Release Notes¶

v0.9.2 Maintenance Release (30/11/2021)¶

Added¶

Added support for running pytorch_lightning.trainer.test (#759)

Fixed¶

Fix inattention mutation to x_cont (#732).
Compatability with pytorch-lightning 1.5 (#758)

Contributors¶

eavae
danielgafni
jdb78

v0.9.1 Maintenance Release (26/09/2021)¶

Added¶

Use target name instead of target number for logging metrics (#588)
Optimizer can be initialized by passing string, class or function (#602)
Add support for multiple outputs in Baseline model (#603)
Added Optuna pruner as optional parameter in TemporalFusionTransformer.optimize_hyperparameters (#619)
Dropping support for Python 3.6 and starting support for Python 3.9 (#639)

Fixed¶

Initialization of TemporalFusionTransformer with multiple targets but loss for only one target (#550)
Added missing transformation of prediction for MLP (#602)
Fixed logging hyperparameters (#688)
Ensure MultiNormalizer fit state is detected (#681)
Fix infinite loop in TimeDistributedEmbeddingBag (#672)

Contributors¶

jdb78
TKlerx
chefPony
eavae
L0Z1K

v0.9.0 Simplified API (04/06/2021)¶

Breaking changes¶

Removed dropout_categoricals parameter from TimeSeriesDataSet. Use categorical_encoders=dict(<variable_name>=NaNLabelEncoder(add_nan=True)) instead (#518)
Rename parameter allow_missings for TimeSeriesDataSet to allow_missing_timesteps (#518)
Transparent handling of transformations. Forward methods should now call two new methods (#518):
- transform_output to explicitly rescale the network outputs into the de-normalized space
- to_network_output to create a dict-like named tuple. This allows tracing the modules with PyTorch’s JIT. Only prediction is still required which is the main network output.
Example:
```
def forward(self, x):
    normalized_prediction = self.module(x)
    prediction = self.transform_output(prediction=normalized_prediction, target_scale=x["target_scale"])
    return self.to_network_output(prediction=prediction)
```

Fixed¶

Fix quantile prediction for tensors on GPUs for distribution losses (#491)
Fix hyperparameter update for RecurrentNetwork.from_dataset method (#497)

Added¶

Improved validation of input parameters of TimeSeriesDataSet (#518)

v0.8.5 Generic distribution loss(es) (27/04/2021)¶

Added¶

Allow lists for multiple losses and normalizers (#405)
Warn if normalization is with scale < 1e-7 (#429)
Allow usage of distribution losses in all settings (#434)

Fixed¶

Fix issue when predicting and data is on different devices (#402)
Fix non-iterable output (#404)
Fix problem with moving data to CPU for multiple targets (#434)

Contributors¶

jdb78
domplexity

v0.8.4 Simple models (07/03/2021)¶

Added¶

Adding a filter functionality to the timeseries datasset (#329)
Add simple models such as LSTM, GRU and a MLP on the decoder (#380)
Allow usage of any torch optimizer such as SGD (#380)

Fixed¶

Moving predictions to CPU to avoid running out of memory (#329)
Correct determination of output_size for multi-target forecasting with the TemporalFusionTransformer (#328)
Tqdm autonotebook fix to work outside of Jupyter (#338)
Fix issue with yaml serialization for TensorboardLogger (#379)

Contributors¶

jdb78
JakeForsey
vakker

v0.8.3 Bugfix release (31/01/2021)¶

Added¶

Make tuning trainer kwargs overwritable (#300)
Allow adding categories to NaNEncoder (#303)

Fixed¶

Underlying data is copied if modified. Original data is not modified inplace (#263)
Allow plotting of interpretation on passed figure for NBEATS (#280)
Fix memory leak for plotting and logging interpretation (#311)
Correct shape of predict() method output for multi-targets (#268)
Remove cloudpickle to allow GPU trained models to be loaded on CPU devices from checkpoints (#314)

Contributors¶

jdb78
kigawas
snumumrik

v0.8.2 Fix for output transformer (12/01/2021)¶

Added missing output transformation which was switched off by default (#260)

v0.8.1 Adding support for lag variables (10/01/2021)¶

Added¶

Add “Release Notes” section to docs (#237)
Enable usage of lag variables for any model (#252)

Changed¶

Require PyTorch>=1.7 (#245)

Fixed¶

Fix issue for multi-target forecasting when decoder length varies in single batch (#249)
Enable longer subsequences for min_prediction_idx that were previously wrongfully excluded (#250)

