"""LargeVis algorithm."""
# Author: Hugues Van Assel <vanasselhugues@gmail.com>
#
# License: BSD 3-Clause License
from torchdr.affinity import EntropicAffinity, StudentAffinity
from torchdr.neighbor_embedding.base import SampledNeighborEmbedding
from torchdr.utils import cross_entropy_loss, sum_output
[docs]
class LargeVis(SampledNeighborEmbedding):
r"""LargeVis algorithm introduced in :cite:`tang2016visualizing`.
It uses a :class:`~torchdr.EntropicAffinity` as input
affinity :math:`\mathbf{P}` and a :class:`~torchdr.StudentAffinity` as output
affinity :math:`\mathbf{Q}`.
The loss function is defined as:
.. math::
-\sum_{ij} P_{ij} \log Q_{ij} + \sum_{i,j \in \mathrm{Neg}(i)} \log (1 - Q_{ij})
where :math:`\mathrm{Neg}(i)` is the set of negatives samples for point :math:`i`.
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 3000.
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'.
n_negatives : int, optional
Number of negative samples for the repulsive loss.
sparsity : bool, optional
Whether to use sparsity mode for the input affinity. Default is True.
""" # noqa: E501
def __init__(
self,
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,
init: str = "pca",
init_scaling: float = 1e-4,
min_grad_norm: float = 1e-7,
max_iter: int = 3000,
device: str = None,
backend: str = None,
verbose: bool = False,
random_state: float = None,
early_exaggeration_coeff: float = 12.0,
early_exaggeration_iter: int = 250,
tol_affinity: float = 1e-3,
max_iter_affinity: int = 100,
metric_in: str = "sqeuclidean",
metric_out: str = "sqeuclidean",
n_negatives: int = 5,
sparsity: bool = True,
):
self.metric_in = metric_in
self.metric_out = metric_out
self.perplexity = perplexity
self.max_iter_affinity = max_iter_affinity
self.tol_affinity = tol_affinity
self.sparsity = sparsity
affinity_in = EntropicAffinity(
perplexity=perplexity,
metric=metric_in,
tol=tol_affinity,
max_iter=max_iter_affinity,
device=device,
backend=backend,
verbose=verbose,
sparsity=sparsity,
)
affinity_out = StudentAffinity(
metric=metric_out,
device=device,
backend=backend,
verbose=False,
)
super().__init__(
affinity_in=affinity_in,
affinity_out=affinity_out,
n_components=n_components,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
min_grad_norm=min_grad_norm,
max_iter=max_iter,
lr=lr,
scheduler=scheduler,
scheduler_kwargs=scheduler_kwargs,
init=init,
init_scaling=init_scaling,
device=device,
backend=backend,
verbose=verbose,
random_state=random_state,
early_exaggeration_coeff=early_exaggeration_coeff,
early_exaggeration_iter=early_exaggeration_iter,
n_negatives=n_negatives,
)
@sum_output
def _repulsive_loss(self):
indices = self._sample_negatives()
Q = self.affinity_out(self.embedding_, indices=indices)
Q = Q / (Q + 1)
return -(1 - Q).log() / self.n_samples_in_
def _attractive_loss(self):
Q = self.affinity_out(self.embedding_, indices=self.NN_indices_)
Q = Q / (Q + 1)
return cross_entropy_loss(self.PX_, Q)
