[](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
### Basic Usage
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)
>>> # solver can also be equal to "cqm"
>>> X_new = SelectFromQuadraticModel(num_features=20, solver="nl").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 [`time_limit`](https://docs.dwavequantum.com/en/latest/industrial_optimization/solver_cqm_parameters.html#time-limit) or Nonlinear (NL) solver's
[`time_limit`](https://docs.dwavequantum.com/en/latest/industrial_optimization/solver_nl_parameters.html#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.
```python
>>> # solver can also be equal to "nl"
>>> X_new = SelectFromQuadraticModel(num_features=20, time_limit=200, solver="cqm").fit_transform(X, y)
```
### Tuning
You can use `SelectFromQuadraticModel` with scikit-learn's
[hyper-parameter optimizers](https://scikit-learn.org/stable/modules/classes.html#hyper-parameter-optimizers).
For example, the number of features can be tuned using a grid search. **Please note that this will
submit many problems to the hybrid solver.**
```python
>>> import numpy as np
...
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.model_selection import GridSearchCV
>>> from sklearn.pipeline import Pipeline
>>> from dwave.plugins.sklearn import SelectFromQuadraticModel
...
>>> X, y = load_breast_cancer(return_X_y=True)
...
>>> num_features = X.shape[1]
>>> searchspace = np.linspace(1, num_features, num=5, dtype=int, endpoint=True)
...
>>> # solver can also be equal to "cqm"
>>> pipe = Pipeline([
>>> ('feature_selection', SelectFromQuadraticModel(solver="nl")),
>>> ('classification', RandomForestClassifier())
>>> ])
...
>>> clf = GridSearchCV(pipe, param_grid=dict(feature_selection__num_features=searchspace))
>>> search = clf.fit(X, y)
>>> print(search.best_params_)
{'feature_selection__num_features': 22}
```
## 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.
Raw data
{
"_id": null,
"home_page": null,
"name": "dwave-scikit-learn-plugin",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": null,
"author": null,
"author_email": "\"D-Wave Inc.\" <tools@dwavesys.com>",
"download_url": "https://files.pythonhosted.org/packages/04/08/4eb091c429b4ecdf92b7b435df209938a70c9aecda1d86e0bc216422d3b6/dwave_scikit_learn_plugin-0.2.0.tar.gz",
"platform": null,
"description": "[](https://pypi.python.org/pypi/dwave-scikit-learn-plugin)\n[](https://dl.circleci.com/status-badge/redirect/gh/dwavesystems/dwave-scikit-learn-plugin)\n\n# D-Wave `scikit-learn` Plugin\n\nThis package provides a [scikit-learn](https://scikit-learn.org/) transformer for \n[feature selection](https://en.wikipedia.org/wiki/Feature_selection) using a\nquantum-classical [hybrid solver](https://docs.ocean.dwavesys.com/en/stable/concepts/hybrid.html).\n\nThis plugin makes use of a Leap\u2122 quantum-classical hybrid solver. Developers can get started by\n[signing up](https://cloud.dwavesys.com/leap/signup/) for the Leap quantum cloud service for free.\nThose seeking a more collaborative approach and assistance with building a production application can\nreach 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.\n\nThe package's main class, `SelectFromQuadraticModel`, can be used in any existing `sklearn` pipeline.\nFor an introduction to hybrid methods for feature selection, see the [Feature Selection for CQM](https://github.com/dwave-examples/feature-selection-cqm).\n\n## Examples\n\n### Basic Usage\n\nA minimal example of using the plugin to select 20 of 30 features of an `sklearn` dataset: \n\n```python\n>>> from sklearn.datasets import load_breast_cancer\n>>> from dwave.plugins.sklearn import SelectFromQuadraticModel\n... \n>>> X, y = load_breast_cancer(return_X_y=True)\n>>> X.shape\n(569, 30)\n>>> # solver can also be equal to \"cqm\"\n>>> X_new = SelectFromQuadraticModel(num_features=20, solver=\"nl\").fit_transform(X, y)\n>>> X_new.shape\n(569, 20)\n```\n\nFor large problems, the default runtime may be insufficient. You can use the CQM solver's [`time_limit`](https://docs.dwavequantum.com/en/latest/industrial_optimization/solver_cqm_parameters.html#time-limit) or Nonlinear (NL) solver's\n[`time_limit`](https://docs.dwavequantum.com/en/latest/industrial_optimization/solver_nl_parameters.html#time-limit)\nmethod to find the minimum accepted runtime for your problem; alternatively, simply submit as above \nand check the returned error message for the required runtime. \n\nThe feature selector can be re-instantiated with a longer time limit.