Contributors¶

jdb78

v0.8.0 Adding multi-target support (03/01/2021)¶

Added¶

Adding support for multiple targets in the TimeSeriesDataSet (#199) and amended tutorials.
Temporal fusion transformer and DeepAR with support for multiple tagets (#199)
Check for non-finite values in TimeSeriesDataSet and better validate scaler argument (#220)
LSTM and GRU implementations that can handle zero-length sequences (#235)
Helpers for implementing auto-regressive models (#236)

Changed¶

TimeSeriesDataSet’s y of the dataloader is a tuple of (target(s), weight) - potentially breaking for model or metrics implementation Most implementations will not be affected as hooks in BaseModel and MultiHorizonMetric were modified. (#199)

Fixed¶

Fixed autocorrelation for pytorch 1.7 (#220)
Ensure reproducibility by replacing python set() with dict.fromkeys() (mostly TimeSeriesDataSet) (#221)
Ensures BetaDistributionLoss does not lead to infinite loss if actuals are 0 or 1 (#233)
Fix for GroupNormalizer if scaling by group (#223)
Fix for TimeSeriesDataSet when using min_prediction_idx (#226)

Contributors¶

jdb78
JustinNeumann
reumar
rustyconover

v0.7.1 Tutorial on how to implement a new architecture (07/12/2020)¶

Added¶

Tutorial on how to implement a new architecture covering basic and advanced use cases (#188)
Additional and improved documentation - particularly of implementation details (#188)

Changed (breaking for new model implementations)¶

Moved multiple private methods to public methods (particularly logging) (#188)
Moved get_mask method from BaseModel into utils module (#188)
Instead of using label to communicate if model is training or validating, using self.training attribute (#188)
Using sample((n,)) of pytorch distributions instead of deprecated sample_n(n) method (#188)

v0.7.0 New API for transforming inputs and outputs with encoders (03/12/2020)¶

Added¶

Beta distribution loss for probabilistic models such as DeepAR (#160)

Changed¶

BREAKING: Simplifying how to apply transforms (such as logit or log) before and after applying encoder. Some transformations are included by default but a tuple of a forward and reverse transform function can be passed for arbitrary transformations. This requires to use a transformation keyword in target normalizers instead of, e.g. log_scale (#185)

Fixed¶

Incorrect target position if len(static_reals) > 0 leading to leakage (#184)
Fixing predicting completely unseen series (#172)

Contributors¶

jdb78
JakeForsey

v0.6.1 Bugfixes and DeepAR improvements (24/11/2020)¶

Added¶

Using GRU cells with DeepAR (#153)

Fixed¶

GPU fix for variable sequence length (#169)
Fix incorrect syntax for warning when removing series (#167)
Fix issue when using unknown group ids in validation or test dataset (#172)
Run non-failing CI on PRs from forks (#166, #156)

Docs¶

Improved model selection guidance and explanations on how TimeSeriesDataSet works (#148)
Clarify how to use with conda (#168)

Contributors¶

jdb78
JakeForsey

v0.6.0 Adding DeepAR (10/11/2020)¶

Added¶

DeepAR by Amazon (#115)
- First autoregressive model in PyTorch Forecasting
- Distribution loss: normal, negative binomial and log-normal distributions
- Currently missing: handling lag variables and tutorial (planned for 0.6.1)
Improved documentation on TimeSeriesDataSet and how to implement a new network (#145)

Changed¶

Internals of encoders and how they store center and scale (#115)

Fixed¶

Update to PyTorch 1.7 and PyTorch Lightning 1.0.5 which came with breaking changes for CUDA handling and with optimizers (PyTorch Forecasting Ranger version) (#143, #137, #115)

Contributors¶

jdb78
JakeForesey

v0.5.3 Bug fixes (31/10/2020)¶

Fixes¶

Fix issue where hyperparameter verbosity controlled only part of output (#118)
Fix occasional error when .get_parameters() from TimeSeriesDataSet failed (#117)
Remove redundant double pass through LSTM for temporal fusion transformer (#125)
Prevent installation of pytorch-lightning 1.0.4 as it breaks the code (#127)
Prevent modification of model defaults in-place (#112)

v0.5.2 Fixes to interpretation and more control over hyperparameter verbosity (18/10/2020)¶

Added¶

Hyperparameter tuning with optuna to tutorial
Control over verbosity of hyper parameter tuning

