%global _empty_manifest_terminate_build 0
Name: python-whylogs
Version: 1.1.39
Release: 1
Summary: Profile and monitor your ML data pipeline end-to-end
License: Apache-2.0
URL: https://docs.whylabs.ai
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a0/d2/50c3c523d2cf5cc61c0fdb387f677b367cc37afb60f174a2345f7e0d0094/whylogs-1.1.39.tar.gz
BuildArch: noarch
Requires: python3-Pillow
Requires: python3-boto3
Requires: python3-fugue
Requires: python3-furo
Requires: python3-google-cloud-storage
Requires: python3-importlib-metadata
Requires: python3-ipython
Requires: python3-ipython_genutils
Requires: python3-mlflow-skinny
Requires: python3-myst-parser[sphinx]
Requires: python3-nbconvert
Requires: python3-nbsphinx
Requires: python3-numpy
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-protobuf
Requires: python3-pyarrow
Requires: python3-pybars3
Requires: python3-pyspark
Requires: python3-requests
Requires: python3-scikit-learn
Requires: python3-scikit-learn
Requires: python3-scipy
Requires: python3-scipy
Requires: python3-sphinx
Requires: python3-sphinx-autoapi
Requires: python3-sphinx-autobuild
Requires: python3-sphinx-copybutton
Requires: python3-sphinx-inline-tabs
Requires: python3-sphinxext-opengraph
Requires: python3-typing-extensions
Requires: python3-whylabs-client
Requires: python3-whylogs-sketching
%description
The open standard for data logging
## What is whylogs
whylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called _whylogs profiles_) which they can use to:
1. Track changes in their dataset
2. Create _data constraints_ to know whether their data looks the way it should
3. Quickly visualize key summary statistics about their datasets
These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:
- Detect data drift in model input features
- Detect training-serving skew, concept drift, and model performance degradation
- Validate data quality in model inputs or in a data pipeline
- Perform exploratory data analysis of massive datasets
- Track data distributions & data quality for ML experiments
- Enable data auditing and governance across the organization
- Standardize data documentation practices across the organization
- And more
## Quickstart
Install whylogs using the pip package manager in a terminal by running:
```
pip install whylogs
```
Then you can log data in python as simply as this:
```python
import whylogs as why
import pandas as pd
df = pd.read_csv("path/to/file.csv")
results = why.log(df)
```
And voilà, you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, check out our [docs](https://whylogs.readthedocs.io/en/latest/) and our [examples](https://github.com/whylabs/whylogs/tree/mainline/python/examples).
%package -n python3-whylogs
Summary: Profile and monitor your ML data pipeline end-to-end
Provides: python-whylogs
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-whylogs
The open standard for data logging
## What is whylogs
whylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called _whylogs profiles_) which they can use to:
1. Track changes in their dataset
2. Create _data constraints_ to know whether their data looks the way it should
3. Quickly visualize key summary statistics about their datasets
These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:
- Detect data drift in model input features
- Detect training-serving skew, concept drift, and model performance degradation
- Validate data quality in model inputs or in a data pipeline
- Perform exploratory data analysis of massive datasets
- Track data distributions & data quality for ML experiments
- Enable data auditing and governance across the organization
- Standardize data documentation practices across the organization
- And more
## Quickstart
Install whylogs using the pip package manager in a terminal by running:
```
pip install whylogs
```
Then you can log data in python as simply as this:
```python
import whylogs as why
import pandas as pd
df = pd.read_csv("path/to/file.csv")
results = why.log(df)
```
And voilà, you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, check out our [docs](https://whylogs.readthedocs.io/en/latest/) and our [examples](https://github.com/whylabs/whylogs/tree/mainline/python/examples).
%package help
Summary: Development documents and examples for whylogs
Provides: python3-whylogs-doc
%description help
The open standard for data logging
## What is whylogs
whylogs is an open source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called _whylogs profiles_) which they can use to:
1. Track changes in their dataset
2. Create _data constraints_ to know whether their data looks the way it should
3. Quickly visualize key summary statistics about their datasets
These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:
- Detect data drift in model input features
- Detect training-serving skew, concept drift, and model performance degradation
- Validate data quality in model inputs or in a data pipeline
- Perform exploratory data analysis of massive datasets
- Track data distributions & data quality for ML experiments
- Enable data auditing and governance across the organization
- Standardize data documentation practices across the organization
- And more
## Quickstart
Install whylogs using the pip package manager in a terminal by running:
```
pip install whylogs
```
Then you can log data in python as simply as this:
```python
import whylogs as why
import pandas as pd
df = pd.read_csv("path/to/file.csv")
results = why.log(df)
```
And voilà, you now have a whylogs profile. To learn more about what a whylogs profile is and what you can do with it, check out our [docs](https://whylogs.readthedocs.io/en/latest/) and our [examples](https://github.com/whylabs/whylogs/tree/mainline/python/examples).
%prep
%autosetup -n whylogs-1.1.39
%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-whylogs -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.1.39-1
- Package Spec generated