Repository files navigation

River is a Python library foronline 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 betweencreme andscikit-multiflow.

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

>>>frompprintimportpprint>>>fromriverimportdatasets>>>dataset=datasets.Phishing()>>>forx,yindataset:...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.

>>>fromriverimportcompose>>>fromriverimportlinear_model>>>fromriverimportmetrics>>>fromriverimportpreprocessing>>>model=compose.Pipeline(...preprocessing.StandardScaler(),...linear_model.LogisticRegression()... )>>>metric=metrics.Accuracy()>>>forx,yindataset:...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>>>metricAccuracy:89.28%

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

River is intended to work withPython 3.10 and above. Installation can be done withpip:

pip install river

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

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

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

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

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 outthe API for a comprehensive overview

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 toconcept 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.

• Documentation
• Package releases
• awesome-online-machine-learning
• 2022 presentation at GAIA
• Online Clustering: Algorithms, Evaluation, Metrics, Applications and Benchmarking fromKDD'22.

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 toopen an issue if you think you've spotted a bug or a performance issue.
• Ourroadmap is public. Feel free to work on anything that catches your eye, or to make suggestions.

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

If River has been useful to you, and you would like to cite it in a scientific publication, please refer to thepaper 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}}

River is free and open-source software licensed under the3-clause BSD license.