Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

automl-streams-0.0.1.tar.gz (15.8 kB view hashes)

Uploaded source

Built Distribution

automl_streams-0.0.1-py2.py3-none-any.whl (8.3 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page