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Recent Releases of NeuralHydrology

NeuralHydrology - v1.12.0

Added

  • Added input replacing, masked mean, and attention support to InputLayer. This allows the models to handle NaN inputs. More details in the preprint How to deal w___ missing input data. Use nan_handling_method and related config arguments to control this behavior. Note that the sample validation remains unchanged, i.e., during training we only allow simulating NaNs through nan_step_probability/nan_sequence_probability.
  • BaseDataset now returns dynamic features as a dictionary and no longer as a merged "x_d" tensor. This makes it much easier to build models like MC-LSTM that handle certain inputs (e.g., precipitation) seperately from the other inputs.
  • MacOS Metal accelerator support (neuralhydrology/neuralhydrology#213, thanks @tberends)
  • Allow changing the run directory for evaluation (neuralhydrology/neuralhydrology#195, thanks @BaptisteFrancois)

Fixes

  • Hourly frequency identifiers changed from H to h. This resolves pandas deprecation warnings.
  • Fixed metrics writing when cfg.metrics is specified as all and when doing multi-target prediction
  • Fixed FLV metrics docstring (neuralhydrology/neuralhydrology#203, thanks @XuHuanHydro)
  • Fixed forecast configuration documentation
  • Fixed forecast overlap regularization
  • Updated CI workflow

Hydrosphere - Freshwater and Hydrology - Python
Published by gauchm about 2 months ago

NeuralHydrology - v1.11.0

Added

  • Mamba state space model (#163, thanks @taddyb)
  • AdamW optimizer option and ReLU activation function option (#169, thanks @Multihuntr)

Fixes

  • Added missing API docs for hybrid models
  • Fix in the implementation of the SHM hybrid model (thanks @eduardoAcunaEspinoza)
  • Ensure compatibility with Pandas >=2.2 (frequency stuff and items vs. iteritems)
  • Attribute loading in Caravan dataset (#168)

Hydrosphere - Freshwater and Hydrology - Python
Published by gauchm 9 months ago

NeuralHydrology - v1.10.0

Added

  • HybridModel A wrapper class to combine deep learning models and conceptual hydrology models, where the deep learning model parameterizes the conceptual model, which also needs to be implemented in PyTorch. In the current implementation, the deep learning model is always a standard LSTM, as commonly used in literature.
  • BaseConceptualModel a parent class to facilitate adding new conceptual models into the NeuralHydrology framework.
  • SHM one implementation of a BaseConceptualModel that adds a modified version of the SHM to the modelzoo. See the documentation for details and references about this model.

Fixes

  • A solution that removes the circular import error of #157 As a result of that, load_scaler.py has been moved to datautils/utils.py
  • An update of the documentation to resolve #138
  • Some corner cases in the sampling utils related to #154
  • Minor changes to the Tester class to resolve #158

Huge thanks to @eduardoAcunaEspinoza for contributing the HybridModel, BaseConceptualModel and SHM implementation.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 1 year ago

NeuralHydrology - v1.9.1

  • Fix recursive import issue
  • Fix typos
  • Remove old and broken code.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 1 year ago

NeuralHydrology - v.1.9.0

Added

  • Option to end an epoch early after a given number of update steps. Use max_updates_per_epoch to define a fixed number of update steps per epoch. This can be useful if you train a model with many samples and want to evaluate the model more frequently than just after each full epoch. See also #131
  • Redesign of the config argument experiment_name. You can now add wildcards to the experiment name using curly brackets and any config argument inside of it. E.g. my_awesome_run_{batch_size}_{hidden_size}. Now, if you start training, the wildcards will be replaced with the respective values of the config arguments (here batch size and hidden size), keeping the name of the config argument for easier recognition. To our experience, this makes it easier if you e.g. to some hyper parameter tuning and you don't want to change the experiment name every time but still want to have expressive folder names and run names in Tensorboard. For details, check the documentation.

Fixes

  • Some Pandas Futurewarnings

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 1 year ago

NeuralHydrology - v.1.8.1

Fixes

  • Fixed #133 and also fixed an issue where the metrics csv file would be empty for multi-frequency runs.
  • Fixes a bug where uncertainty (GMM & CMAL) runs with predict_last_n > 1 would generate incrorrect predictions due to a mixup of dimensions. This was discovered in an MTS-LSTM setting, where the hourly branch has predict_last_n 24. Visually, this resulted in 24-hourly steps in the predictions. UMAL and MCD are unaffected because they sample differently.
  • Fixes and issue with uncertainty runs with active negative sample handling where centering would cut off values below the normalized value of zero (i.e., usually the mean) rather than the actual zero. This commit fixes this by calculating the normalized value of zero from the scaler as the cutoff value. Also includes a faster check for negative values (vectorized torch.any instead of any). Relates also to #88

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert almost 2 years ago

NeuralHydrology - v.1.8.0

New Features

  • Option to save all outputs from any model instead of just the target variable (cfg.save_all_validation_output)
  • Several new forecasting models:
    • HandoffForecastLSTM: a forecasting model that uses a state-handoff to transition from a hindcast sequence model to a forecast sequence (LSTM) model.
    • MultiheadForecastLSTM: a forecasting model that runs a sequential (LSTM) model up to the forecast issue time, and then directly predicts a sequence of forecast timesteps without using a recurrent rollout.
    • SequentialForecastLSTM: a forecasting model that uses a single sequential (LSTM) model that rolls out through both the hindcast and forecast sequences.
    • StackedForecastLSTM: a forecasting model that uses two stacked sequential (LSTM) models to handle hindcast vs. forecast.
  • Option to add a timestep counter for forecasting (cfg.timestep_counter)

To use the new forecasting models, there are several new config options, most notably cfg.forecast_inputs and cfg.hindcast_inputs to specify which inputs are used for forecasting vs. for hindcasting. See the documentation for more details.

