AutoML framework for implementing automated machine learning on data streams.
Project description
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
Project details
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