Comments (4)
There's a note about it in the readme installation instructions. In short: either update numpy, or install without pep517.
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Hi @david-cortes ,
Thank you for your speedy reply! I uninstalled the contextualbandits package and reinstalled it with the command given in the tutorial. But I still got the same error.
python -m pip uninstall contextualbandits
python -m pip install --no-use-pep517 contextualbandits
Any suggestions?
Note: to make sure that I successfully reinstalled the package. I uninstalled the package, and then I restarted the jupyter server, confirming the package has been successfully removed. After that, I installed the package again according to the command in the readme file.
from contextualbandits.
That's weird. Perhaps updating numpy to >= 1.20.0 would do (might also need updating cython too). Alternatively, you could also install it without pip with python setup.py install
, or if you know how to create installable packages, perhaps modifying the requirements.txt
by changing numpy
to numpy<=1.19.0
.
from contextualbandits.
Hi @david-cortes ,
I upgraded my numpy version to 1.20.1
and it works. Thank you very much for your help!
from contextualbandits.
Related Issues (20)
- Question regarding using contextual bandits for Learning-To-Rank HOT 1
- Different predictions of the same model.dill file in different CPUs for the LinUCB algorithm HOT 1
- Support for continuous rewards HOT 1
- ParametricTS fails with: '_OneVsRest' object has no attribute 'beta_counters' HOT 2
- XGBClassifier becomes un-serializable after being used as a base_model HOT 2
- Need more understanding for the method beta_prior HOT 1
- Any good strategies to run Simulation other then one in Online policy example HOT 1
- How to deal with this Scenario while applying CB techniques HOT 2
- Getting topN arm features in Offpolicy method HOT 4
- Type error if beta_prior == "auto" and nchoices is list HOT 1
- Possibly unexpected behaviour of decision function HOT 1
- _BasePolicy.add_arm(); NameError: name 'base_algorithm' is not defined HOT 1
- AssertionError: online_contextual_bandits.ipynb HOT 1
- TypeError: contextual bandits with custom 'choice_names' (online.py) HOT 3
- Getting topN arm features in onlinepolicy method HOT 9
- topN inputs clarification HOT 3
- warm_start for online policy HOT 1
- Doubt in DREstimator HOT 3
- New release HOT 1
- If r != 0 then could be working for negative reward as well? HOT 1
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