/automl-streams

AutoML framework for implementing automated machine learning on data streams

Primary LanguagePythonMIT LicenseMIT

AutoML Streams

An AutoML framework for implementing automated machine learning on data streams architectures in production environments.

Installation

From pip

pip install -U automl-streams

or conda:

conda install automl-streams

Usage

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 directory.

Development

Create and activate a virtualenv for the project:

$ virtualenv .venv
$ source .venv/bin/activate

Install the development dependencies:

$ pip install -e .

Install the app in "development" mode:

$ python setup.py develop

Paper

https://arxiv.org/abs/2106.07317

@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}
}