\n\n```python\n>>> # solver can also be equal to \"nl\"\n>>> X_new = SelectFromQuadraticModel(num_features=20, time_limit=200, solver=\"cqm\").fit_transform(X, y)\n```\n\n### Tuning\n\nYou can use `SelectFromQuadraticModel` with scikit-learn's\n[hyper-parameter optimizers](https://scikit-learn.org/stable/modules/classes.html#hyper-parameter-optimizers).\n\nFor example, the number of features can be tuned using a grid search. **Please note that this will\nsubmit many problems to the hybrid solver.**\n\n```python\n>>> import numpy as np\n...\n>>> from sklearn.datasets import load_breast_cancer\n>>> from sklearn.ensemble import RandomForestClassifier\n>>> from sklearn.model_selection import GridSearchCV\n>>> from sklearn.pipeline import Pipeline\n>>> from dwave.plugins.sklearn import SelectFromQuadraticModel\n...\n>>> X, y = load_breast_cancer(return_X_y=True)\n...\n>>> num_features = X.shape[1]\n>>> searchspace = np.linspace(1, num_features, num=5, dtype=int, endpoint=True)\n...\n>>> # solver can also be equal to \"cqm\"\n>>> pipe = Pipeline([\n>>> ('feature_selection', SelectFromQuadraticModel(solver=\"nl\")),\n>>> ('classification', RandomForestClassifier())\n>>> ])\n...\n>>> clf = GridSearchCV(pipe, param_grid=dict(feature_selection__num_features=searchspace))\n>>> search = clf.fit(X, y)\n>>> print(search.best_params_)\n{'feature_selection__num_features': 22}\n```\n\n## Installation\n\nTo install the core package:\n\n```bash\npip install dwave-scikit-learn-plugin\n```\n\n## License\n\nReleased under the Apache License 2.0\n\n## Contributing\n\nOcean's [contributing guide](https://docs.ocean.dwavesys.com/en/stable/contributing.html)\nhas guidelines for contributing to Ocean packages.\n\n### Release Notes\n\n**dwave-scikit-learn-plugin** makes use of [reno](https://docs.openstack.org/reno/) to manage its\nrelease notes.\n\nWhen making a contribution to **dwave-scikit-learn-plugin** that will affect users, create a new\nrelease note file by running\n\n```bash\nreno new your-short-descriptor-here\n```\n\nYou can then edit the file created under ``releasenotes/notes/``.\nRemove any sections not relevant to your changes.\nCommit the file along with your changes.\n",
"bugtrack_url": null,
"license": null,
"summary": "A plugin to scikit-learn for quantum-classical hybrid solving.",
"version": "0.2.0",
"project_urls": {
"Download": "https://github.com/dwavesystems/dwave-scikit-learn-plugin/releases",
"Homepage": "https://github.com/dwavesystems/dwave-scikit-learn-plugin"
},
"split_keywords": [],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "910f7518d9973ebcf1c50fe56ba2ece1b1937566a39f2ee9ff5b1a0b99b4e08f",
"md5": "158beb842e6237511c9ed2347c33749e",
"sha256": "5b63e2c0aa8e1940c9ce56bbaf259dd2b1fa46b8b82fd5448175a0942ade9832"
},
"downloads": -1,
"filename": "dwave_scikit_learn_plugin-0.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "158beb842e6237511c9ed2347c33749e",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 16643,
"upload_time": "2025-08-15T18:15:45",
"upload_time_iso_8601": "2025-08-15T18:15:45.611072Z",
"url": "https://files.pythonhosted.org/packages/91/0f/7518d9973ebcf1c50fe56ba2ece1b1937566a39f2ee9ff5b1a0b99b4e08f/dwave_scikit_learn_plugin-0.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "04084eb091c429b4ecdf92b7b435df209938a70c9aecda1d86e0bc216422d3b6",
"md5": "0e0ec5c0a5f77f0c6efffb441c57598f",
"sha256": "1976acb97c740df46e50d2452ed1a04ded892c8bec77ad18901b69ec6117d869"
},
"downloads": -1,
"filename": "dwave_scikit_learn_plugin-0.2.0.tar.gz",
"has_sig": false,
"md5_digest": "0e0ec5c0a5f77f0c6efffb441c57598f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 20575,
"upload_time": "2025-08-15T18:15:46",
"upload_time_iso_8601": "2025-08-15T18:15:46.420812Z",
"url": "https://files.pythonhosted.org/packages/04/08/4eb091c429b4ecdf92b7b435df209938a70c9aecda1d86e0bc216422d3b6/dwave_scikit_learn_plugin-0.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-15 18:15:46",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "dwavesystems",
"github_project": "dwave-scikit-learn-plugin",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"circle": true,
"requirements": [
{
"name": "dimod",
"specs": [
[
"==",
"0.12.20"
]
]
},
{
"name": "dwave-optimization",
"specs": [
[
"==",
"0.6.2"
]
]
},
{
"name": "dwave-system",
"specs": [
[
"==",
"1.32.0"
]
]
},
{
"name": "numpy",
"specs": [
[
"==",
"2.0.2"
]
]
},
{
"name": "scikit-learn",
"specs": [
[
"==",
"1.6.1"
]
]
},
{
"name": "reno",
"specs": [
[
"==",
"3.5.0"
]
]
}
],
"lcname": "dwave-scikit-learn-plugin"
}
JSON
