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example code (run with sklearn 0.20 installed):
openml.flows.get_flow(6969)
When deciding whether to deserialize the model, it checks the dependencies according to a different schedule than when it actually deserialized the model. In the prior case, it just checks whether the correct library is available. In the latter case, it also checks the version number.
personally, I we should fix this before releasing a next version, as it will affect many users.
traceback:
/home/janvanrijn/anaconda3/envs/openml-python-contrib/bin/python /home/janvanrijn/projects/openml-python-contrib/testjan.py
Traceback (most recent call last):
File "/home/janvanrijn/projects/openml-python-contrib/testjan.py", line 3, in <module>
openml.flows.get_flow(6969)
File "/home/janvanrijn/projects/openml-python/openml/flows/functions.py", line 23, in get_flow
flow = OpenMLFlow._from_dict(flow_dict)
File "/home/janvanrijn/projects/openml-python/openml/flows/flow.py", line 321, in _from_dict
model = flow_to_sklearn(flow)
File "/home/janvanrijn/projects/openml-python/openml/flows/sklearn_converter.py", line 173, in flow_to_sklearn
rval = _deserialize_model(o, initialize_with_defaults)
File "/home/janvanrijn/projects/openml-python/openml/flows/sklearn_converter.py", line 442, in _deserialize_model
_check_dependencies(flow.dependencies)
File "/home/janvanrijn/projects/openml-python/openml/flows/sklearn_converter.py", line 516, in _check_dependencies
'%s not satisfied.' % dependency_string)
ValueError: Trying to deserialize a model with dependency sklearn==0.20.0 not satisfied.