James Melville jlmelville

why use reticulate when you can make life really difficult for yourself?

Carry out the tdf-if procedure from https://umap-learn.readthedocs.io/en/latest/sparse.html, save it in a form palatable to R:

# change this
your_data_path = "/your/data/path"

import os

Ubuntu, CUDA, WSL2 and legacy keyrings

In the Ubuntu tutorial on enabling CUDA with WSL2, and specifically in part 3, you are directed to use apt-key to point Ubuntu at the correct toolkit package. On Ubuntu 22.04 this will lead to the following warning whenever you run apt-get update:

 Key is stored in legacy trusted.gpg keyring (/etc/apt/trusted.gpg), see the DEPRECATION section in apt-key(8) for details.

January 11 2025 I recently upgraded to Ubuntu 12.10 (oracular) and tried all this again. Some settings needed to be changed:

We're up to CUDA 12.5 now, but we also get GCC 14 with the latest ubuntu (I haven't tried any of this with clang). nvcc doesn't want anything above version 13, so you will want to sudo apt-get install g++-13 and then make sure nvcc and cmake know about it. Also, my attempts to adjust the C++ compiler did not affect the C compiler and it was still reporting version 14. I don't know if that would work, but I tried to also make it use version 13 also.
I also wanted to use a version of python set by pyenv, but unsurprisingly I also had to specifically set where to look for python and numpy libs.
Weirdly, I had to edit /usr/share/cmake-3.30/Modules/FindOpenMP.cmake. If your line 628 says something like:

	# Create the degree matrix D from an affinity/adjacency matrix
	degmat <- function(X) {
	diag(colSums(X))
	}

	# A random affinity matrix
	randw <- function(n = 3) {
	X <- matrix(rnorm(n * n), nrow = n)
	X <- X * X
	X <- t(X) + X

	import collections
	import pprint

	import numpy as np
	import scipy as sp
	import scipy.linalg

	AffDeg = collections.namedtuple("AffDeg", ["W", "D"])
	Lap = collections.namedtuple("Lap", ["L", "Lsym", "Lrw", "P"])
	Eig = collections.namedtuple("Eig", ["vectors", "values", "lengths"])

	benchmark <- function(datasets = smallvis_datasets(), fun = smallvis::smallvis,
	vfun = NULL, mfun = NULL, ...) {
	varargs <- list(...)
	if (is.null(varargs$ret_extra)) {
	varargs$ret_extra <- TRUE
	}

	dataset_names <- names(datasets)
	varargs_orig <- varargs

	whitening_matrix <- function(X, eps = 1e-8, zca = FALSE) {
	X <- scale(X, center = TRUE, scale = FALSE)
	s <- svd(X, nu = 0)

	Linvsqr <- sqrt(nrow(X) - 1) / (s$d + eps)

	W <- Linvsqr * t(s$v)
	if (zca) {
	W <- s$v %*% W
	}

	import os
	import pickle
	from pathlib import Path


	def read_data(dataset, suffix=None, dir=None, repickle=False):
	"""Set `repickle=True` to rewrite pickle file if CSV has changed"""
	home = str(Path.home())
	if dir is None:
	dataset_path = os.path.join(home, "dev", "datasets")

	install.packages("devtools")
	devtools::install_github("r-lib/revdepcheck")

	# may need to install bioconductor
	if (!require("BiocManager", quietly = TRUE)) {
	install.packages("BiocManager")
	BiocManager::install(version = "3.14")
	}

	library(revdepcheck)

	# modified slightly from https://gist.github.com/floybix/6533090

	distrib.pkgs <- library(lib.loc=R.home("library"))$results[,1]

	dependencies <-
	function(pkg, dependencies = c("Depends", "Imports", "LinkingTo"),
	pl = installed.packages())
	{
	if (!(pkg %in% rownames(pl))) stop("unknown pkg ", pkg)
	fields <- pl[pkg, dependencies]