dwave-scikit-learn-plugin 0.0.1

A plugin to scikit-learn for quantum hybrid solving

D-Wave scikit-learn Plugin

This package provides a scikit-learn transformer for feature selection using a quantum-classical hybrid solver.

For an introduction to hybrid methods for feature selection, see the Feature Selection example Jupyter Notebook.

The package's main class, SelectFromQuadraticModel, can be used in any existing sklearn pipeline.

Examples

A minimal example of using the plugin:

from dwave.plugins.sklearn.transformers import SelectFromQuadraticModel
import numpy as np

# generate uniformly random data, 10,000 observations and 300 features
data = np.random.uniform(-10, 10, size=(10_000, 300))

outcome = np.asarray(np.random.uniform(0, 1, size=10_000) > 0.5, dtype=int)

# instantiate the feature selection class
selector = SelectFromQuadraticModel()

# solve the feature-selection problem
data_transformed = selector.fit_transform(data, outcome)

For large problems, the default runtime may be insufficient. You can use the CQM solver's min_time_limit method to find the minimum accepted runtime for your problem; alternatively, simply submit as above and check the returned error message for the required runtime.

The feature selector can be re-instantiated with a longer time limit.

# instantiate the feature selection class with a longer time limit
selector = SelectFromQuadraticModel(time_limit=200)

Installation

To install the core package:

pip install dwave-scikit-learn-plugin

License

Released under the Apache License 2.0

Contributing

Ocean's contributing guide has guidelines for contributing to Ocean packages.

Release Notes

dwave-scikit-learn-plugin makes use of reno to manage its release notes.

When making a contribution to dwave-scikit-learn-plugin that will affect users, create a new release note file by running

reno new your-short-descriptor-here

You can then edit the file created under releasenotes/notes/. Remove any sections not relevant to your changes. Commit the file along with your changes.