https://github.com/aleximb/automl-streams

AutoML framework for implementing automated machine learning on data streams
https://github.com/aleximb/automl-streams

automl data-streams scikit-learn

Last synced: 9 days ago
JSON representation

AutoML framework for implementing automated machine learning on data streams

• Host: GitHub
• URL: https://github.com/aleximb/automl-streams
• Owner: AlexImb
• License: mit
• Created: 2020-01-11T18:45:45.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-06-29T21:05:10.000Z (over 1 year ago)
• Last Synced: 2024-04-28T19:03:54.801Z (6 months ago)
• Topics: automl, data-streams, scikit-learn
• Language: Python
• Homepage: https://pypi.org/project/automl-streams
• Size: 80.4 MB
• Stars: 14
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

README

        

# AutoML Streams

An AutoML framework for implementing automated machine learning on data streams 

architectures in production environments.

# Installation

From `pip`

```shell

pip install -U automl-streams

```

or `conda`:

```shell

conda install automl-streams

```

# Usage

```py

from skmultiflow.trees import HoeffdingTree

from skmultiflow.evaluation import EvaluatePrequential

from automlstreams.streams import KafkaStream

stream = KafkaStream(topic, bootstrap_servers=broker)

stream.prepare_for_use()

ht = HoeffdingTree()

evaluator = EvaluatePrequential(show_plot=True,

                                pretrain_size=200,

                                max_samples=3000)

evaluator.evaluate(stream=stream, model=[ht], model_names=['HT'])

```

More demonstrations available in the [demos](./demos) directory.

# Development

Create and activate a `virtualenv` for the project:

```shell

$ virtualenv .venv

$ source .venv/bin/activate

```

Install the `development` dependencies:

```shell

$ pip install -e . 

```

Install the app in "development" mode:

```shell

$ python setup.py develop  

```

# Paper

https://arxiv.org/abs/2106.07317

```bibtex

@article{DBLP:journals/corr/abs-2106-07317,

  author       = {Alexandru{-}Ionut Imbrea},

  title        = {Automated Machine Learning Techniques for Data Streams},

  journal      = {CoRR},

  volume       = {abs/2106.07317},

  year         = {2021},

  url          = {https://arxiv.org/abs/2106.07317},

  eprinttype    = {arXiv},

  eprint       = {2106.07317},

  timestamp    = {Wed, 16 Jun 2021 10:42:19 +0200},

  biburl       = {https://dblp.org/rec/journals/corr/abs-2106-07317.bib},

  bibsource    = {dblp computer science bibliography, https://dblp.org}

}

```