[](https://pypi.python.org/pypi/dwave-scikit-learn-plugin)
[](https://dl.circleci.com/status-badge/redirect/gh/dwavesystems/dwave-scikit-learn-plugin)
# D-Wave `scikit-learn` Plugin
This package provides a [scikit-learn](https://scikit-learn.org/) transformer for
[feature selection](https://en.wikipedia.org/wiki/Feature_selection) using a
quantum-classical [hybrid solver](https://docs.ocean.dwavesys.com/en/stable/concepts/hybrid.html).
This plugin makes use of a Leap™ quantum-classical hybrid solver. Developers can get started by
[signing up](https://cloud.dwavesys.com/leap/signup/) for the Leap quantum cloud service for free.
Those seeking a more collaborative approach and assistance with building a production application can
reach out to D-Wave [directly](https://www.dwavesys.com/solutions-and-products/professional-services/) and also explore the feature selection [offering](https://aws.amazon.com/marketplace/pp/prodview-bsrc3yuwgjbo4) in AWS Marketplace.
The package's main class, `SelectFromQuadraticModel`, can be used in any existing `sklearn` pipeline.
For an introduction to hybrid methods for feature selection, see the [Feature Selection for CQM](https://github.com/dwave-examples/feature-selection-cqm).
## Examples
A minimal example of using the plugin to select 20 of 30 features of an `sklearn` dataset:
```python
>>> from sklearn.datasets import load_breast_cancer
>>> from dwave.plugins.sklearn import SelectFromQuadraticModel
...
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFromQuadraticModel(num_features=20).fit_transform(X, y)
>>> X_new.shape
(569, 20)
```
For large problems, the default runtime may be insufficient. You can use the CQM solver's
[`min_time_limit`](https://docs.ocean.dwavesys.com/en/stable/docs_system/reference/generated/dwave.system.samplers.LeapHybridCQMSampler.min_time_limit.html)
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.
```python
>>> X_new = SelectFromQuadraticModel(num_features=20, time_limit=200).fit_transform(X, y)
```
## Installation
To install the core package:
```bash
pip install dwave-scikit-learn-plugin
```
## License
Released under the Apache License 2.0
## Contributing
Ocean's [contributing guide](https://docs.ocean.dwavesys.com/en/stable/contributing.html)
has guidelines for contributing to Ocean packages.
### Release Notes
**dwave-scikit-learn-plugin** makes use of [reno](https://docs.openstack.org/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
```bash
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.
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