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diff --git a/python-neptune-sklearn.spec b/python-neptune-sklearn.spec new file mode 100644 index 0000000..b0e5ee8 --- /dev/null +++ b/python-neptune-sklearn.spec @@ -0,0 +1,314 @@ +%global _empty_manifest_terminate_build 0 +Name: python-neptune-sklearn +Version: 2.1.0 +Release: 1 +Summary: Neptune.ai scikit-learn integration library +License: Apache-2.0 +URL: https://neptune.ai/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/eb/ce/8cd5c232fa15c62b6d15eee2560e046996c7031a4d433df900b96f3b14e3/neptune_sklearn-2.1.0.tar.gz +BuildArch: noarch + +Requires: python3-importlib-metadata +Requires: python3-neptune +Requires: python3-pre-commit +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-scikit-learn +Requires: python3-scikit-plot +Requires: python3-yellowbrick + +%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](https://docs.neptune.ai/logging/what_you_can_log) + + +*Confusion matrix logged to Neptune* + +## 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 support@neptune.ai + + + +%package -n python3-neptune-sklearn +Summary: Neptune.ai scikit-learn integration library +Provides: python-neptune-sklearn +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-neptune-sklearn +# 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](https://docs.neptune.ai/logging/what_you_can_log) + + +*Confusion matrix logged to Neptune* + +## 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 support@neptune.ai + + + +%package help +Summary: Development documents and examples for neptune-sklearn +Provides: python3-neptune-sklearn-doc +%description help +# 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](https://docs.neptune.ai/logging/what_you_can_log) + + +*Confusion matrix logged to Neptune* + +## 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 support@neptune.ai + + + +%prep +%autosetup -n neptune-sklearn-2.1.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-neptune-sklearn -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.0-1 +- Package Spec generated |