Skip to content

udpate the optimize function #11

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ZeyaWang wants to merge 1 commit into
base: master
Choose a base branch
from
Open

udpate the optimize function #11

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions tuun/main.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
"""
from argparse import Namespace
import numpy as np
import copy

from .backend import ProboBackend, DragonflyBackend

Expand Down Expand Up @@ -334,14 +335,14 @@ def suggest_to_minimize(self, data=None, verbose=True, seed=None):
If not None, set the random seed to seed.
"""
self._set_seed(seed=seed)
data = self._format_initial_data(data)
data_backend = copy.copy(self._format_initial_data(data))

# Set data-dependent subseed
subseed = int(np.random.uniform(12345 * (len(data.x) + 1)))
subseed = int(np.random.uniform(12345 * (len(data_backend.x) + 1)))

# Call backend suggest_to_minimize method
suggestion = self.backend.suggest_to_minimize(
data=data, verbose=verbose, seed=subseed
data=data_backend, verbose=verbose, seed=subseed
)

return suggestion
Expand All @@ -359,9 +360,9 @@ def suggest_to_maximize(self, data=None, verbose=True, seed=None):
seed : int
If not None, set the random seed to seed.
"""
data = self._format_initial_data(data)
data.y = [-1*v for v in data.y]
suggestion = self.suggest_to_minimize(data, verbose, seed)
data_backend = copy.copy(self._format_initial_data(data))
data_backend.y = [-1*v for v in data_backend.y]
suggestion = self.suggest_to_minimize(data_backend, verbose, seed)
return suggestion

def minimize_function(
Expand Down