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Release notes

0.3.9

  • support to MPS backend. Both the MPS accelerator and the PyTorch backend are still experimental. As such, not all operations are currently supported.
  • compatibility with torch 2.2
  • ability to pass arch_config to multimodal models

0.3.8

New Features

  • added Hydra and HydraMultiRocket archs (#800)

Bugs Squashed

  • UCR Dataset download link has been updated (#827)

  • mWDNPlus now supports multidimensional outputs (#802)

  • Fixed import issues with demo code in Readme.MD (#798)

0.3.7

New Features

  • added functionality to support inputs with static/ observed (time-dependent) features

  • added functionality to support inputs with categorical/ continuous features

  • added functionality to apply patches to time series models

  • Added MultiRocket/ MultiRocketPlus architectures

  • added TSSelfDropout (#790)

  • added get_feat_idxs to calculate multimodal indices (#789)

  • remaining features assigned to o_cont_idxs by default (#788)

  • added patch encoder to MultiInputWrapper (#787)

  • added TSTargetEncoder transform (#769)

  • added TSRobustScaler to tfm pipelines (#763)

  • added new tfms - TSDropIfTrueCols and ApplyFunc (#760)

  • tensor slices in different devices when using TensorSplitter (#799)

Bugs Squashed

  • mixed augmentations (MixUp1d, CutMix1d,..) are not updating labels (#791)

  • get_UCR_data function fails due to changed download link (#785)

  • error when using TSSelectColumns due to pandas df slicing (#762)

  • short arrays create issues when running get_usable_idxs (#761)

  • get_X_pred creates different probablities when using numpy array or torch tensor (#754)

  • partial_n is applied to all datasets by default (#748)

  • get_best_dls_params function still prints output when the verbose parameter is set to false (#737)

  • using xresnet for vision classification raises an error (#728)

0.3.6

New Features

  • added optional activation to get_X_preds (#715)

  • added external vocab option to dls (#705)

  • allow classification outputs with n dimensions (#704)

  • added get_sweep_config to wandb module (#687)

  • added functionality to run pipeline sweeps (#686)

  • added seed to learners to make training reproducible (#685)

  • added functionality to filter df for required forecasting dates (#679)

  • added option to train model on train only (#671)

Bugs Squashed

  • access all available dataloaders in dls (#724)

  • make all models ending in Plus work with ndim classification targets (#719)

  • make all models ending in Plus work with ndim work with ndim regression/ forecasting targets (#718)

  • added MiniRocket to get_arch (#717)

  • fixed issue with get_arch missing new models (#709)

  • valid_metrics causes an error when using TSLearners (#708)

  • valid_metrics are not shown when an array is passed within splits (#707)

  • TSDatasets w/o tfms and inplace=False creates new X (#695)

  • Prediction and True Values Swapped in plot_forecast (utils.py) (#690)

  • MiniRocket incompatible with latest scikit-learn version (#677)

  • Df2xy causing incorrect splits (#666)

  • Feature Importance & Step Importance Not working (#647)

  • multi-horizon forecasting (#591)

  • Issues saving models with TSMetaDataset Dataloader (#317)

0.3.5

Breaking Changes

  • removed default transforms from TSClassifier, TSRegressor and TSForecaster (#665)

New Features

  • add option to pass an instantiated model to TSLearners (#650)

  • Added PatchTST model to tsai (#638)

  • Added new long-term time series forecasting tutorial notebook

Bugs Squashed

  • Undefined variable (#662)

  • Multivariate Regression and Forecasting basic tutorials throw an error (#629)

  • TypeError: init() got an unexpected keyword argument 'custom_head' (#597)

  • Issues with TSMultiLabelClassification (#533)

  • Incompatible errors or missing functions in 'tutorial_nbs' notebooks, please fix. (#447)

  • Saving models with TSUnwindowedDataset Dataloaders: AttributeError: 'TSUnwindowedDataset' object has no attribute 'new_empty' (#215)

0.3.4

New Features

  • compatibility with Pytorch 1.13 (#619)

  • added sel_vars to get_robustscale_params (#610)

  • added sel_steps to TSRandom2Value (#607)

  • new walk forward cross-validation in tsai (#582)

Bugs Squashed

  • fixed issue when printing an empty dataset wo transforms NoTfmLists (#622)

  • fixed minor issue in get_robustscaler params with sel_vars (#615)

  • fixed issue when using tsai in dev with VSCode (#614)

