arm64/Apple Silicon compatibility

Installing hypertools currently fails on Apple Silicon Macs (and arm64 architectures in general). There are a few reasons for this I've figured out so far:

[x] requirement: scikit-learn>=0.19.1,!=0.22,<0.24
- scikit-learn started releasing arm64-compatible wheels with v1.0.2. We have <0.24 pinned because the layout of the sklearn.decomposition subpackage changed in v0.24, which was causing the example data (pre-built hypertools.DataGeometry objects saved with deepdish) to fail to load because they were created with an older scikit-learn version. We should really either update those objects to a different format or recreate them altogether.
- the temporary workaround for this is to build scikit-learn from the source:
  - make sure the programs needed to compile the package are installed.
    - MacOS:
```
xcode-select --install
```
    - Debian/Ubuntu:
```
apt-get update && apt-get install --no-install-recommends libc6-dev gcc g++
```
  - install the Python packages required to build scikit-learn from source. These must be installed and built BEFORE building scikit-learn, with a separate pip-install command.
```
pip install "cython>=0.28.5"    # required to build scikit-learn from source
pip install "numpy>=1.10.4,<1.22"      # must be installed and built BEFORE building scikit-learn
```
  - install and build scikit-learn from sdist. Using --no-binary :all: in place of --no-use-pep517 should theoretically also work, but was hanging for me on MacOS.
```
pip install --no-use-pep517 "scikit-learn>=0.19.1,!=0.22,<0.24"
```
  - Then you should be able to pip install hypertools. Note: if your numpy version changes from the one used to build scikit-learn, the package may fail to load
[x] requirement: `umap-learn>=0.4.6
- umap-learn requires numba, which in turn requires llvmlite. Both numba and llvmlite release binary wheels on PyPI for x86 architectures only, so pip-installing either one from an ARM machine will try use the sdist, which will fail because llvmlite doesn't include a source distribution for LLVM
- the only workaround for this is to install umap-learn (or just numba, or just llvmlite) via conda/mamba:
```
conda install -yc conda-forge "umap-learn>=0.4.6"
```

Ultimately, we really should package and release hypertools on conda-forge. That would solve both these issues, as well as the issue with MacOS that requires installing hdf5 separately via homebrew.

If I come across any more issues related to this, I'll track those here as well.

ContextLab / hypertools

arm64/Apple Silicon compatibility #253