Skip to content

FIX: iterative CV #1121

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

Merged
merged 1 commit into from
Apr 9, 2021
Merged

FIX: iterative CV #1121

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
18 changes: 10 additions & 8 deletions autosklearn/evaluation/train_evaluator.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -386,21 +386,23 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None:

# Compute weights of each fold based on the number of samples in each
# fold.
train_fold_weights = [w / sum(train_fold_weights)
for w in train_fold_weights]
opt_fold_weights = [w / sum(opt_fold_weights)
for w in opt_fold_weights]
train_fold_weights_percentage = [
w / sum(train_fold_weights) for w in train_fold_weights
]
opt_fold_weights_percentage = [
w / sum(opt_fold_weights) for w in opt_fold_weights
]

# train_losses is a list of either scalars or dicts. If it contains
# dicts, then train_loss is computed using the target metric
# (self.metric).
if all(isinstance(elem, dict) for elem in train_losses):
train_loss = np.average([train_losses[i][str(self.metric)]
for i in range(self.num_cv_folds)],
weights=train_fold_weights,
weights=train_fold_weights_percentage,
)
else:
train_loss = np.average(train_losses, weights=train_fold_weights)
train_loss = np.average(train_losses, weights=train_fold_weights_percentage)

# if all_scoring_function is true, return a dict of opt_loss.
# Otherwise, return a scalar.
Expand All @@ -412,10 +414,10 @@ def fit_predict_and_loss(self, iterative: bool = False) -> None:
opt_losses[i][metric]
for i in range(self.num_cv_folds)
],
weights=opt_fold_weights,
weights=opt_fold_weights_percentage,
)
else:
opt_loss = np.average(opt_losses, weights=opt_fold_weights)
opt_loss = np.average(opt_losses, weights=opt_fold_weights_percentage)

Y_targets = self.Y_targets
Y_train_targets = self.Y_train_targets
Expand Down