Index _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | V | X | Z _ __call__() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] A add_module() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] adjust_learning_rate() (in module cleanlab.coteaching) apply() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] assert_inputs_are_valid() (in module cleanlab.util) B baseline_argmax() (in module cleanlab.baseline_methods) baseline_argmax_calibrated_confusion_matrix() (in module cleanlab.baseline_methods) baseline_argmax_confusion_matrix() (in module cleanlab.baseline_methods) batch_size (cleanlab.models.mnist_pytorch.CNN attribute), [1] bfloat16() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] buffers() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] C calibrate_confident_joint() (in module cleanlab.latent_estimation) call_bn() (in module cleanlab.models.cifar_cnn), [1] children() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] cleanlab.baseline_methods module cleanlab.classification module cleanlab.coteaching module cleanlab.latent_algebra module cleanlab.latent_estimation module cleanlab.models.cifar_cnn module, [1] cleanlab.models.mnist_pytorch module, [1] cleanlab.noise_generation module cleanlab.polyplex module cleanlab.pruning module cleanlab.util module clip_noise_rates() (in module cleanlab.util) clip_values() (in module cleanlab.util) CNN (class in cleanlab.models.cifar_cnn), [1] (class in cleanlab.models.mnist_pytorch), [1] compute_confident_joint() (in module cleanlab.latent_estimation) compute_inv_noise_matrix() (in module cleanlab.latent_algebra) compute_noise_matrix_from_inverse() (in module cleanlab.latent_algebra) compute_ps_py_inv_noise_matrix() (in module cleanlab.latent_algebra) compute_py() (in module cleanlab.latent_algebra) compute_py_inv_noise_matrix() (in module cleanlab.latent_algebra) compute_pyx() (in module cleanlab.latent_algebra) confusion_matrix() (in module cleanlab.util) converge_estimates() (in module cleanlab.latent_estimation) cpu() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] cuda() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] D dataset (cleanlab.models.mnist_pytorch.CNN attribute), [1] double() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] dump_patches (cleanlab.models.cifar_cnn.CNN attribute), [1] (cleanlab.models.mnist_pytorch.SimpleNet attribute), [1] E epochs (cleanlab.models.mnist_pytorch.CNN attribute), [1] estimate_confident_joint_and_cv_pred_proba() (in module cleanlab.latent_estimation) estimate_confident_joint_from_probabilities() (in module cleanlab.latent_estimation) estimate_cv_predicted_probabilities() (in module cleanlab.latent_estimation) estimate_joint() (in module cleanlab.latent_estimation) estimate_latent() (in module cleanlab.latent_estimation) estimate_noise_matrices() (in module cleanlab.latent_estimation) estimate_pu_f1() (in module cleanlab.util) estimate_py_and_noise_matrices_from_probabilities() (in module cleanlab.latent_estimation) estimate_py_noise_matrices_and_cv_pred_proba() (in module cleanlab.latent_estimation) eval() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] evaluate() (in module cleanlab.coteaching) extra_repr() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] F fit() (cleanlab.classification.LearningWithNoisyLabels method) (cleanlab.models.mnist_pytorch.CNN method), [1], [2], [3] float() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] forget_rate_scheduler() (in module cleanlab.coteaching) forward() (cleanlab.models.cifar_cnn.CNN method), [1], [2], [3] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] G generate_n_rand_probabilities_that_sum_to_m() (in module cleanlab.noise_generation) generate_noise_matrix() (in module cleanlab.noise_generation) generate_noise_matrix_from_trace() (in module cleanlab.noise_generation) generate_noisy_labels() (in module cleanlab.noise_generation) get_buffer() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] get_extra_state() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] get_mnist_dataset() (in module cleanlab.models.mnist_pytorch), [1] get_noise_indices() (in module cleanlab.pruning) get_parameter() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] get_params() (cleanlab.classification.LearningWithNoisyLabels method) (cleanlab.models.mnist_pytorch.CNN method), [1] get_sklearn_digits_dataset() (in module cleanlab.models.mnist_pytorch), [1] get_submodule() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] H half() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] I initialize_lr_scheduler() (in module cleanlab.coteaching) int2onehot() (in module cleanlab.util) is_compatible() (cleanlab.util.VersionWarning method) J joint_bounds() (in module cleanlab.polyplex) joint_min_max() (in module cleanlab.polyplex) K keep_at_least_n_per_class() (in module cleanlab.pruning) L LearningWithNoisyLabels (class in cleanlab.classification) load_state_dict() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] loader (cleanlab.models.mnist_pytorch.CNN attribute), [1] log_interval (cleanlab.models.mnist_pytorch.CNN attribute), [1] loss_coteaching() (in module cleanlab.coteaching) lr (cleanlab.models.mnist_pytorch.CNN attribute), [1] M module cleanlab.baseline_methods cleanlab.classification cleanlab.coteaching cleanlab.latent_algebra cleanlab.latent_estimation cleanlab.models.cifar_cnn, [1] cleanlab.models.mnist_pytorch, [1] cleanlab.noise_generation cleanlab.polyplex cleanlab.pruning cleanlab.util modules() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] momentum (cleanlab.models.mnist_pytorch.CNN attribute), [1] multiclass_crossval_predict() (in module cleanlab.pruning) N named_buffers() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] named_children() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] named_modules() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] named_parameters() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] no_cuda (cleanlab.models.mnist_pytorch.CNN attribute), [1] noise_matrix_is_valid() (in module cleanlab.noise_generation) num_label_errors() (in module cleanlab.latent_estimation) O onehot2int() (in module cleanlab.util) order_label_errors() (in module cleanlab.pruning) P parameters() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] predict() (cleanlab.classification.LearningWithNoisyLabels method) (cleanlab.models.mnist_pytorch.CNN method), [1], [2], [3] predict_proba() (cleanlab.classification.LearningWithNoisyLabels method) (cleanlab.models.mnist_pytorch.CNN method), [1], [2], [3] print_inverse_noise_matrix() (in module cleanlab.util) print_joint_matrix() (in module cleanlab.util) print_noise_matrix() (in module cleanlab.util) print_square_matrix() (in module cleanlab.util) R randomly_distribute_N_balls_into_K_bins() (in module cleanlab.noise_generation) reduce_prune_counts() (in module cleanlab.pruning) register_backward_hook() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] register_buffer() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] register_forward_hook() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] register_forward_pre_hook() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] register_full_backward_hook() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] register_parameter() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] remove_noise_from_class() (in module cleanlab.util) requires_grad_() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] round_preserving_row_totals() (in module cleanlab.util) round_preserving_sum() (in module cleanlab.util) S score() (cleanlab.classification.LearningWithNoisyLabels method) seed (cleanlab.models.mnist_pytorch.CNN attribute), [1] set_extra_state() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] set_params() (cleanlab.classification.LearningWithNoisyLabels method) (cleanlab.models.mnist_pytorch.CNN method), [1] share_memory() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] SimpleNet (class in cleanlab.models.mnist_pytorch), [1] slope_intercept() (in module cleanlab.polyplex) state_dict() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] T T_destination (cleanlab.models.cifar_cnn.CNN attribute), [1] (cleanlab.models.mnist_pytorch.SimpleNet attribute), [1] test_batch_size (cleanlab.models.mnist_pytorch.CNN attribute), [1] to() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] to_empty() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] train() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] (in module cleanlab.coteaching) training (cleanlab.models.cifar_cnn.CNN attribute), [1] (cleanlab.models.mnist_pytorch.SimpleNet attribute), [1] type() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] V value_counts() (in module cleanlab.util) VersionWarning (class in cleanlab.util) X xpu() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1] Z zero_grad() (cleanlab.models.cifar_cnn.CNN method), [1] (cleanlab.models.mnist_pytorch.SimpleNet method), [1]