Fixes¶

Interpretation error when different batches had different maximum decoder lengths
Fix some typos (no changes to user API)

v0.5.1 PyTorch Lightning 1.0 compatibility (14/10/2020)¶

This release has only one purpose: Allow usage of PyTorch Lightning 1.0 - all tests have passed.

v0.5.0 PyTorch Lightning 0.10 compatibility and classification (12/10/2020)¶

Added¶

Additional checks for TimeSeriesDataSet inputs - now flagging if series are lost due to high min_encoder_length and ensure parameters are integers
Enable classification - simply change the target in the TimeSeriesDataSet to a non-float variable, use the CrossEntropy metric to optimize and output as many classes as you want to predict

Changed¶

Ensured PyTorch Lightning 0.10 compatibility
- Using LearningRateMonitor instead of LearningRateLogger
- Use EarlyStopping callback in trainer callbacks instead of early_stopping argument
- Update metric system update() and compute() methods
- Use trainer.tuner.lr_find() instead of trainer.lr_find() in tutorials and examples
Update poetry to 1.1.0

v0.4.1 Various fixes models and data (01/10/2020)¶

Fixes¶

Model¶

Removed attention to current datapoint in TFT decoder to generalise better over various sequence lengths
Allow resuming optuna hyperparamter tuning study

Data¶

Fixed inconsistent naming and calculation of encoder_lengthin TimeSeriesDataSet when added as feature

Contributors¶

jdb78

v0.4.0 Metrics, performance, and subsequence detection (28/09/2020)¶

Added¶

Models¶

Backcast loss for N-BEATS network for better regularisation
logging_metrics as explicit arguments to models

Metrics¶

MASE (Mean absolute scaled error) metric for training and reporting
Metrics can be composed, e.g. 0.3* metric1 + 0.7 * metric2
Aggregation metric that is computed on mean prediction over all samples to reduce mean-bias

Data¶

Increased speed of parsing data with missing datapoints. About 2s for 1M data points. If numba is installed, 0.2s for 1M data points
Time-synchronize samples in batches: ensure that all samples in each batch have with same time index in decoder

Breaking changes¶

Improved subsequence detection in TimeSeriesDataSet ensures that there exists a subsequence starting and ending on each point in time.
Fix min_encoder_length = 0 being ignored and processed as min_encoder_length = max_encoder_length

Contributors¶

jdb78
dehoyosb

v0.3.1 More tests and better docs (13/09/2020)¶

More tests driving coverage to ~90%
Performance tweaks for temporal fusion transformer
Reformatting with sort
Improve documentation - particularly expand on hyper parameter tuning

Fixed¶

Fix PoissonLoss quantiles calculation
Fix N-Beats visualisations

v0.3.0 More testing and interpretation features (02/09/2020)¶

Added¶

Calculating partial dependency for a variable
Improved documentation - in particular added FAQ section and improved tutorial
Data for examples and tutorials can now be downloaded. Cloning the repo is not a requirement anymore
Added Ranger Optimizer from pytorch_ranger package and fixed its warnings (part of preparations for conda package release)
Use GPU for tests if available as part of preparation for GPU tests in CI

Changes¶

BREAKING: Fix typo “add_decoder_length” to “add_encoder_length” in TimeSeriesDataSet

Bugfixes¶

Fixing plotting predictions vs actuals by slicing variables

v0.2.4 Fix edge case in prediction logging (26/08/2020)¶

Fixed¶

Fix bug where predictions were not correctly logged in case of decoder_length == 1.

Added¶

Add favicon to docs page

v0.2.3 Make pip installable from master branch (23/08/2020)¶

Update build system requirements to be parsed correctly when installing with pip install https://github.com/jdb78/pytorch-forecasting/

v0.2.2 Improving tests (23/08/2020)¶

Add tests for MacOS
Automatic releases
Coverage reporting

v0.2.1 Patch release (23/08/2020)¶

This release improves robustness of the code.

Fixing bug across code, in particularly
- Ensuring that code works on GPUs
- Adding tests for models, dataset and normalisers
- Test using GitHub Actions (tests on GPU are still missing)
Extend documentation by improving docstrings and adding two tutorials.
Improving default arguments for TimeSeriesDataSet to avoid surprises

v0.2.0 Minor release (16/08/2020)¶

Added¶

Basic tests for data and model (mostly integration tests)
Automatic target normalization
Improved visualization and logging of temporal fusion transformer
Model bugfixes and performance improvements for temporal fusion transformer

Modified¶

Metrics are reduced to calculating loss. Target transformations are done by new target transformer

OutputMixIn