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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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‎CHANGELOG.rst‎

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@@ -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>`__

‎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>
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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>

‎torchensemble/adversarial_training.py‎

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@@ -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)
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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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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="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)
@@ -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
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with warnings.catch_warnings():
@@ -595,11 +602,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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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)

‎torchensemble/bagging.py‎

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@@ -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()
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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
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with warnings.catch_warnings():
@@ -271,11 +277,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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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="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
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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
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with warnings.catch_warnings():
@@ -464,11 +471,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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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)

‎torchensemble/voting.py‎

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@@ -276,6 +276,12 @@ def _forward(estimators, *x):
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self.tb_logger.add_scalar(
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"voting/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()
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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
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with warnings.catch_warnings():
@@ -294,11 +300,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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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="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)
@@ -532,6 +533,12 @@ def _forward(estimators, *x):
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self.tb_logger.add_scalar(
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"voting/Validation_Loss", val_loss, 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
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with warnings.catch_warnings():
@@ -547,11 +554,6 @@ def _forward(estimators, *x):
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else:
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scheduler_.step()
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550-
self.estimators_ = nn.ModuleList()
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self.estimators_.extend(estimators)
552-
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")
556558
def evaluate(self, test_loader):
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return super().evaluate(test_loader)

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