  • issue when using lists as sel_vars and sel_steps in TSRandom2Value (#612)

  • fixed issue with feature_importance and step_importance when using metrics (#609)

  • renamed data processing tfms feature_idxs as sel_vars for consistency (#608)

  • fixed issue when importing 'GatedTabTransformer' (#536)

0.3.2

Breaking Changes

  • replaced TSOneHot preprocessor by TSOneHotEncode using a different API (#502)

  • replaced MultiEmbedding n_embeds, embed_dims and padding_idxs by n_cat_embeds, cat_embed_dims and cat_padding_idxs (#497)

New Features

  • added GaussianNoise transform (#514)

  • added TSSequencer model based on Sequencer: Deep LSTM for Image Classification paper (#508)

  • added TSPosition to be able to pass any steps list that will be concatenated to the input (#504)

  • added TSPosition preprocessor to allow the concatenation of a custom position sequence (#503)

  • added TSOneHot class to encode a variable on the fly (#501)

  • added token_size and tokenizer arguments to tsai (#496)

  • SmeLU activation function not found (#495)

  • added example on how to perform inference, partial fit and fine tuning (#491)

  • added get_time_per_batch and get_dl_percent_per_epoch (#489)

  • added TSDropVars used to removed batch variables no longer needed (#488)

  • added SmeLU activation function (#458)

  • Feature request: gMLP and GatedTabTransformer. (#354)

  • Pay Attention to MLPs - gMLP (paper, implementation)

  • The GatedTabTransformer (paper, implementation);

Bugs Squashed

  • after_batch tfms set to empty Pipeline when using dl.new() (#516)

  • 00b_How_to_use_numpy_arrays_in_fastai: AttributeError: attribute 'device' of 'torch._C._TensorBase' objects is not writable (#500)

  • getting regression data returns _check_X() argument error (#430)

  • I wonder why only 'Nor' is displayed in dls.show_batch(sharvey=True). (#416)

0.3.1

New Features

  • added StratifiedSampler to handle imbalanced datasets (#479)

  • added seq_embed_size and seq_embed arguments to TSiT (#476)

  • added get_idxs_to_keep that can be used to filter indices based on different conditions (#469)

  • added SmeLU activation function (#458)

  • added split_in_chunks (#454)

  • upgraded min Python version to 3.7 (#450)

  • added sampler argument to NumpyDataLoader and TSDataLoader (#436)

  • added TSMask2Value transform which supports multiple masks (#431)

  • added TSRandomStandardize for improved ood generalization (#428)

  • added get_dir_size function (#421)

  • Is there any ways of recording the wrong predictions into a txt files? (#397)

    • I am wondering if the net could record the wrong predictions of the dataset? So maybe I can find a pattern from the wrong files and adjust my method of preprocessing data.

Bugs Squashed

  • slow import of MiniRocketMultivariate from sktime (#482)

  • Installing from source fails on Windows (UnicodeDecodeError) (#470)

    • Hi everyone,

trying to install the package from source does not work for on Windows 10; it fails with UnicodeDecodeError: 'charmap' codec can't decode byte 0x8f in position [...]. The problem also affects other packages (e.g. iitzco/faced#21), but is easily solved by specifying an encoding in the setup.py, see PR.

  • TSDataset error oindex is not an attribute (#462)

  • split_in_chunks incorrectly calculated (#455)

  • _check_X() got an unexpected keyword argument 'coerce_to_numpy' (#415)

0.3.0

New Features

  • Added function that pads sequences to same length (#410)

  • Added TSRandomStandardize preprocessing technique (#396)

  • New visualization techniques: model's feature importance and step importance (#393)

  • Allow from tsai.basics import * to speed up loading (#320)

Bugs Squashed

  • Separate core from non-core dependencies in tsai - pip install tsaiextras. This is an important change that:
    • reduces the time to pip install tsai
    • avoid errors during installation
    • reduces the time to load tsai using from tsai.all import *

0.2.25

Breaking Changes

  • updated forward_gaps removing nan_to_num (#331)

  • TSRobustScaler only applied by_var (#329)

  • remove add_na arg from TSCategorize (#327)

New Features

  • added IntraClassCutMix1d (#384)

  • added learn.calibrate_model method (#379)

  • added analyze_array function (#378)

  • Added TSAddNan transform (#376)

  • added dummify function to create dummy data from original data (#366)

  • added Locality Self Attention to TSiT (#363)

  • added sel_vars argument to MVP callback (#349)

