river

river_logo

River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow.

⚡️ Quickstart

As a quick example, we'll train a logistic regression to classify the website phishing dataset. Here's a look at the first observation in the dataset.

>>> from pprint import pprint
>>> from river import datasets

>>> dataset = datasets.Phishing()

>>> for x, y in dataset:
...     pprint(x)
...     print(y)
...     break
{'age_of_domain': 1,
 'anchor_from_other_domain': 0.0,
 'empty_server_form_handler': 0.0,
 'https': 0.0,
 'ip_in_url': 1,
 'is_popular': 0.5,
 'long_url': 1.0,
 'popup_window': 0.0,
 'request_from_other_domain': 0.0}
True

Now let's run the model on the dataset in a streaming fashion. We sequentially interleave predictions and model updates. Meanwhile, we update a performance metric to see how well the model is doing.

>>> from river import compose
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing

>>> model = compose.Pipeline(
...     preprocessing.StandardScaler(),
...     linear_model.LogisticRegression()
... )

>>> metric = metrics.Accuracy()

>>> for x, y in dataset:
...     y_pred = model.predict_one(x)      # make a prediction
...     metric.update(y, y_pred)           # update the metric
...     model.learn_one(x, y)              # make the model learn

>>> metric
Accuracy: 89.28%

Of course, this is just a contrived example. We welcome you to check the introduction section of the documentation for a more thorough tutorial.

🛠 Installation

River is intended to work with Python 3.11 and above. Installation can be done with pip:

pip install river

There are wheels available for Linux, MacOS, and Windows. This means you most probably won't have to build River from source.

River's core online interface (learn_one / predict_one) has no pandas dependency. The mini-batch interface (learn_many, predict_many, predict_proba_many, transform_many) is built on pandas and is opt-in:

pip install "river[pandas]"

You can install the latest development version from GitHub as so:

pip install git+https://github.com/online-ml/river --upgrade
pip install git+ssh://git@github.com/online-ml/river.git --upgrade  # using SSH

This method requires having Cython and Rust installed on your machine.

🔮 Features

River provides online implementations of the following family of algorithms:

Linear models, with a wide array of optimizers
Decision trees and random forests
(Approximate) nearest neighbors
Anomaly detection
Drift detection
Recommender systems
Time series forecasting
Bandits
Factorization machines
Imbalanced learning
Clustering
Bagging/boosting/stacking
Active learning

River also provides other online utilities:

Feature extraction and selection
Online statistics and metrics
Preprocessing
Built-in datasets
Progressive model validation
Model pipelines

Check out the API for a comprehensive overview

🤔 Should I be using River?

You should ask yourself if you need online machine learning. The answer is likely no. Most of the time batch learning does the job just fine. An online approach might fit the bill if:

You want a model that can learn from new data without having to revisit past data.
You want a model which is robust to concept drift.
You want to develop your model in a way that is closer to what occurs in a production context, which is usually event-based.

Some specificities of River are that:

It focuses on clarity and user experience, more so than performance.
It's very fast at processing one sample at a time. Try it, you'll see.
It plays nicely with the rest of Python's ecosystem.

🔗 Useful links

👐 Contributing

Feel free to contribute in any way you like, we're always open to new ideas and approaches.

Open a discussion if you have any question or enquiry whatsoever. It's more useful to ask your question in public rather than sending us a private email. It's also encouraged to open a discussion before contributing, so that everyone is aligned and unnecessary work is avoided.
Feel welcome to open an issue if you think you've spotted a bug or a performance issue.
Our roadmap is public. Feel free to work on anything that catches your eye, or to make suggestions.

Please check out the contribution guidelines if you want to bring modifications to the code base.

🤝 Affiliations

affiliations

💬 Citation

If River has been useful to you, and you would like to cite it in a scientific publication, please refer to the paper published at JMLR:

@article{montiel2021river,
  title={River: machine learning for streaming data in Python},
  author={Montiel, Jacob and Halford, Max and Mastelini, Saulo Martiello
          and Bolmier, Geoffrey and Sourty, Raphael and Vaysse, Robin and Zouitine, Adil
          and Gomes, Heitor Murilo and Read, Jesse and Abdessalem, Talel and others},
  year={2021}
}

📝 License

River is free and open-source software licensed under the 3-clause BSD license.

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river 0.26.0

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Algorithm	Hash digest
SHA256	`f226f03f15f85f7a93d0ff1f67dffc6fd56d45f77ac6be8fa89c0a2e08c0df44`
MD5	`fbf26c5ae21b5ee1d1a44ba1a5b1a6af`
BLAKE2b-256	`cc435f4d2bb832295aedafb2d69274047b667157d80f3f30605b436173641a91`

Algorithm	Hash digest
SHA256	`93b12716049ca6656c3fb9dc9409400f775d41449079b65d9d8d9cfca7864032`
MD5	`f129bb7a21633ae995fc9fe37ac717e5`
BLAKE2b-256	`4e8fa0cf04911c8397104e6fac0acb843a7b5474f750d34ad7a50c7565bfd8a9`

Algorithm	Hash digest
SHA256	`cd91187763114a249e8fcd009344a98f25533cb66cc8c6505b29a28a0c3dd185`
MD5	`777e6a52588fb5dd2d73ed7d799d8fe5`
BLAKE2b-256	`036f81a959a62ec7c3746762e90c9aa0e2da58d5361ea5883b6de7efa6e342c1`

Algorithm	Hash digest
SHA256	`e040afb605cb7bff02787f28379de22e134cb2cd289498ee1293aee056a237af`
MD5	`d8a50338cd468dcac0b1e3690d510e63`
BLAKE2b-256	`87d52e4cdbb9d2e4a6c5ab39c34c8cb99fef54b906a70415a8bb61978f2cb2d4`

Algorithm	Hash digest
SHA256	`6bf8a5b1425097ace6a845171140f6d028e3dbc82ea57f510cb6da90eb2a9119`
MD5	`fdd3878007711d02793680a7ef36f9bc`
BLAKE2b-256	`3a3ca18852ec269c8a2a5c3ea92ef4dc34be6e56d11a6a568cf3dac977858573`

Algorithm	Hash digest
SHA256	`9cfb0552a45de44f0e0e7b8a17489ef8a1ae51348d0b0fdc283fe85fda22e0fe`
MD5	`8e0eafde21477ded367cd6fc998524b8`
BLAKE2b-256	`654fa45fefd077de50148e26d62826ec54642cf3ae483a0aa7bde16e989f73e4`

Algorithm	Hash digest
SHA256	`dfcd82f6b98073160ddda2261ae6d2e7c17edc053e1d8f29f1e88425cf8e68c8`
MD5	`2e4cd052664099a5fa2e571d5bf15845`
BLAKE2b-256	`d7b74ddbc3f5fdbb9627b0632b1716bcb3e07c850003cccf376197c9b7426bbe`

Algorithm	Hash digest
SHA256	`1e960f7d5c3f353393c9dc8b71f25cf12a087d5a30313cc570192629528feb09`
MD5	`47574446b3ff8438f93167104ca4feba`
BLAKE2b-256	`4f3ebbc6f6158a5d8c35460e729fb81c0773ea3884cf95cb1e7a2b0b24cd59f2`

Algorithm	Hash digest
SHA256	`7575d9f7691b036584f40ab562668079965f8c0863b68b68d8c10b59c5512585`
MD5	`6099bd4a5a6c010ebef584125f4d24d4`
BLAKE2b-256	`f69c8322e1e07de88d4b5da6dfc6ad92373a33b1494ebec4f12822d980c06df5`