_set
        • credal_bnn
        • credal_ensembling
        • credal_net
          • CredalNetPredictor
        • credal_relative_likelihood
        • credal_wrapper
        • dare
        • ddu
        • deup
          • deup.torch
            • ErrorPredictionHead
            • LogDUEVariance
            • LogGMMDensity
            • LogMAFDensity
            • LogMCDropoutVariance
            • StationarizingFeatureProvider
            • TorchDEUPPredictor
            • TorchDEUPRepresentation
        • dropconnect
        • dropout
        • duq
        • efficient_credal_prediction
        • ensemble
        • evidential
          • evidential.classification
          • evidential.regression
        • graph_posterior_network
        • het_net
        • laplace
          • laplace.torch
            • LaplaceRepresenter
        • mahalanobis
        • natural_posterior_network
        • posterior_network
        • prior_network
          • PriorNetworkPredictor
        • sngp
        • subensemble
      • metrics
        • array
          • auc_numpy
          • average_precision_score_numpy
          • precision_recall_curve_numpy
          • roc_auc_score_numpy
          • roc_curve_numpy
        • jax
          • auc_jax
          • average_precision_score_jax
          • precision_recall_curve_jax
          • roc_auc_score_jax
          • roc_curve_jax
        • torch
          • auc_torch
          • average_precision_score_torch
          • precision_recall_curve_torch
          • roc_auc_score_torch
          • roc_curve_torch
      • plot
        • config
          • PlotConfig
        • credal
          • credal.plot
            • plot_credal_set
        • ood
          • plot_histogram
          • plot_pr_curve
          • plot_roc_curve
        • utils
      • predictor
        • laplace
        • sklearn
          • predict_sklearn
      • quantification
        • decomposition
          • decomposition.decomposition
            • AdditiveDecomposition
            • AleatoricDecomposition
            • AleatoricEpistemicDecomposition
            • AleatoricEpistemicTotalDecomposition
            • AleatoricTotalDecomposition
            • CachingDecomposition
            • ConstantTotalDecomposition
            • Decomposer
            • Decomposition
            • EpistemicDecomposition
            • EpistemicTotalDecomposition
            • TotalDecomposition
          • decomposition.entropy
          • decomposition.ordinal
          • decomposition.scoring_rule
          • decomposition.spectral
            • decomposition.spectral.torch
              • SpectralDecomposition
              • spectral_decomposition
              • torch_embedding_sample_sample_spectral_decomposition
          • decomposition.variance
          • decomposition.wasserstein
          • decomposition.zero_one
        • measure
        • notion
          • AleatoricUncertainty
          • EpistemicUncertainty
          • Notion
          • TotalUncertainty
        • scoring_rule
          • scoring_rule.array
            • array_brier_loss_vector
            • array_log_loss_vector
            • array_spherical_loss_vector
            • array_zero_one_loss_vector
          • scoring_rule.torch
            • torch_brier_loss_vector
            • torch_log_loss_vector
            • torch_spherical_loss_vector
            • torch_zero_one_loss_vector
      • representation
        • array_like
          • ArrayFlagsLike
          • ArrayLike
          • NumpyArrayLike
          • NumpyArrayLikeConvertible
          • NumpyArrayLikeImplementation
        • conformal_set
          • conformal_set.array
            • ArrayIntervalConformalSet
            • ArrayOneHotConformalSet
          • conformal_set.torch
            • TorchIntervalConformalSet
            • TorchOneHotConformalSet
        • credal_set
          • credal_set.array
            • ArrayCategoricalCredalSet
            • ArrayConvexCredalSet
            • ArrayDiscreteCredalSet
            • ArrayDistanceBasedCredalSet
            • ArrayProbabilityIntervalsCredalSet
            • ArraySingletonCredalSet
          • credal_set.array_functions
          • credal_set.torch
            • TorchCategoricalCredalSet
            • TorchConvexCredalSet
            • TorchDirichletLevelSetCredalSet
            • TorchDistanceBasedCredalSet
            • TorchProbabilityIntervalsCredalSet
        • distribution
          • distribution.array_bernoulli