  • added sel_vars argument to TSNan2Value (#348)

  • added multiclass, weighted FocalLoss (#346)

  • added TSRollingMean batch transform (#343)

  • added recall_at_specificity metric (#342)

  • added train_metrics argument to ts_learner (#341)

  • added hist to PredictionDynamics for binary classification (#339)

  • add padding_idxs to MultiEmbedding (#330)

Bugs Squashed

  • sort_by data may be duplicated in SlidingWindowPanel (#389)

  • create_script splits the nb name if multiple underscores are used (#385)

  • added torch functional dependency to plot_calibration_curve (#383)

  • issue when setting horizon to 0 in SlidingWindow (#382)

  • replace learn by self in calibrate_model patch (#381)

  • Argument d_head is not used in TSiTPlus (#380)

    • tsai/tsai/models/TSiTPlus.py

      Line 63 in 6baf0ba

      def __init__(self, c_in:int, seq_len:int, depth:int=6, d_model:int=128, n_heads:int=16, d_head:Optional[int]=None, act:str='gelu',

  • replace default relu activation by gelu in TSiT (#361)

  • sel_vars and sel_steps in TSDatasets and TSDalaloaders don't work when used simultaneously (#347)

  • ShowGraph fails when recoder.train_metrics=True (#340)

  • fixed 'se' always equal to 16 in MLSTM_FCN (#337)

  • ShowGraph doesn't work well when train_metrics=True (#336)

  • TSPositionGaps doesn't work on cuda (#333)

  • XResNet object has no attribute 'backbone' (#332)

  • import InceptionTimePlus in tsai.learner (#328)

  • df2Xy: Format correctly without the need to specify sort_by (#324)

  • bug in MVP code learn.model --> self.learn.model (#323)

  • Colab install issues: importing the lib takes forever (#315)

  • Calling learner.feature_importance on larger than memory dataset causes OOM (#310)

0.2.24

Breaking Changes

  • removed InceptionTSiT, InceptionTSiTPlus, ConvTSiT & ConvTSiTPlus (#276)

New Features

  • add stateful custom sklearn API type tfms: TSShrinkDataFrame, TSOneHotEncoder, TSCategoricalEncoder (#313)

  • Pytorch 1.10 compatibility (#311)

  • ability to pad at the start/ end of sequences and filter results in SlidingWindow (#307)

  • added bias_init to TSiT (#288)

  • plot permutation feature importance after a model's been trained (#286)

  • added separable as an option to MultiConv1d (#285)

  • Modified TSiTPlus to accept a feature extractor and/or categorical variables (#278)

Bugs Squashed

  • learn modules takes too long to load (#312)

  • error in roll2d and roll3d when passing index 2 (#304)

  • TypeError: unhashable type: 'numpy.ndarray' (#302)

  • ValueError: only one element tensors can be converted to Python scalars (#300)

  • unhashable type: 'numpy.ndarray' when using multiclass multistep labels (#298)

  • incorrect data types in NumpyDatasets subset (#297)

  • create_future_mask creates a mask in the past (#293)

  • NameError: name 'X' is not defined in learner.feature_importance (#291)

  • TSiT test fails on cuda (#287)

  • MultiConv1d breaks when ni == nf (#284)

  • WeightedPerSampleLoss reported an error when used with LDS_weights (#281)

  • pos_encoding transfer weight in TSiT fails (#280)

  • MultiEmbedding cat_pos and cont_pos are not in state_dict() (#277)

  • fixed issue with MixedDataLoader (#229), thanks to @Wabinab

0.2.23

Breaking Changes

  • removed torch-optimizer dependency (#228)

New Features

  • added option to train MVP on random sequence lengths (#252)

  • added ability to pass an arch name (str) to learner instead of class (#217)

  • created convenience fns create_directory and delete_directory in utils (#213)

  • added option to create random array of given shapes and dtypes (#212)

  • my_setup() print your main system and package versions (#202)

  • added a new tutorial on how to train large datasets using tsai (#199)

  • added a new function to load any file as a module (#196)

Bugs Squashed

  • Loading code just for inference takes too long (#273)

  • Fixed out-of-memory issue with large datasets on disk (#126)

  • AttributeError: module 'torch' has no attribute 'nan_to_num' (#262)

  • Fixed TypeError: unhashable type: 'numpy.ndarray' (#250)

  • Wrong link in paper references (#249)

  • remove default PATH which overwrites custom PATH (#238)

