Skip to content
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

Jump to bottom

Add the attribute best_epoch to record the best epoch num #342

Merged
merged 2 commits into from
Apr 8, 2024
Merged

Add the attribute best_epoch to record the best epoch num #342

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
efbf731
feat: add the attribute best_epoch to record the epoch num when the b…
WenjieDu Apr 8, 2024
93062a2
feat: remind the best epoch num after training is finished;
WenjieDu Apr 8, 2024
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
4 changes: 3 additions & 1 deletion pypots/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -450,6 +450,8 @@ class BaseNNModel(BaseModel):
The criteria to judge whether the model's performance is the best so far.
Usually the lower, the better.

best_epoch : int, default = -1,
The epoch number when the best loss is got.

Notes
-----
Expand Down Expand Up @@ -494,8 +496,8 @@ def __init__(
self.model = None
self.optimizer = None
self.best_model_dict = None
# WDU: may enable users to customize the criteria in the future
self.best_loss = float("inf")
self.best_epoch = -1

def _print_model_size(self) -> None:
"""Print the number of trainable parameters in the initialized NN model."""
Expand Down
5 changes: 4 additions & 1 deletion pypots/classification/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -337,6 +337,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -376,7 +377,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

@abstractmethod
def fit(
Expand Down
5 changes: 4 additions & 1 deletion pypots/clustering/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -336,6 +336,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -369,7 +370,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

@abstractmethod
def fit(
Expand Down
5 changes: 4 additions & 1 deletion pypots/clustering/crli/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -296,6 +296,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -335,7 +336,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

def fit(
self,
Expand Down
5 changes: 4 additions & 1 deletion pypots/clustering/vader/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -309,6 +309,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -348,7 +349,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

def fit(
self,
Expand Down
5 changes: 4 additions & 1 deletion pypots/forecasting/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -331,6 +331,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -370,7 +371,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

@abstractmethod
def fit(
Expand Down
5 changes: 4 additions & 1 deletion pypots/imputation/base.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -334,6 +334,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -373,7 +374,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

@abstractmethod
def fit(
Expand Down
5 changes: 4 additions & 1 deletion pypots/imputation/csdi/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -283,6 +283,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -322,7 +323,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

def fit(
self,
Expand Down
5 changes: 4 additions & 1 deletion pypots/imputation/gpvae/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -313,6 +313,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -352,7 +353,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

def fit(
self,
Expand Down
5 changes: 4 additions & 1 deletion pypots/imputation/usgan/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -330,6 +330,7 @@ def _train_model(
)

if mean_loss < self.best_loss:
self.best_epoch = epoch
self.best_loss = mean_loss
self.best_model_dict = self.model.state_dict()
self.patience = self.original_patience
Expand Down Expand Up @@ -369,7 +370,9 @@ def _train_model(
if np.isnan(self.best_loss):
raise ValueError("Something is wrong. best_loss is Nan after training.")

logger.info("Finished training.")
logger.info(
f"Finished training. The best model is from epoch#{self.best_epoch}."
)

def fit(
self,
Expand Down
Loading