  • Enable non-verbose mode in trainer and tester (#124)
  • Enable predictions in basins with no observations (#121)

Fixes

  • Error in FDC slope signatures formula (#125)

Hydrosphere - Freshwater and Hydrology - Python
Published by gauchm almost 2 years ago

NeuralHydrology - v.1.7.0

Fixes

Note that with this release, the umal_extend_batch method is moved to utils.samplingutils, and training.utils and training.umaltrainer are removed. The UMAL functionality remains unchanged, though.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert almost 2 years ago

NeuralHydrology - v.1.6.0

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 2 years ago

NeuralHydrology - v.1.5.1

Fix

  • Fix in basetrainer.py to resolve problems around finetuning, see #105

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 2 years ago

NeuralHydrology - v.1.5.0

Fixes

  • Updated documentation on Installation and data download (of CAMELS US).

Added

  • Dataset class for the Caravan dataset

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 2 years ago

NeuralHydrology - v.1.4.0

Updates/Fixes

  • Adapted CAMELS-CL data loader to new data layout of "Enero 2022" dataset version.
  • Resolved np.int deprecation warnings
  • In LamaH discharge loader, replace -999 values (invalid observation marker) with np.nan, when loading data.
  • Run scheduler now moves processed configs to newly created sub-directory, which makes it easier to continue the scheduler in case it fails at any point.

Additions

  • Added new metric that checks if a model misses or hits a flood peak.

Internal, but worth to mention

  • Dataset objects now contain the date that corresponds to the target(s).

First time contributors

  • @SebaDro with #95
  • @shamshaw with #97

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 2 years ago

NeuralHydrology - v.1.3.0

New Features:

  • Added new dataset classes for the CAMELS-BR, CAMELS-AUS, and LamaH-CE dataset.
  • Added the AR-LSTM (as proposed in this paper). This model can be used by setting the config argument model: arlstm. Please refer to the documentation about specific requirements of this model.
    • Added an option random_holdout_from_dynamic_features to the config that applies dropout on the time series features by sampling two Bernoulli processes with different rate parameters. This is used for training the arlstm to simulate missing (autoregressive) inputs already during training, which are then replaced by the model outputs of previous timesteps.

Fixes:

  • A bug when writing the metrics.csv file during validation if the first basin (in the basin list) has no validation data.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.5

Bugfixes

  • fixed InputLayer that was misbehaving when using single frequency data that was resampled from a different temporal resolution (from raw data).
  • fixed severe bug when using use_basin_id_encoding: True. Essentially during training, all samples in a batch had the one-hot vector of the last sample in the batch.

Updates

  • Removed prediction_interval_plots as it was a duplicate of percentile_plot. Also updated docstring of percentile_plot

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.4

Fixes

  • Beta-KGE was not in the list of available metrics returned by neuralhydrology.evaluation.metrics.get_available_metrics(). Equally, it was not computed in neuralhydrology.evaluation.metrics.calculate_all_metrics() and neuralhydrology.evaluation.metrics.calculate_metrics() raised an ValueError because of an unknown metric

Other

  • Added CITATION.cff for the JOSS paper

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.3 JOSS paper publication

Updates

  • removed broken cuda9.2 environment and added a new cuda11.3 environment
  • updated docs of GenericDataset and description in Tutorial 3

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.2

New

  • NeuralHydrology can now be installed from PyPI (we added automatic upload to PyPI for every tagged released). #66

Fixes

  • Some typo and type annotation fixes in the Transformer model

Updates

  • Updated installation guide to include info about PyPI installation options
  • Added missing config argument allow_subsequent_nan_losses to config docs

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.1

Updates and Additions

  • Added tutorial that explains the download process for data that is required to run our tutorials locally. #68
  • Updated tutorials better highlight the data requirements. #68
  • Update tutorials to be easier to run on CPU environments without changes of the config/code #68
  • Added templates for opening an issue
  • Fixed typos

Fixes

  • Fixed problem of loading hourly streamflow data from csv (CAMELS US dataset) #67
  • Fixed problems with logging the git hash that made the commit hash appear badly formatted

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 3 years ago

NeuralHydrology - v.1.2.0

New Feature

  • If you use NeuralHydrology as a git repository and have uncommited changes in your local copy, we added an option to include the git diff in the run directory. Set save_git_diff to True to make use of this feature.

