TSNE

class torchdr.TSNE(perplexity: float = 30, n_components: int = 2, lr: float | str = 'auto', optimizer: str = 'auto', optimizer_kwargs: dict | str = 'auto', scheduler: str = 'constant', scheduler_kwargs: dict | None = None, init: str = 'pca', init_scaling: float = 0.0001, min_grad_norm: float = 1e-07, max_iter: int = 2000, device: str | None = None, backend: str | None = None, verbose: bool = False, random_state: float | None = None, early_exaggeration_coeff: float = 12.0, early_exaggeration_iter: int = 250, tol_affinity: float = 0.001, max_iter_affinity: int = 100, metric_in: str = 'sqeuclidean', metric_out: str = 'sqeuclidean', sparsity: bool = True, **kwargs)

Bases: SparseNeighborEmbedding

t-Stochastic Neighbor Embedding (t-SNE) introduced in [Van der Maaten and Hinton, 2008].

It uses a EntropicAffinity as input affinity \(\mathbf{P}\) and a StudentAffinity as output affinity \(\mathbf{Q}\).

The loss function is defined as:

\[-\sum_{ij} P_{ij} \log Q_{ij} + \log \Big( \sum_{ij} Q_{ij} \Big) \:.\]

Parameters:

• perplexity (float) – Number of ‘effective’ nearest neighbors. Consider selecting a value between 2 and the number of samples. Different values can result in significantly different results.

• n_components (int, optional) – Dimension of the embedding space.

• lr (float or 'auto', optional) – Learning rate for the algorithm, by default ‘auto’.

• optimizer ({'SGD', 'Adam', 'NAdam', 'auto}, optional) – Which pytorch optimizer to use, by default ‘auto’.

• optimizer_kwargs (dict or 'auto', optional) – Arguments for the optimizer, by default ‘auto’.

• scheduler ({'constant', 'linear'}, optional) – Learning rate scheduler.

• scheduler_kwargs (dict, optional) – Arguments for the scheduler, by default None.

• init ({'normal', 'pca'} or torch.Tensor of shape (n_samples, output_dim), optional) – Initialization for the embedding Z, default ‘pca’.

• init_scaling (float, optional) – Scaling factor for the initialization, by default 1e-4.

• min_grad_norm (float, optional) – Precision threshold at which the algorithm stops, by default 1e-7.

• max_iter (int, optional) – Number of maximum iterations for the descent algorithm, by default 2000.

• device (str, optional) – Device to use, by default “auto”.

• backend ({"keops", "faiss", None}, optional) – Which backend to use for handling sparsity and memory efficiency. Default is None.

• verbose (bool, optional) – Verbosity, by default False.

• random_state (float, optional) – Random seed for reproducibility, by default None.

• early_exaggeration_coeff (float, optional) – Coefficient for the attraction term during the early exaggeration phase. By default 12.0 for early exaggeration.

• early_exaggeration_iter (int, optional) – Number of iterations for early exaggeration, by default 250.

• tol_affinity (_type_, optional) – Precision threshold for the entropic affinity root search.

• max_iter_affinity (int, optional) – Number of maximum iterations for the entropic affinity root search.

• metric_in ({'sqeuclidean', 'manhattan'}, optional) – Metric to use for the input affinity, by default ‘sqeuclidean’.

• metric_out ({'sqeuclidean', 'manhattan'}, optional) – Metric to use for the output affinity, by default ‘sqeuclidean’.

Examples using TSNE:

TSNE embedding of the swiss roll dataset

TSNE embedding of the swiss roll dataset

Neighbor Embedding on genomics & equivalent affinity matcher formulation

Neighbor Embedding on genomics & equivalent affinity matcher formulation