            • ArrayBernoulliDistribution
            • ArrayBernoulliDistributionSample
            • ArrayLogitBernoulliDistribution
            • ArrayProbabilityBernoulliDistribution
          • distribution.array_categorical
            • ArrayCategoricalDistribution
            • ArrayCategoricalDistributionSample
            • ArrayLogitCategoricalDistribution
            • ArrayProbabilityCategoricalDistribution
          • distribution.array_dirichlet
            • ArrayDirichletDistribution
          • distribution.array_gaussian
            • ArrayGaussianDistribution
            • ArrayGaussianDistributionSample
          • distribution.torch_bernoulli
            • TorchBernoulliDistribution
            • TorchBernoulliDistributionSample
            • TorchLogitBernoulliDistribution
            • TorchProbabilityBernoulliDistribution
          • distribution.torch_categorical
            • TorchCategoricalDistribution
            • TorchCategoricalDistributionSample
            • TorchLogitCategoricalDistribution
            • TorchProbabilityCategoricalDistribution
          • distribution.torch_dirichlet
            • TorchDirichletDistribution
          • distribution.torch_gaussian
            • TorchGaussianDistribution
            • TorchGaussianDistributionSample
          • distribution.torch_mixture
            • TorchDirichletMixtureDistribution
            • TorchMixtureDistribution
          • distribution.torch_sparse_log_categorical
            • TorchSparseLogCategoricalDistribution
            • TorchSparseLogCategoricalDistributionSample
        • embedding
          • embedding.torch
            • TorchEmbedding
            • TorchEmbeddingSample
            • TorchEmbeddingSampleSample
        • jax_like
          • JaxLikeImplementation
        • representation
          • Representation
        • sample
          • sample.array
            • ArraySample
          • sample.array_functions
            • ArraySampleCreator
            • ArraySampleInternals
            • array_apply_along_axis_function
            • array_concatenate_function
            • array_copy_function
            • array_expand_dims_function
            • array_function_override
            • array_internals_override
            • array_matrix_transpose
            • array_reduction_function
            • array_reshape_function
            • array_sample_axis_preserving_function
            • array_sample_internals
            • array_squeeze_function
            • array_stack_function
            • array_swapaxes_function
            • array_transpose
            • track_sample_axis_after_reduction
          • sample.axis_tracking
            • ArrayIndex
            • AxisTrackingResult
            • track_axis
          • sample.jax
            • JaxArraySample
          • sample.jax_axis_tracking
          • sample.torch
            • TorchSample
          • sample.torch_axis_tracking
          • sample.torch_functions
            • TorchSampleCreator
            • TorchSampleInternals
            • torch_adjoint_function
            • torch_cat_function
            • torch_function_override
            • torch_internals_override
            • torch_movedim_function
            • torch_permute_function
            • torch_reduction_function
            • torch_sample_internals
            • torch_stack_function
            • torch_transpose_function
            • track_sample_dim_after_reduction
        • text_generation
          • text_generation.torch
            • TorchTextGeneration
            • TorchTextGenerationSample
            • TorchTextGenerationSampleSample
            • TorchTokenGeneration
        • torch_functions
          • torch_average
        • torch_like
          • TorchLike
          • TorchLikeConvertible
          • TorchLikeImplementation
      • representer
        • clarifier
          • clarifier.huggingface
            • HFQuestionClarifier
        • credal_set
          • ConvexCredalSetRepresenter
          • ProbabilityIntervalsRepresenter
          • RepresentativeConvexCredalSetRepresenter
          • SampleMeanConvexCredalSetRepresenter
        • credal_set_torch
          • torch_compute_representative_sample
        • embedder
          • embedder.huggingface
            • HFTextEmbedder
            • SentenceTransformerLike
        • sampler
          • sampler.flax
            • register_forced_train_mode
          • sampler.huggingface
