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|
%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)
*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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.0-1
- Package Spec generated
|
