"""UMAP algorithm."""
# Author: Hugues Van Assel <vanasselhugues@gmail.com>
#
# License: BSD 3-Clause License
from typing import Dict, Optional, Union, Type
import torch
import numpy as np
from torchdr.affinity import UMAPAffinity
from torchdr.neighbor_embedding.base import SampledNeighborEmbedding
from torchdr.distance import symmetric_pairwise_distances_indices
from scipy.optimize import curve_fit
# from umap/umap/umap_.py
def find_ab_params(spread, min_dist):
"""Fit a, b params as in UMAP.
Fit (a, b) for the differentiable curve used in lower
dimensional fuzzy simplicial complex construction. We want the
smooth curve (from a pre-defined family with simple gradient) that
best matches an offset exponential decay.
"""
def curve(x, a, b):
return 1.0 / (1.0 + a * x ** (2 * b))
xv = np.linspace(0, spread * 3, 300)
yv = np.zeros(xv.shape)
yv[xv < min_dist] = 1.0
yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread)
params, covar = curve_fit(curve, xv, yv)
return params[0].item(), params[1].item()
[docs]
class UMAP(SampledNeighborEmbedding):
r"""UMAP introduced in :cite:`mcinnes2018umap` and further studied in :cite:`damrich2021umap`.
It uses a :class:`~torchdr.UMAPAffinity` as input
affinity :math:`\mathbf{P}`.
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
----------
n_neighbors : float, optional
Number of nearest neighbors.
n_components : int, optional
Dimension of the embedding space.
min_dist : float, optional
Minimum distance between points in the embedding space.
spread : float, optional
The effective scale of the embedded points. Used to configure the UMAPAffinityOut.
a : float, optional
Parameter for the Student t-distribution.
b : float, optional
Parameter for the Student t-distribution.
lr : float, optional
Learning rate for the algorithm, by default 1e-1.
optimizer : str or torch.optim.Optimizer, optional
Name of an optimizer from torch.optim or an optimizer class.
Default is "SGD".
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 "LinearLR".
scheduler_kwargs : dict, 'auto', or None, optional
Additional keyword arguments for the scheduler. Default is 'auto', which
corresponds to a linear decay from the learning rate to 0 for `LinearLR`.
init : {'normal', 'pca', 'umap_spectral'} or torch.Tensor of shape (n_samples, output_dim), optional
Initialization for the embedding Z, default 'pca'. 'umap_spectral' uses the
original UMAP package's spectral initialization.
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 "faiss".
verbose : bool, optional
Verbosity, by default False.
random_state : float, optional
Random seed for reproducibility, by default None.
max_iter_affinity : int, optional
Number of maximum iterations for the input affinity computation.
metric_in : {'euclidean', 'manhattan'}, optional
Metric to use for the input affinity, by default 'euclidean'.
metric_out : {'euclidean', 'manhattan'}, optional
Metric to use for the output affinity, by default 'euclidean'.
n_negatives : int, optional
Number of negative samples for the noise-contrastive loss, by default 10.
check_interval : int, optional
Check interval for the algorithm, by default 50.
discard_NNs : bool, optional
Whether to discard the nearest neighbors from the negative sampling.
Default is False.
compile : bool, optional
Whether to compile the algorithm using torch.compile. Default is False.