  • Predictions where not properly decoded when using with_decoded. (#237)

  • SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame (#221)

  • InceptionTimePlus wasn't imported by TSLearners (#218)

  • get_subset_dl fn is not properly creating a subset dataloader (#211)

  • Bug in WeightedPersSampleLoss (#203)

0.2.19

New Features

  • implemented src_key_padding_mask in TST & TSTPlus (#79)

Bugs Squashed

  • Problem with get_minirocket_features while using CUDA in training (#153)

0.2.19

New Features

  • Models:
    • implement src_key_padding_mask in TST & TSTPlus (#79)

Bugs Squashed

  • Models:
    • Problem with get_minirocket_features while using CUDA in training (#153)

0.2.18

New features

  • Data:

    • Update TSStandardize to accept some variables and/or groups of variables when using by_var.
    • added option to pad labeled and unlabed datasets with SlidingWindow with a padding value
    • added split_idxs and idxs to mixed_dls
    • added sklearn preprocessing tfms
    • added functions to measure sequence gaps
    • added decodes to TSStandardize

  • Callbacks:

    • change mask return values in MVP to True then mask
    • updated MVP to accept nan values

  • Models:

    • updated mWDN to take either model or arch
    • added padding_var to TST
    • added MiniRocketFeatures in Pytorch

  • Losses & metrics:

    • added WeightedPerSampleLoss
    • added mean_per_class_accuracy to metrics
    • added mape metric
    • added HuberLoss and LogCoshLoss

  • Learner:

    • added Learner.remove_all_cbs
    • updated get_X_preds to work with multilabel datasets

  • Miscellaneous:

    • added rotate_axis utility functions

Bug Fixes

  • Callbacks:

    • fixed and issue with inconsistency in show_preds in MVP

  • Models:

    • Fixed an issue in InceptionTimePlus with stochastic depth regularization (stoch_depth parameter)
    • Fixed issue with get_X_preds (different predictions when executed multiple times)
    • fixed stoch_depth issue in InceptionTimePlus
    • fixed kwargs issue in MultiInceptionTimePlus

  • Data:

    • fixed issue in delta gap normalize

  • Learner:

    • fixed bug in get_X_preds device
    • updated get_X_preds to decode classification and regression outputs

0.2.17

Bug Fixes

  • Models:
    • Fixed an issue in TST and TSTPlus related to encoder layer creation.
    • Fixed issue in TSStandardize when passing tensor with nan values

New features

  • Models:

    • Added TabTransformer, a state-of-the-art tabular transformer released in Dec 2020.
    • TSTPlus now supports padding masks (passed as nan values) by default.

  • Data:

    • Added a Nan2Value batch transform that removes any nan value in the tensor by zero or median.
    • Faster dataloader when suffle == True.
    • Added TSUndindowedDataset and TSUnwindowedDatasets, which apply window slicing online to prepare time series data.
    • Added TSMetaDataset and TSMetaDatasets, which allow you to use one or multiple X (and y) arrays as input. In this way, you won't need to merge all data into a single array. This will allow you to work with larger than memory datasets.
    • Added a new tutorial notebook that demonstrates both multi-class and multi-label classification using tsai.
    • Upgraded df2Xy to accept y_func that allows calculation of different types of targets

  • Callbacks:

    • MVP is now much faster as masks are now created directly as cuda tensors. This has increased speed by 2.5x in some tests.

Breaking changes

  • Data:
    • train_perc in get_splits has been changed to train_size to allow both floats or integers.
    • df2Xy API has been modified

Updates

  • Learner:

    • Updated 3 new learner APIs: TSClassifier, TSRegressor, TSForecaster.

  • ShowGraph callback:

    • Callback optionally plots all metrics at the end of training.

0.2.16

Bug Fixes

  • Data:
    • Updated df2xy function to fix a bug.

Updates

  • Tutorial notebooks:
    • Updated 04 (regression) to use the recently released Monash, UEA & UCR Time Series Extrinsic Regression Repository (2020).

New features

  • Models:
    • Added new pooling layers and 3 new heads: attentional_pool_head, universal_pool_head, gwa_pool_head

0.2.15

New Features

  • General:

    • Added 3 new sklearn-type APIs: TSClassifier, TSRegressor and TSForecaster.

