NegativeSamplingNeighborEmbedding#

class torchdr.NegativeSamplingNeighborEmbedding(affinity_in: Affinity, affinity_out: Affinity | None = None, kwargs_affinity_out: Dict | None = None, n_components: int = 2, lr: float | str = 1.0, optimizer: str | Type[Optimizer] = 'SGD', optimizer_kwargs: Dict | str = 'auto', scheduler: str | Type[LRScheduler] | None = None, scheduler_kwargs: Dict | str | None = 'auto', min_grad_norm: float = 1e-07, max_iter: int = 2000, init: str = 'pca', init_scaling: float = 0.0001, device: str = 'auto', backend: str | FaissConfig | None = None, verbose: bool = False, random_state: float | None = None, early_exaggeration_coeff: float = 1.0, early_exaggeration_iter: int | None = None, repulsion_strength: float = 1.0, n_negatives: int = 5, check_interval: int = 50, discard_NNs: bool = False, compile: bool = False, **kwargs)[source]#

Bases: NeighborEmbedding

Neighbor embedding that approximates the repulsive term via negative sampling.

This class extends NeighborEmbedding for methods that avoid the \(O(n^2)\) cost of computing the repulsive term over all point pairs. Instead, a fixed number of negative samples (n_negatives) are drawn uniformly per point at each iteration, reducing the repulsive cost to \(O(n)\).

Negative sampling details:

  • At each iteration, n_negatives indices are sampled uniformly (excluding the point itself) for each point in the local chunk.

  • When discard_NNs is True, nearest neighbors are also excluded from the negative samples to avoid conflicting gradients.

  • The sampled indices are stored in neg_indices_ and refreshed every iteration via on_training_step_start().

Inherits distributed multi-GPU support from NeighborEmbedding.

Subclasses must implement _compute_attractive_loss() and _compute_repulsive_loss() (or the gradient equivalents).

Direct subclasses: UMAP, LargeVis, InfoTSNE, PACMAP.

Parameters:

  • affinity_in (Affinity) – The affinity object for the input space.

  • affinity_out (Affinity, optional) – The affinity object for the output embedding space. Default is None.

  • kwargs_affinity_out (dict, optional) – Additional keyword arguments for the affinity_out method.

  • n_components (int, optional) – Number of dimensions for the embedding. Default is 2.

  • lr (float or 'auto', optional) – Learning rate for the optimizer. Default is 1e0.

  • optimizer (str or torch.optim.Optimizer, optional) – Name of an optimizer from torch.optim or an optimizer class. Default is “SGD”. For best results, we recommend using “SGD” with ‘auto’ learning rate.

  • optimizer_kwargs (dict or 'auto', optional) – Additional keyword arguments for the optimizer. Default is ‘auto’, which sets appropriate momentum values for SGD based on early exaggeration phase.

  • scheduler (str or torch.optim.lr_scheduler.LRScheduler, optional) – Name of a scheduler from torch.optim.lr_scheduler or a scheduler class. Default is None (no scheduler).

  • scheduler_kwargs (dict, optional) – Additional keyword arguments for the scheduler. Default is “auto”, which corresponds to a linear decay from the learning rate to 0 for LinearLR.

  • min_grad_norm (float, optional) – Tolerance for stopping criterion. Default is 1e-7.

  • max_iter (int, optional) – Maximum number of iterations. Default is 2000.

  • init (str, optional) – Initialization method for the embedding. Default is “pca”.

  • init_scaling (float, optional) – Scaling factor for the initial embedding. Default is 1e-4.

  • device (str, optional) – Device to use for computations. Default is “auto”.

  • backend ({"keops", "faiss", None} or FaissConfig, optional) – Which backend to use for handling sparsity and memory efficiency. Can be: - “keops”: Use KeOps for memory-efficient symbolic computations - “faiss”: Use FAISS for fast k-NN computations with default settings - None: Use standard PyTorch operations - FaissConfig object: Use FAISS with custom configuration Default is None.

  • verbose (bool, optional) – Verbosity of the optimization process. Default is False.

  • random_state (float, optional) – Random seed for reproducibility. Default is None.

  • early_exaggeration_coeff (float, optional) – Coefficient for the attraction term during the early exaggeration phase. Default is 1.0.

  • early_exaggeration_iter (int, optional) – Number of iterations for early exaggeration. Default is None.

  • repulsion_strength (float, optional) – Strength of the repulsive term. Default is 1.0.

  • n_negatives (int, optional) – Number of negative samples to use. Default is 5.

  • check_interval (int, optional) – Number of iterations between two checks for convergence. Default is 50.

  • discard_NNs (bool, optional) – Whether to discard nearest neighbors from negative sampling. Default is False.

  • compile (bool, default=False) – Whether to use torch.compile for faster computation.

  • **kwargs – All other parameters (including distributed) are forwarded to NeighborEmbedding.

on_affinity_computation_end()[source]#

Build per-row exclusion indices for negative sampling.

on_training_step_start()[source]#

Sample negatives using a unified path for single- and multi-GPU.

Examples using NegativeSamplingNeighborEmbedding:#

Neighbor Embedding on genomics & equivalent affinity matcher formulation

Neighbor Embedding on genomics & equivalent affinity matcher formulation