            • HFTextGenerationSampler
            • load_model
          • sampler.sklearn
            • register_forced_fitted_already_mode
          • sampler.torch
            • register_forced_train_mode
        • semantic_clustering
          • semantic_clustering.huggingface
            • HFGreedySemanticClusterer
            • HFSemanticClusterer
      • train
        • bayesian
          • bayesian.torch
            • ELBOLoss
            • collect_kl_divergence
        • calibration
          • calibration.torch
            • ExpectedCalibrationError
            • FocalLoss
            • LabelRelaxationLoss
            • LabelSmoothingLoss
        • credal
          • credal.torch
            • intersection_probability_ce_loss
        • dare
          • dare.torch
            • dare_regularizer
        • evidential
          • evidential.common
          • evidential.torch
            • der_loss
            • dirichlet_entropy
            • evidential_ce_loss
            • evidential_kl_divergence
            • evidential_log_loss
            • evidential_mse_loss
            • evidential_nignll_loss
            • evidential_regression_regularization
            • ird_loss
            • kl_dirichlet
            • lop_gpn_loss
            • lp_fn
            • make_in_domain_target_alpha
            • make_ood_target_alpha
            • mixture_uce_loss
            • natpn_loss
            • normal_wishart_log_prob
            • pn_loss
            • postnet_loss
            • predictive_probs
            • regularization_fn
            • rpn_distillation_loss
            • rpn_loss
            • rpn_ng_kl
            • rpn_prior
            • unified_evidential_train
      • transformation
        • batchensemble
        • bayesian
        • bayesian_ensemble
        • calibration
          • calibration.sklearn
            • SklearnIdentityLogitEstimator
            • SklearnVectorScalingPredictor
            • calibrate_sklearn_calibrated_classifier_cv
            • generate_sklearn_scaling_calibrator
          • calibration.torch
            • TorchAffineLogitCalibrationPredictor
            • TorchIdentityLogitModel
            • TorchIsotonicCalibrationPredictor
            • generate_torch_scaling_calibrator
          • sklearn_identity_logit_estimator
          • torch_identity_logit_model
        • cast
        • class_bias_ensemble
        • conformal
          • conformal.flax
            • FlaxConformalSetPredictor
          • conformal.sklearn
            • SklearnConformalSetPredictor
          • conformal.torch
            • TorchConformalSetPredictor
        • conformal_credal_set
          • conformal_credal_set.torch
            • TorchConformalCredalSetPredictor
        • dirichlet_clipped_exp_one_activation
        • dirichlet_exp_activation
        • dirichlet_softplus_activation
        • dropconnect
        • dropout
        • ensemble
        • interval_classifier
        • natural_posterior_network
          • natural_posterior_network.torch
            • TorchNaturalPosteriorNetwork
        • normal_inverse_gamma_head
        • posterior_network
        • subensemble
        • transformation
          • PredictorTransformation
          • PredictorTransformationDecorator
          • predictor_transformation
      • traverse_nn
        • flax
        • reset_traverser
        • torch
        • torch_utils
        • utils
          • get_output_dim
      • utils
        • iterable
          • first_element
        • quantile
          • quantile.jax
          • quantile.torch
        • switchdispatch
        • torch
          • intersection_probability
          • temperature_softmax
          • torch_collect_outputs
          • torch_entropy
          • torch_reset_all_parameters
  • References and Further Reading
  • FAQ and Troubleshooting
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Plot¶

Examples concerning the probly.plot module.

Visualising OOD detection results

Visualising OOD detection results

Plotting credal sets on the simplex

Plotting credal sets on the simplex

Plotting binary credal sets on an interval

Plotting binary credal sets on an interval

Plotting credal sets on a spider (radar) chart

Plotting credal sets on a spider (radar) chart
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Visualising OOD detection results
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DEUP on MNIST
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