""" # noqa: E501
def __init__(
self,
n_neighbors: float = 30,
n_components: int = 2,
min_dist: float = 0.1,
spread: float = 1.0,
a: Optional[float] = None,
b: Optional[float] = None,
lr: float = 1e0,
optimizer: Union[str, Type[torch.optim.Optimizer]] = "SGD",
optimizer_kwargs: Union[Dict, str] = None,
scheduler: Optional[
Union[str, Type[torch.optim.lr_scheduler.LRScheduler]]
] = "LinearLR",
scheduler_kwargs: Union[Dict, str, None] = "auto",
init: str = "pca",
init_scaling: float = 1e-4,
min_grad_norm: float = 1e-7,
max_iter: int = 1000,
device: Optional[str] = None,
backend: Optional[str] = "faiss",
verbose: bool = False,
random_state: Optional[float] = None,
max_iter_affinity: int = 100,
metric_in: str = "sqeuclidean",
negative_sample_rate: int = 5,
check_interval: int = 50,
discard_NNs: bool = False,
compile: bool = False,
**kwargs,
):
self.n_neighbors = n_neighbors
self.min_dist = min_dist
self.spread = spread
self.metric_in = metric_in
self.max_iter_affinity = max_iter_affinity
self.negative_sample_rate = negative_sample_rate
self.metric_out = "sqeuclidean"
self.sparsity = True
self._use_direct_gradients = True
self._eps = 1e-3
if a is None or b is None:
a, b = find_ab_params(self.spread, self.min_dist)
self._a = a
self._b = b
self.n_negatives = int(self.negative_sample_rate * self.n_neighbors)
affinity_in = UMAPAffinity(
n_neighbors=n_neighbors,
metric=metric_in,
max_iter=max_iter_affinity,
device=device,
backend=backend,
verbose=verbose,
sparsity=self.sparsity,
compile=compile,
)
super().__init__(
affinity_in=affinity_in,
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,
check_interval=check_interval,
discard_NNs=discard_NNs,
compile=compile,
n_negatives=self.n_negatives,
**kwargs,
)
[docs]
def on_affinity_computation_end(self):
super().on_affinity_computation_end()
# Remove small affinity edges
A_max = self.affinity_in_.max()
threshold = A_max / self.max_iter
small_affinity_edges = self.affinity_in_ <= threshold
if self.verbose:
kept_pct = (~small_affinity_edges).float().mean().item() * 100
self.logger.info(f"Keeping {kept_pct:.1f}% of affinity edges.")
self.affinity_in_.add_(1e-3).reciprocal_().mul_(A_max)
self.affinity_in_.masked_fill_(
small_affinity_edges, float("inf")
) # avoid updating these edges
self.epochs_per_sample = self.affinity_in_
self.epoch_of_next_sample = self.epochs_per_sample.clone()
def _compute_attractive_gradients(self):
D = symmetric_pairwise_distances_indices(
self.embedding_,
metric=self.metric_out,
indices=self.NN_indices_,
)
positive_edges = D > 0
D_ = 1 + self._a * D**self._b
D.pow_(self._b - 1)
D.mul_(2 * self._a * self._b).div_(D_)
D.masked_fill_(~positive_edges, 0) # prevent infinities when b < 1
# UMAP keeps a per-edge counter (epoch_of_next_sample) so that stronger edges
# (higher affinity → smaller epochs_per_sample) get updated more often.
# Use tensor iteration counter from base class for torch compile compatibility
self.mask_affinity_in_ = self.epoch_of_next_sample <= self.n_iter_ + 1
self.epoch_of_next_sample[self.mask_affinity_in_] += self.epochs_per_sample[
self.mask_affinity_in_
]
D.masked_fill_(~self.mask_affinity_in_, 0)
diff = self.embedding_.unsqueeze(1) - self.embedding_[self.NN_indices_]
grad = torch.einsum("ijk,ij->ik", diff, D)
grad.clamp_(-4, 4) # clamp as in umap repo
return grad
def _compute_repulsive_gradients(self):
D = symmetric_pairwise_distances_indices(
self.embedding_,
metric=self.metric_out,
indices=self.neg_indices_,
)
D_ = 1 + self._a * D**self._b
D.add_(self._eps)
D.mul_(D_)
D.reciprocal_().mul_(-2 * self._b)
# Filter to keep 'negative_sample_rate' negative edges per positive edge.
neg_counts = (self.mask_affinity_in_.sum(dim=1) * self.negative_sample_rate).to(
torch.long
)
col_idx = torch.arange(self.n_negatives, device=self.embedding_.device)
filtered_edges = col_idx[None, :].ge(neg_counts[:, None])
D.masked_fill_(filtered_edges, 0)
diff = self.embedding_.unsqueeze(1) - self.embedding_[self.neg_indices_]
grad = torch.einsum("ijk,ij->ik", diff, D)
grad.clamp_(-4, 4) # clamp as in umap repo
return grad
