jeffkinnison / pyrameter Goto Github PK
View Code? Open in Web Editor NEWStructure, sample, and savor hyperparameter searches.
Structure, sample, and savor hyperparameter searches.
in pyrameter/domains/continuous.py
for the from_json
function, random_state
is not defined.
Incorporate visualization methods for plotting trials over time, hyperparameters vs objective, etc.
At present, database support for storing search data is incomplete, working only with JSON. The full suite should support Mongo and SQLite (at the very least).
This project needs support for evolutionary algs and other population-based methods. Essentially, instead of generating single hyperparameter sets at a time, there should be a way to generate a batch of them.
This could be implemented using a generator in SearchSpace.generate() with n samples and updating when the generator is empty.
When attempting to load and plot with the results file, get the following KeyError
File "driver_mnist.py", line 67, in <module>
opt.load()
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/shadho/shadho.py", line 219, in load
super().load()
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/optimizer.py", line 174, in load
self.searchspaces = self.backend.load()
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/backend/local.py", line 46, in load
return [SearchSpace.from_json(obj) for obj in objs]
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/backend/local.py", line 46, in <listcomp>
return [SearchSpace.from_json(obj) for obj in objs]
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/searchspace.py", line 132, in from_json
domains = [Domain.from_json(d) for d in obj['domains']]
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/searchspace.py", line 132, in <listcomp>
domains = [Domain.from_json(d) for d in obj['domains']]
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/domains/base.py", line 112, in from_json
return cls.from_json(obj)
File "/afs/crc.nd.edu/user/s/sabraha2/Projects/optimization/Neural_network_search/venv/lib/python3.7/site-packages/pyrameter/domains/discrete.py", line 73, in from_json
rng = obj['domain_kwargs']['random_state']
KeyError: 'domain_kwargs'
Implement NAS as a search method, incorporating reinforcement learning a la DeepMind. This will totally go well.
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