Fixes

  • fixed a bug from v.1.1.0 that caused problems when evaluating basins with all-NaN targets

Other

  • Added guide for contributing to NeuralHydrology
  • Update the tutorials to include to the underlying jupyter notebooks and removed full paths from some notebooks.
  • Updated the quickstart guide to include a better guide for setting up NeuralHydrology

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 3 years ago

NeuralHydrology - Additional loss logging and some language fixes

New Features

  • Besides logging validation metrics, now also the validation loss is logged to tensorboard (always), computed as the weighted (by number of batches) average loss across all basins.

Fixes

  • Spelling mistakes

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 3 years ago

NeuralHydrology - v.1.0.0 First major release

A long time has passed since we started this library for internal research use but also since its publication as an open source library. After an extended beta period we are happy to finally bump NeuralHydrology to it's first major release version.

New Features

  • We removed the dependency of the pickle library for storing the scaler and basin-id-to-integer dictionaries. pickle has brought a lot of headaches in the past, because it can get troublesome to run old models after upgrading the Python libraries. This PR removes the pickle dependencies and from now on, the scaler (used or normalization) and basin-id-to-integer dictionaries (in case of using basin-one-hot-encoding) are stored in YAML files. However, the old format is still supported so that old runs can still be used.

Additionally

  • Updates of the description of the config argument metrics and clip_targets_to_zero

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 3 years ago

NeuralHydrology - Update of data use in tutorial noteboks

In some of the tutorials, we used the updated forcing data of the CAMELS US data set but we missed to link to the corresponding download links. This has been updated and information on the data requirements was added. We also removed the requirement for the updated forcing data on the very first tutorial so that this can be run with only the original CAMELS US data set.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert over 3 years ago

NeuralHydrology - Small performance improvements, minor fix

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert almost 4 years ago

NeuralHydrology - Bugfixes for MTS-LSTM uncertainty sampling and ensemble script

  • MTS-LSTM failed with uncertainty heads, due to a missing class attribute.
  • Ensemble script failed due to issues with the new datetime logic (#30) and type incompatibilities.

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert almost 4 years ago

NeuralHydrology - Multi-target, multi-freq uncertainty support

With this update we will transition into the beta phase of v.1.0.0 as it includes the last thing we had planned for the first major version release, which is full multi-target, multi-frequency (and the combination of both) regression and uncertainty heads. We will most likely add some tutorial show-casing some of the new features. For everyone who is using the library, we would be grateful to report any bugs you encounter, so we can get rid of them.

New Features/Additions

  • Support for multi-frequency and multi-target (and the combination of both) uncertainty models.
  • Added tests for frequency handling

Fixes

  • Issues around various frequencies and combinations of those. Closes #30

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 4 years ago

NeuralHydrology - added MC-LSTM, multi-target support for all losses

Another minor updated before v.1.0.0 final.

New Features

  • (weighted) multi-target support for all losses
  • added MC-LSTM to modelzoo, as proposed in this publication. The publication also includes benchmarking results using the CAMELS US dataset.

Fixes

  • ignore capitalization of loss name in config

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 4 years ago

NeuralHydrology - Uncertainty heads and minor additions/fixes

NeuralHydrology 1.0.0-alpha Release Notes

This version marks the first steps towards our first major version release. The biggest addition are the newly added uncertainty heads that are now available for all models. That is, it is now possible to train any of the implemented models with probabilistic model heads. For details on these probabilistic model heads, see Klotz et al. (2021). The uncertainty heads support all models, also the multi-frequency models like MTS-LSTM. The only limitation so far are multi-targets (per-frequency), which is currently not supported but will be added within the next days/weeks. We also plan to release a tutorial around the uncertainty heads soon.

New Features

  • The optional embedding of static and dynamic inputs was re-implemented and made more flexible for possible extensions in the future. It is now possible to specify distinct architectures for dynamic and static input embeddings. The config arguments statics/dynamics_embedding should now be dictionaries with keys 'type', 'hiddens', 'activation', 'dropout' or None (no embedding). The old way of specifying embeddings via embedding_hiddens etc. still works but is deprecated.
  • The config argument lagged_features does now accept a list of integers (as well as just an integer like before) to specify multiple lagged copies of a single feature.
  • The warning message of the metric functions does now include the basin id, if all simulations or observations are NaN, to facilitate the inspection of eventual problems with the model/data.
  • Different model heads for uncertainty estimation (see the corresponding publication by Klotz et al.). It is now possible to combine any model with different heads (config argument head, see Config arguments), instead of before only regression. The new heads are cmal, umal, gmm (see the linked publication for more details). All heads require special loss functions that are also provided and all models now have a sample function for the different head (e.g. model.sample_cmal() for a model trained with CMAL head), to sample from the (trained) model. There are also a couple of new config arguments that are required/optional for those heads, so make sure to checkout the specific sections in the Documentation

Fixes

  • Missing raise in the dump_config method of the Config class, if the file already exists.
  • Problem, when evaluating the validation period outside of the model training process with log_n_figures > 0 in the config
  • Problem in continue-training mode, when specifying device as special input argument

Hydrosphere - Freshwater and Hydrology - Python
Published by kratzert about 4 years ago