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-rw-r--r--python-featuretools-tsfresh-primitives.spec378
-rw-r--r--sources1
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diff --git a/.gitignore b/.gitignore
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+/featuretools_tsfresh_primitives-1.0.2.tar.gz
diff --git a/python-featuretools-tsfresh-primitives.spec b/python-featuretools-tsfresh-primitives.spec
new file mode 100644
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--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name: python-featuretools-tsfresh-primitives
+Version: 1.0.2
+Release: 1
+Summary: TSFresh primitives for featuretools
+License: MIT
+URL: https://github.com/alteryx/featuretools-tsfresh-primitives/
+Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8e/54/9489bcfb8390a99e89c83fc8663966ae28a5d934d9e25868cb2fccdd9c35/featuretools_tsfresh_primitives-1.0.2.tar.gz
+BuildArch: noarch
+
+Requires: python3-tsfresh
+Requires: python3-pandas
+Requires: python3-featuretools
+
+%description
+# TSFresh Primitives
+
+<p align="center">
+ <a href="https://github.com/alteryx/featuretools-tsfresh-primitives/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
+ <img src="https://github.com/alteryx/featuretools-tsfresh-primitives/workflows/Tests/badge.svg?branch=main" alt="Tests" />
+ </a>
+ <a href="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives">
+ <img src="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives/branch/main/graph/badge.svg?token=FtcPOJLpjj"/>
+ </a>
+ <a href="https://badge.fury.io/py/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://badge.fury.io/py/featuretools-tsfresh-primitives.svg?maxAge=2592000" alt="PyPI Version" />
+ </a>
+ <a href="https://pepy.tech/project/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://pepy.tech/badge/featuretools-tsfresh-primitives/month" alt="PyPI Downloads" />
+ </a>
+</p>
+<hr>
+
+### Installation
+
+Install with pip:
+
+```python
+python -m pip install "featuretools[tsfresh]"
+```
+
+## Calculating Features
+
+In `tsfresh`, this is how you can calculate a feature.
+
+```python
+from tsfresh.feature_extraction.feature_calculators import agg_autocorrelation
+
+data = list(range(10))
+param = [{'f_agg': 'mean', 'maxlag': 5}]
+agg_autocorrelation(data, param=param)
+```
+
+```bash
+[('f_agg_"mean"__maxlag_5', 0.1717171717171717)]
+```
+
+With tsfresh primtives in `featuretools`, this is how you can calculate the same feature.
+```python
+from featuretools.tsfresh import AggAutocorrelation
+
+data = list(range(10))
+AggAutocorrelation(f_agg='mean', maxlag=5)(data)
+```
+
+```bash
+0.1717171717171717
+```
+
+## Combining Primitives
+
+In `featuretools`, this is how to combine tsfresh primitives with built-in or other installed primitives.
+```python
+import featuretools as ft
+from featuretools.tsfresh import AggAutocorrelation, Mean
+
+entityset = ft.demo.load_mock_customer(return_entityset=True)
+agg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]
+feature_matrix, features = ft.dfs(entityset=entityset, target_dataframe_name='sessions', agg_primitives=agg_primitives)
+
+feature_matrix[[
+ 'MEAN(transactions.amount)',
+ 'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',
+]].head()
+```
+```
+ MEAN(transactions.amount) AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 76.813125 0.044268
+2 74.696000 -0.053110
+3 88.600000 0.007520
+4 64.557200 -0.034542
+5 70.638182 -0.100571
+```
+Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.
+
+```python
+feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()
+```
+```
+ customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 0.011102
+2 -0.001686
+3 -0.010679
+4 0.011204
+5 -0.010679
+```
+
+## Built at Alteryx Innovation Labs
+
+<a href="https://www.alteryx.com/innovation-labs">
+ <img src="https://evalml-web-images.s3.amazonaws.com/alteryx_innovation_labs.png" alt="Alteryx Innovation Labs" />
+</a>
+
+
+
+
+%package -n python3-featuretools-tsfresh-primitives
+Summary: TSFresh primitives for featuretools
+Provides: python-featuretools-tsfresh-primitives
+BuildRequires: python3-devel
+BuildRequires: python3-setuptools
+BuildRequires: python3-pip
+%description -n python3-featuretools-tsfresh-primitives
+# TSFresh Primitives
+
+<p align="center">
+ <a href="https://github.com/alteryx/featuretools-tsfresh-primitives/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
+ <img src="https://github.com/alteryx/featuretools-tsfresh-primitives/workflows/Tests/badge.svg?branch=main" alt="Tests" />
+ </a>
+ <a href="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives">
+ <img src="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives/branch/main/graph/badge.svg?token=FtcPOJLpjj"/>
+ </a>
+ <a href="https://badge.fury.io/py/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://badge.fury.io/py/featuretools-tsfresh-primitives.svg?maxAge=2592000" alt="PyPI Version" />
+ </a>
+ <a href="https://pepy.tech/project/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://pepy.tech/badge/featuretools-tsfresh-primitives/month" alt="PyPI Downloads" />
+ </a>
+</p>
+<hr>
+
+### Installation
+
+Install with pip:
+
+```python
+python -m pip install "featuretools[tsfresh]"
+```
+
+## Calculating Features
+
+In `tsfresh`, this is how you can calculate a feature.
