federerjiang/river: 🌊 Online machine learning in Python · GitHub

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on streaming data.

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 = metric.update(y, y_pred)  # update the metric
...     model = model.learn_one(x, y)      # make the model learn

>>> metric
Accuracy: 89.20%

River is intended to work with Python 3.6 or above. Installation can be done with pip:

pip install river

There are wheels available for Linux, MacOS, and Windows, which means that 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 --upgrade

Or, through SSH:

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

Machine learning is often done in a batch setting, whereby a model is fitted to a dataset in one go. This results in a static model which has to be retrained in order to learn from new data. In many cases, this isn't elegant nor efficient, and usually incurs a fair amount of technical debt. Indeed, if you're using a batch model, then you need to think about maintaining a training set, monitoring real-time performance, model retraining, etc.

With River, we encourage a different approach, which is to continuously learn a stream of data. This means that the model process one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning (and therefore River!) might be what you're looking for.

Here are some benefits of using River (and online machine learning in general):

• Incremental: models can update themselves in real-time.
• Adaptive: models can adapt to concept drift.
• Production-ready: working with data streams makes it simple to replicate production scenarios during model development.
• Efficient: models don't have to be retrained and require little compute power, which lowers their carbon footprint
• Fast: when the goal is to learn and predict with a single instance at a time, then River is an order of magnitude faster than PyTorch, Tensorflow, and scikit-learn.

• Linear models with a wide array of optimizers
• Nearest neighbors, decision trees, naïve Bayes
• Progressive model validation
• Model pipelines as a first-class citizen
• Anomaly detection
• Recommender systems
• Time series forecasting
• Imbalanced learning
• Clustering
• Feature extraction and selection
• Online statistics and metrics
• Built-in datasets
• And much more

• Documentation
• Benchmarks
• Issue tracker
• Package releases

• PyData Amsterdam 2019 presentation (slides, video)
• Toulouse Data Science Meetup presentation
• Machine learning for streaming data with creme
• Hong Kong Data Science Meetup presentation

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

There are three ways for users to get involved:

• Issue tracker: this place is meant to report bugs, request for minor features, or small improvements. Issues should be short-lived and solved as fast as possible.
• Discussions: you can ask for new features, submit your questions and get help, propose new ideas, or even show the community what you are achieving with River! If you have a new technique or want to port a new functionality to River, this is the place to discuss.
• Roadmap: you can check what we are doing, what are the next planned milestones for River, and look for cool ideas that still need someone to make them become a reality!

Please check out the contribution guidelines if you want to bring modifications to the code base. You can view the list of people who have contributed here.

These are companies that we know have been using River, be it in production or for prototyping.

Feel welcome to get in touch if you want us to add your company logo!

Sponsors

Collaborating institutions and groups

If river has been useful for your research and you would like to cite it in an scientific publication, please refer to this paper:

@misc{2020river,
      title={River: machine learning for streaming data in Python},
      author={Jacob Montiel and Max Halford and Saulo Martiello Mastelini
              and Geoffrey Bolmier and Raphael Sourty and Robin Vaysse
              and Adil Zouitine and Heitor Murilo Gomes and Jesse Read
              and Talel Abdessalem and Albert Bifet},
      year={2020},
      eprint={2012.04740},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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