mlstream

Machine learning for streamflow prediction.

PyPI: https://pypi.org/project/mlstream/

Documentation: https://mlstream.readthedocs.io/

Usage

This project is work in progress. The idea is to create an easy way of training machine learning streamflow models: Just provide your data, select a model (or provide your own), and get the predictions.

Training

exp = Experiment(data_path, is_train=True, run_dir=run_dir,
                 start_date='01012000', end_date='31122015',
                 basins=train_basin_ids, 
                 forcing_attributes=['precip', 'tmax', 'tmin'],
                 static_attributes=['area', 'regulation'])

exp.set_model(model)
exp.train()

Inference

run_dir = Path('./experiments')
exp = Experiment(data_path, is_train=False, 
                 run_dir=run_dir, 
                 basins=test_basin_ids,
                 start_date='01012016', end_date='31122018')
model.load(run_dir / 'model.pkl')
exp.set_model(model)  
results = exp.predict()

Metadata

Release files for mlstream 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlstream 0.1.2
File	Size	Uploaded	Provenance	Actions
mlstream-0.1.2.tar.gz	20.0 kB	Feb 14, 2020	Without provenance	Details for mlstream-0.1.2.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for mlstream 0.1.2
File	Interpreter	ABI	Platform	Actions Reset
mlstream-0.1.2-py3-none-any.whl 28.5 kB Feb 14, 2020 Without provenance	Python 3	none	any	Details for mlstream-0.1.2-py3-none-any.whl

Total release size: 48.5 kB

Release files / mlstream-0.1.2.tar.gz

Download URL	mlstream-0.1.2.tar.gz
Size	20.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`e6867fd4236de880fe601d0cd949e04aaa836acf190be04e8895d260e9448b65`
BLAKE2b-256 checksum How to use checksums	`0a5cd778315d7caeb58780676b56566f789181ffced5c84090b4f708d81c22e3`
Upload date	Feb 14, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191101 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.7.3`

Release files / mlstream-0.1.2-py3-none-any.whl

Download URL	mlstream-0.1.2-py3-none-any.whl
Size	28.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b93517e2c4cf08512c586dcad5de021248507c31807fb6a27bcbdb7e3894d354`
BLAKE2b-256 checksum How to use checksums	`ee4c1ec2a126e7cf4376eeeeae902ca2469fc96775c1e60493e1493fdd0717ba`
Upload date	Feb 14, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191101 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.7.3`

Security for mlstream 0.1.2

Report project as malware

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

mlstream 0.1.2