  • Data:

    • External: added a new function get_forecasting_data to access some forecasting datasets.
    • Modified TimeSplitter to also allow passing testing_size.
    • Utilities: add a simple function (standardize) to scale any data using splits.
    • Preprocessing: added a new class (Preprocess) to be able to preprocess data before creating the datasets/ dataloaders. This is mainly to test different target preprocessing techniques.
    • Utils added Nan2Value batch transform to remove any nan values in the dataset.
    • Added a new utility function to easy the creation of a single TSDataLoader when no splits are used (for example with unlabeled datasets).
    • Added a new function to quickly create empty arrays on disk or in memory (create_empty_array).

  • Models:

    • TST: Added option to visualize self-attention maps.
    • Added 3 new SOTA models: MiniRocketClassifier and MiniRocketRegressor for datasets <10k samples, and MiniRocket (Pytorch) which supports any dataset size.
    • Added a simple function to create a naive forecast.
    • Added future_mask to TSBERT to be able to train forecasting models.
    • Added option to pass any custom mask to TSBERT.

  • Training:

    • PredictionDynamics callback: allows you to visualize predictions during training.

  • Tutorial notebooks:

    • New notebook demonstrating the new PredictionDynamics callback.

Bug Fixes

  • Models:
    • 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.

0.2.14

New Features

  • Data:

    • External: added a new function get_Monash_data to get extrinsic regression data.

  • Models:

    • Added show_batch functionality to TSBERT.

0.2.13

New Features

  • General: Added min requirements for all package dependencies.

  • Data:

    • Validation: added split visualization (show_plot=True by default).
    • Data preprocessing: add option to TSStandardize or TSNormalize by_step.
    • Featurize time series: added tsfresh library to allow the creation of features from time series.

  • Models:

    • Updated ROCKET to speed up feature creation and allow usage of large datasets.
    • Added change_model_head utility function to ease the process of changing an instantiated models head.
    • conv_lin_3d_head function to allow generation of 3d output tensors. This may be useful for multivariate, multi-horizon direct (non-recursive) time series forecasting, multi-output regression tasks, etc.
    • Updated TST (Time series transformer) to allow the use of residual attention (based on He, R., Ravula, A., Kanagal, B., & Ainslie, J. (2020). Realformer: Transformer Likes Informed Attention. arXiv preprint arXiv:2012.11747.)
    • provided new functionality to transfer model's weights (useful when using pre-trained models).
    • updated build_ts_model to be able to use pretrained model weights.

  • Training:

    • TSBERT: a new callback has been added to be able to train a model in a self-supervised manner (similar to BERT).

  • Tutorial notebooks:

    • I've added a new tutorial notebook to demonstrate how to apply TSBERT (self-supervised method for time series).

Bug Fixes

  • Data:
    • ROCKET: fixed a bug in create_rocket_features.

0.2.12

New Features

  • Data:
    • core: get_subset_dl and get_subset_dlsconvenience function have been added.
    • data preparation: SlidingWindow and SlidingWindowPanel functions are now vectorized, and are at least an order of magnitude faster.
  • Models:
    • XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification have been added. Official code not released yet. This is a stete-of-the-art time series model that combines Conv1d and Conv2d and has good explainability.
  • Training:
    • learner: ts_learner and tsimage_learner convenience functions have been added, as well as a get_X_preds methods to facilitate the generation of predictions.

0.2.8

New Features

  • Data:
    • data preparation: a new SlidingWindowPanel function has been added to help prepare the input from panel data. SlidingWindow has also been enhanced.
    • new preprocessors: TSRobustScaler, TSClipOutliers, TSDiff, TSLog, TSLogReturn
  • Models:
    • MLP and TCN (Temporal Convolutional Network) have been added.
  • Training:
    • Callback: Uncertainty-based data augmentation
    • Label-mixing transforms (data augmentation): MixUp1D, CutMix1D callbacks
  • Utility functions: build_ts_model, build_tabular_model, get_ts_dls, get_tabular_dls, ts_learner

0.2.4

New Features

  • Added support to Pytorch 1.7.

0.2.0

tsai 0.2.0 is a major update to the tsai library. These are the major changes made to the library:

  • New tutorial nbs have been added to demonstrate the use of new functionality like:
    • Time series data preparation
    • Intro to time series regression
    • TS archs comparison
    • TS to image classification
    • TS classification with transformers

New Features

  • More ts data transforms have been added, including ts to images.

  • New callbacks, like the state of the art noisy_student that will allow you to use unlabeled data.

  • 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.

  • Some of the models (those finishing with an plus) have additional, experimental functionality (like coordconv, zero_norm, squeeze and excitation, etc).