https://github.com/neptune-ai/neptune-sklearn

Experiment tracking for scikit-learn. 🧩 Log, organize, visualize and compare model metrics, parameters, dataset versions, and more.
https://github.com/neptune-ai/neptune-sklearn

dashboard logger management metadata mlops optimization organize scikit-learn sklearn store tracker training versioning

Last synced: 3 months ago
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Experiment tracking for scikit-learn. 🧩 Log, organize, visualize and compare model metrics, parameters, dataset versions, and more.

• Host: GitHub
• URL: https://github.com/neptune-ai/neptune-sklearn
• Owner: neptune-ai
• License: apache-2.0
• Created: 2021-04-29T07:36:36.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-07-05T13:47:15.000Z (6 months ago)
• Last Synced: 2024-09-28T13:23:11.067Z (3 months ago)
• Topics: dashboard, logger, management, metadata, mlops, optimization, organize, scikit-learn, sklearn, store, tracker, training, versioning
• Language: Python
• Homepage: https://docs.neptune.ai/integrations/sklearn/
• Size: 114 KB
• Stars: 6
• Watchers: 8
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# Neptune + scikit-learn integration

Experiment tracking for scikit-learn–trained models.

## What will you get with this integration?

* Log, organize, visualize, and compare ML experiments in a single place

* Monitor model training live

* Version and query production-ready models and associated metadata (e.g., datasets)

* Collaborate with the team and across the organization

## What will be logged to Neptune?

* classifier and regressor parameters,

* pickled model,

* test predictions,

* test predictions probabilities,

* test scores,

* classifier and regressor visualizations, like confusion matrix, precision-recall chart, and feature importance chart,

* KMeans cluster labels and clustering visualizations,

* metadata including git summary info,

* [other metadata](https://docs.neptune.ai/logging/what_you_can_log)

![image](https://docs.neptune.ai/img/app/integrations/scikit-learn.png)

## Resources

* [Documentation](https://docs.neptune.ai/integrations/sklearn)

* [Code example on GitHub](https://github.com/neptune-ai/examples/blob/main/integrations-and-supported-tools/sklearn/scripts/Neptune_Scikit_learn_classification.py)

* [Runs logged in the Neptune app](https://app.neptune.ai/o/common/org/sklearn-integration/e/SKLEAR-95/all)

* [Run example in Google Colab](https://colab.research.google.com/github/neptune-ai/examples/blob/master/integrations-and-supported-tools/sklearn/notebooks/Neptune_Scikit_learn.ipynb)

## Example

```

# On the command line:

pip install neptune-sklearn

```

```python

# In Python, prepare a fitted estimator

parameters = {

    "n_estimators": 70, "max_depth": 7, "min_samples_split": 3

}

estimator = ...

estimator.fit(X_train, y_train)

# Import Neptune and start a run

import neptune

run = neptune.init_run(

    project="common/sklearn-integration",

    api_token=neptune.ANONYMOUS_API_TOKEN,

)

# Log parameters and scores

run["parameters"] = parameters

y_pred = estimator.predict(X_test)

run["scores/max_error"] = max_error(y_test, y_pred)

run["scores/mean_absolute_error"] = mean_absolute_error(y_test, y_pred)

run["scores/r2_score"] = r2_score(y_test, y_pred)

# Stop the run

run.stop()

```

## Support

If you got stuck or simply want to talk to us, here are your options:

* Check our [FAQ page](https://docs.neptune.ai/getting_help)

* You can submit bug reports, feature requests, or contributions directly to the repository.

* Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),

* You can just shoot us an email at [email protected]