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fix: estimator overwrite problem with validation data loader (#143)
* update code * Update index.rst
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-32
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Diff for: ‎CHANGELOG.rst

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Original file line numberDiff line numberDiff line change
@@ -18,6 +18,7 @@ Changelog
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Ver 0.1.*
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---------
2020

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* |Fix| Fix the issue that model will be overwritten when using the validation data loder | `@xuyxu <https://github.com/xuyxu>`__
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* |Feature| Add internal :meth:`unsqueeze` operation in :meth:`forward` of all classifiers | `@xuyxu <https://github.com/xuyxu>`__
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* |Feature| |API| Add ``voting_strategy`` parameter for :class:`VotingClassifer`, :class:`NeuralForestClassifier`, and :class:`SnapshotEnsembleClassifier` | `@LukasGardberg <https://github.com/LukasGardberg>`__
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* |Fix| Fix the sampling issue in :class:`BaggingClassifier` and :class:`BaggingRegressor` | `@SunHaozhe <https://github.com/SunHaozhe>`__

Diff for: ‎docs/index.rst

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@@ -64,8 +64,9 @@ Content
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-------
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.. toctree::
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:maxdepth: 1
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:caption: For Users
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:maxdepth: 2
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:hidden:
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Quick Start <quick_start>
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Introduction <introduction>
@@ -74,8 +75,9 @@ Content
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API Reference <parameters>
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.. toctree::
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:maxdepth: 1
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:caption: For Developers
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:maxdepth: 2
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:hidden:
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Changelog <changelog>
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Roadmap <roadmap>

Diff for: ‎torchensemble/adversarial_training.py

+12-10
Original file line numberDiff line numberDiff line change
@@ -384,6 +384,12 @@ def _forward(estimators, *x):
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acc,
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epoch,
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)
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# No validation
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else:
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self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
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if save_model:
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io.save(self, save_dir, self.logger)
387393

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# Update the scheduler
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with warnings.catch_warnings():
@@ -402,11 +408,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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405-
self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
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if save_model and not test_loader:
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io.save(self, save_dir, self.logger)
409-
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@torchensemble_model_doc(item="classifier_evaluate")
411412
def evaluate(self, test_loader, return_loss=False):
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return super().evaluate(test_loader, return_loss)
@@ -580,6 +581,12 @@ def _forward(estimators, *x):
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val_loss,
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epoch,
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)
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# No validation
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else:
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self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
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if save_model:
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io.save(self, save_dir, self.logger)
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# Update the scheduler
585592
with warnings.catch_warnings():
@@ -595,11 +602,6 @@ def _forward(estimators, *x):
595602
else:
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scheduler_.step()
597604

598-
self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
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if save_model and not test_loader:
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io.save(self, save_dir, self.logger)
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@torchensemble_model_doc(item="regressor_evaluate")
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def evaluate(self, test_loader):
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return super().evaluate(test_loader)

Diff for: ‎torchensemble/bagging.py

+12-10
Original file line numberDiff line numberDiff line change
@@ -253,6 +253,12 @@ def _forward(estimators, *x):
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self.tb_logger.add_scalar(
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"bagging/Validation_Acc", acc, epoch
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)
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# No validation
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else:
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self.estimators_ = nn.ModuleList()
259+
self.estimators_.extend(estimators)
260+
if save_model:
261+
io.save(self, save_dir, self.logger)
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# Update the scheduler
258264
with warnings.catch_warnings():
@@ -271,11 +277,6 @@ def _forward(estimators, *x):
271277
else:
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scheduler_.step()
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274-
self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
276-
if save_model and not test_loader:
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io.save(self, save_dir, self.logger)
278-
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@torchensemble_model_doc(item="classifier_evaluate")
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def evaluate(self, test_loader, return_loss=False):
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return super().evaluate(test_loader, return_loss)
@@ -449,6 +450,12 @@ def _forward(estimators, *x):
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self.tb_logger.add_scalar(
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"bagging/Validation_Loss", val_loss, epoch
451452
)
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# No validation
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else:
455+
self.estimators_ = nn.ModuleList()
456+
self.estimators_.extend(estimators)
457+
if save_model:
458+
io.save(self, save_dir, self.logger)
452459

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# Update the scheduler
454461
with warnings.catch_warnings():
@@ -464,11 +471,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
466473

467-
self.estimators_ = nn.ModuleList()
468-
self.estimators_.extend(estimators)
469-
if save_model and not test_loader:
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io.save(self, save_dir, self.logger)
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@torchensemble_model_doc(item="regressor_evaluate")
473475
def evaluate(self, test_loader):
474476
return super().evaluate(test_loader)

Diff for: ‎torchensemble/voting.py

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Original file line numberDiff line numberDiff line change
@@ -276,6 +276,12 @@ def _forward(estimators, *x):
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self.tb_logger.add_scalar(
277277
"voting/Validation_Acc", acc, epoch
278278
)
279+
# No validation
280+
else:
281+
self.estimators_ = nn.ModuleList()
282+
self.estimators_.extend(estimators)
283+
if save_model:
284+
io.save(self, save_dir, self.logger)
279285

280286
# Update the scheduler
281287
with warnings.catch_warnings():
@@ -294,11 +300,6 @@ def _forward(estimators, *x):
294300
else:
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scheduler_.step()
296302

297-
self.estimators_ = nn.ModuleList()
298-
self.estimators_.extend(estimators)
299-
if save_model and not test_loader:
300-
io.save(self, save_dir, self.logger)
301-
302303
@torchensemble_model_doc(item="classifier_evaluate")
303304
def evaluate(self, test_loader, return_loss=False):
304305
return super().evaluate(test_loader, return_loss)
@@ -532,6 +533,12 @@ def _forward(estimators, *x):
532533
self.tb_logger.add_scalar(
533534
"voting/Validation_Loss", val_loss, epoch
534535
)
536+
# No validation
537+
else:
538+
self.estimators_ = nn.ModuleList()
539+
self.estimators_.extend(estimators)
540+
if save_model:
541+
io.save(self, save_dir, self.logger)
535542

536543
# Update the scheduler
537544
with warnings.catch_warnings():
@@ -547,11 +554,6 @@ def _forward(estimators, *x):
547554
else:
548555
scheduler_.step()
549556

550-
self.estimators_ = nn.ModuleList()
551-
self.estimators_.extend(estimators)
552-
if save_model and not test_loader:
553-
io.save(self, save_dir, self.logger)
554-
555557
@torchensemble_model_doc(item="regressor_evaluate")
556558
def evaluate(self, test_loader):
557559
return super().evaluate(test_loader)

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