Unable to use mean function with inputs raised to a power

[joshnevin]

I am new to the mean function interface and apologise if this is a trivial issue...
I want to do GP regression with 5-dimensional input space and 1-dimensional output.
I am trying to specify a mean function using Patsy of the form

"x[0]^3+x[1]^3+x[2]^3+x[3]^3+x[4]^3"

Checking this using

mf = "x[0]^3+x[1]^3+x[2]^3+x[3]^3+x[4]^3"
print(MeanFunction(mf))

gives

c + cx[0]^3.0 + cx[1]^3.0 + cx[2]^3.0 + cx[3]^3.0 + c*x[4]^3.0

which is what I want, i.e., each input raised to the third power, plus an intercept. However, when I try to create the GP model using

gp_map = mogp_emulator.GaussianProcess(inputs, targets, mean="x[0]^3+x[1]^3+x[2]^3+x[3]^3+x[4]^3", kernel='SquaredExponential', nugget="fit")

I get the error

File ~/nli_char/nlienv/lib/python3.8/site-packages/mogp_emulator/GaussianProcess.py:507, in GaussianProcess.get_design_matrix(self, inputs)
505 dm = np.array(dmatrix(self._mean, data={"x": inputs.T}))
506 except PatsyError:
--> 507 raise ValueError("Provided mean function is invalid")
508 if not dm.shape[0] == inputs.shape[0]:
509 raise ValueError("Provided design matrix is of the wrong shape")
ValueError: Provided mean function is invalid

I am not sure what is wrong with this mean function. I get no errors and can fit a model with a linear mean function
"x[0]+x[1]+x[2]+x[3]+x[4]"
I also get the same error with the mean function
c + c*x[0]^2.0
which is mentioned in the examples: https://mogp-emulator.readthedocs.io/en/latest/implementation/MeanFunction.html.
So, the issue seems to be using a mean function with inputs raised to any power.

Running pip list:
Package Version

---

anyio 3.6.1
appnope 0.1.3
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
asttokens 2.0.5
attrs 21.4.0
Babel 2.10.3
backcall 0.2.0
beautifulsoup4 4.11.1
bleach 5.0.1
certifi 2022.6.15
cffi 1.15.1
charset-normalizer 2.1.0
cycler 0.11.0
debugpy 1.6.0
decorator 5.1.1
defusedxml 0.7.1
entrypoints 0.4
executing 0.8.3
fastjsonschema 2.15.3
fonttools 4.33.3
idna 3.3
importlib-metadata 4.12.0
importlib-resources 5.8.0
ipykernel 6.15.0
ipython 8.4.0
ipython-genutils 0.2.0
ipywidgets 7.7.1
jedi 0.18.1
Jinja2 3.1.2
joblib 1.1.0
json5 0.9.8
jsonschema 4.6.1
jupyter 1.0.0
jupyter-client 7.3.4
jupyter-console 6.4.4
jupyter-core 4.10.0
jupyter-server 1.18.0
jupyterlab 3.4.3
jupyterlab-pygments 0.2.2
jupyterlab-server 2.14.0
jupyterlab-widgets 1.1.1
kiwisolver 1.4.3
MarkupSafe 2.1.1
matplotlib 3.5.2
matplotlib-inline 0.1.3
mistune 0.8.4
mogp-emulator 0.7.0
nbclassic 0.4.0
nbclient 0.6.6
nbconvert 6.5.0
nbformat 5.4.0
nest-asyncio 1.5.5
notebook 6.4.12
notebook-shim 0.1.0
numpy 1.23.0
packaging 21.3
pandas 1.4.3
pandocfilters 1.5.0
parso 0.8.3
patsy 0.5.2
pexpect 4.8.0
pickle-mixin 1.0.2
pickleshare 0.7.5
Pillow 9.2.0
pip 22.1.2
prometheus-client 0.14.1
prompt-toolkit 3.0.30
psutil 5.9.1
ptyprocess 0.7.0
pure-eval 0.2.2
pycparser 2.21
Pygments 2.12.0
pyparsing 3.0.9
pyrsistent 0.18.1
python-dateutil 2.8.2
pytz 2022.1
pyzmq 23.2.0
qtconsole 5.3.1
QtPy 2.1.0
requests 2.28.1
scikit-learn 1.1.1
scipy 1.8.1
Send2Trash 1.8.0
setuptools 41.2.0
six 1.16.0
sniffio 1.2.0
soupsieve 2.3.2.post1
stack-data 0.3.0
terminado 0.15.0
threadpoolctl 3.1.0
tinycss2 1.1.1
tornado 6.2
traitlets 5.3.0
urllib3 1.26.9
wcwidth 0.2.5
webencodings 0.5.1
websocket-client 1.3.3
widgetsnbextension 3.6.1
zipp 3.8.0