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LazyGrid: Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.

Project description

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LazyGrid is a python package providing an automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.

Before fitting a model or a pipeline step, LazyGrid checks inside an internal SQLite database if the model has already been fitted. If the model is found, it won’t be fitted again.

Documentation for the latest stable version is available on ReadTheDocs.

Getting Started

You can install LazyGrid along with all its dependencies from PyPI:

$ pip install -r requirements.txt lazygrid

or from source code:

$ git clone https://github.com/glubbdubdrib/lazygrid.git
$ cd ./lazygrid
$ pip install -r requirements.txt .

LazyGrid is compatible with Python 3.5 and above.

Documentation

Documentation for the latest stable version is available on ReadTheDocs.

Running tests

You can run all unittests from command line by using python:

$ python -m unittest discover

or coverage:

$ coverage run -m unittest discover

Contributing

Please read Contributing.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

  • Pietro Barbiero - Mathematical engineer - GitHub
  • Giovanni Squillero - Professor of computer science at Politecnico di Torino - GitHub

Licence

Copyright 2019 Pietro Barbiero and Giovanni Squillero.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and limitations under the License.

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