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- added `TSRobustScaler` to tfm pipelines ([#763](https://github.com/timeseriesAI/tsai/issues/763))
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- added new tfms - `TSDropIfTrueCols` and ApplyFunc ([#760](https://github.com/timeseriesAI/tsai/issues/760))
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- tensor slices in different devices when using `TensorSplitter` ([#799](https://github.com/timeseriesAI/tsai/issues/799))
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### Bugs Squashed
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- mixed augmentations (`MixUp1d`, `CutMix1d`,..) are not updating labels ([#791](https://github.com/timeseriesAI/tsai/issues/791))
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-`get_UCR_data` function fails due to changed download link ([#785](https://github.com/timeseriesAI/tsai/issues/785))
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- error when using `TSSelectColumns` due to pandas df slicing ([#762](https://github.com/timeseriesAI/tsai/issues/762))
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- short arrays create issues when running `get_usable_idxs` ([#761](https://github.com/timeseriesAI/tsai/issues/761))
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-`get_X_pred` creates different probablities when using numpy array or torch tensor ([#754](https://github.com/timeseriesAI/tsai/issues/754))
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-`partial_n` is applied to all datasets by default ([#748](https://github.com/timeseriesAI/tsai/issues/748))
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-`get_best_dls_params` function still prints output when the verbose parameter is set to false ([#737](https://github.com/timeseriesAI/tsai/issues/737))
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- using xresnet for vision classification raises an error ([#728](https://github.com/timeseriesAI/tsai/issues/728))
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## 0.3.6
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### New Features
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### New Features
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* Models:
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- Models:
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- implement src_key_padding_mask in TST & TSTPlus ([#79](https://github.com/timeseriesAI/tsai/issues/79))
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### Bugs Squashed
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* Models:
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- Models:
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- Problem with get_minirocket_features while using CUDA in training ([#153](https://github.com/timeseriesAI/tsai/issues/153))
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## 0.2.18
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## New features
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* Data:
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- Data:
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- Update TSStandardize to accept some variables and/or groups of variables when using by_var.
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- added option to pad labeled and unlabed datasets with SlidingWindow with a padding value
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- added split_idxs and idxs to mixed_dls
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- added sklearn preprocessing tfms
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- added functions to measure sequence gaps
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- added decodes to TSStandardize
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- Callbacks:
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- change mask return values in MVP to True then mask
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- updated MVP to accept nan values
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### Bug Fixes
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* Callbacks:
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- Callbacks:
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- fixed and issue with inconsistency in show_preds in MVP
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- Models:
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- Fixed an issue in InceptionTimePlus with stochastic depth regularization (stoch_depth parameter)
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- Fixed issue with get_X_preds (different predictions when executed multiple times)
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### Bug Fixes
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* Models:
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- Models:
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- Fixed an issue in TST and TSTPlus related to encoder layer creation.
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- Fixed issue in TSStandardize when passing tensor with nan values
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## New features
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* Models:
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- Models:
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- Added TabTransformer, a state-of-the-art tabular transformer released in Dec 2020.
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- TSTPlus now supports padding masks (passed as nan values) by default.
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- Data:
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- Added a Nan2Value batch transform that removes any nan value in the tensor by zero or median.
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- Faster dataloader when suffle == True.
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### Breaking changes
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* Data:
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- Data:
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- train_perc in get_splits has been changed to train_size to allow both floats or integers.
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- df2Xy API has been modified
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### Updates
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* Learner:
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- Learner:
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- Updated 3 new learner APIs: TSClassifier, TSRegressor, TSForecaster.
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- ShowGraph callback:
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### Bug Fixes
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* Data:
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- Data:
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- Updated df2xy function to fix a bug.
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### Updates
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* Tutorial notebooks:
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- Tutorial notebooks:
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- Updated 04 (regression) to use the recently released Monash, UEA & UCR Time Series Extrinsic Regression Repository (2020).
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## New features
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* Models:
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- Models:
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- Added new pooling layers and 3 new heads: attentional_pool_head, universal_pool_head, gwa_pool_head
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## 0.2.15
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### New Features
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* General:
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- General:
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- Added 3 new sklearn-type APIs: TSClassifier, TSRegressor and TSForecaster.
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- Data:
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### Bug Fixes
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* Models:
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- Models:
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- Fixed bug that prevented models to freeze or unfreeze. Now all models that end with Plus can take predefined weights and learn.freeze()/ learn.unfreeze() will work as expected.
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## 0.2.14
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### New Features
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* Data:
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- Data:
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- External: added a new function get_Monash_data to get extrinsic regression data.
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- Models:
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- Added show_batch functionality to TSBERT.
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## 0.2.13
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### New Features
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* General: Added min requirements for all package dependencies.
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- General: Added min requirements for all package dependencies.
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- Data:
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- Validation: added split visualization (show_plot=True by default).
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- Data preprocessing: add option to TSStandardize or TSNormalize by_step.
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### Bug Fixes
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* Data:
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- Data:
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- ROCKET: fixed a bug in `create_rocket_features`.
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## 0.2.12
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### New Features
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* More ts data transforms have been added, including ts to images.
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- More ts data transforms have been added, including ts to images.
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- New callbacks, like the state of the art noisy_student that will allow you to use unlabeled data.
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- New time series, state-of-the-art models are now available like XceptionTime, RNN_FCN (like LSTM_FCN, GRU_FCN), TransformerModel, TST (Transformer), OmniScaleCNN, mWDN (multi-wavelet decomposition network), XResNet1d.
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- Some of the models (those finishing with an plus) have additional, experimental functionality (like coordconv, zero_norm, squeeze and excitation, etc).
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