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Neptune.ai scikit-learn integration library

Project description

Neptune + Scikit-learn Integration

Experiment tracking, model registry, data versioning, and live model monitoring for Scikit-learn (Sklearn) trained models.

What will you get with this integration?

  • Log, display, organize, and compare ML experiments in a single place
  • Version, store, manage, and query trained models, and model building metadata
  • Record and monitor model training, evaluation, or production runs live

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

image Confusion matrix logged to Neptune

Resources

Example

# On the command line:
pip install scikit-learn neptune-client neptune-sklearn
# 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.new as neptune
run = neptune.init(project="common/sklearn-integration",
                   api_token="ANONYMOUS")


# 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
  • 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 support@neptune.ai

Project details


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Source Distribution

neptune-sklearn-0.9.6.tar.gz (26.6 kB view hashes)

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