%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.aliyun.com/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) ![image](https://user-images.githubusercontent.com/97611089/160642485-afca99da-9f7b-4d80-b0be-810c9d5770e5.png) *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) ![image](https://user-images.githubusercontent.com/97611089/160642485-afca99da-9f7b-4d80-b0be-810c9d5770e5.png) *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) ![image](https://user-images.githubusercontent.com/97611089/160642485-afca99da-9f7b-4d80-b0be-810c9d5770e5.png) *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 * Thu Jun 08 2023 Python_Bot - 2.1.0-1 - Package Spec generated