+
+```python
+from tsfresh.feature_extraction.feature_calculators import agg_autocorrelation
+
+data = list(range(10))
+param = [{'f_agg': 'mean', 'maxlag': 5}]
+agg_autocorrelation(data, param=param)
+```
+
+```bash
+[('f_agg_"mean"__maxlag_5', 0.1717171717171717)]
+```
+
+With tsfresh primtives in `featuretools`, this is how you can calculate the same feature.
+```python
+from featuretools.tsfresh import AggAutocorrelation
+
+data = list(range(10))
+AggAutocorrelation(f_agg='mean', maxlag=5)(data)
+```
+
+```bash
+0.1717171717171717
+```
+
+## Combining Primitives
+
+In `featuretools`, this is how to combine tsfresh primitives with built-in or other installed primitives.
+```python
+import featuretools as ft
+from featuretools.tsfresh import AggAutocorrelation, Mean
+
+entityset = ft.demo.load_mock_customer(return_entityset=True)
+agg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]
+feature_matrix, features = ft.dfs(entityset=entityset, target_dataframe_name='sessions', agg_primitives=agg_primitives)
+
+feature_matrix[[
+ 'MEAN(transactions.amount)',
+ 'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',
+]].head()
+```
+```
+ MEAN(transactions.amount) AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 76.813125 0.044268
+2 74.696000 -0.053110
+3 88.600000 0.007520
+4 64.557200 -0.034542
+5 70.638182 -0.100571
+```
+Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.
+
+```python
+feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()
+```
+```
+ customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 0.011102
+2 -0.001686
+3 -0.010679
+4 0.011204
+5 -0.010679
+```
+
+## Built at Alteryx Innovation Labs
+
+<a href="https://www.alteryx.com/innovation-labs">
+ <img src="https://evalml-web-images.s3.amazonaws.com/alteryx_innovation_labs.png" alt="Alteryx Innovation Labs" />
+</a>
+
+
+
+
+%package help
+Summary: Development documents and examples for featuretools-tsfresh-primitives
+Provides: python3-featuretools-tsfresh-primitives-doc
+%description help
+# TSFresh Primitives
+
+<p align="center">
+ <a href="https://github.com/alteryx/featuretools-tsfresh-primitives/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
+ <img src="https://github.com/alteryx/featuretools-tsfresh-primitives/workflows/Tests/badge.svg?branch=main" alt="Tests" />
+ </a>
+ <a href="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives">
+ <img src="https://codecov.io/gh/alteryx/featuretools-tsfresh-primitives/branch/main/graph/badge.svg?token=FtcPOJLpjj"/>
+ </a>
+ <a href="https://badge.fury.io/py/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://badge.fury.io/py/featuretools-tsfresh-primitives.svg?maxAge=2592000" alt="PyPI Version" />
+ </a>
+ <a href="https://pepy.tech/project/featuretools-tsfresh-primitives" target="_blank">
+ <img src="https://pepy.tech/badge/featuretools-tsfresh-primitives/month" alt="PyPI Downloads" />
+ </a>
+</p>
+<hr>
+
+### Installation
+
+Install with pip:
+
+```python
+python -m pip install "featuretools[tsfresh]"
+```
+
+## Calculating Features
+
+In `tsfresh`, this is how you can calculate a feature.
+
+```python
+from tsfresh.feature_extraction.feature_calculators import agg_autocorrelation
+
+data = list(range(10))
+param = [{'f_agg': 'mean', 'maxlag': 5}]
+agg_autocorrelation(data, param=param)
+```
+
+```bash
+[('f_agg_"mean"__maxlag_5', 0.1717171717171717)]
+```
+
+With tsfresh primtives in `featuretools`, this is how you can calculate the same feature.
+```python
+from featuretools.tsfresh import AggAutocorrelation
+
+data = list(range(10))
+AggAutocorrelation(f_agg='mean', maxlag=5)(data)
+```
+
+```bash
+0.1717171717171717
+```
+
+## Combining Primitives
+
+In `featuretools`, this is how to combine tsfresh primitives with built-in or other installed primitives.
+```python
+import featuretools as ft
+from featuretools.tsfresh import AggAutocorrelation, Mean
+
+entityset = ft.demo.load_mock_customer(return_entityset=True)
+agg_primitives = [Mean, AggAutocorrelation(f_agg='mean', maxlag=5)]
+feature_matrix, features = ft.dfs(entityset=entityset, target_dataframe_name='sessions', agg_primitives=agg_primitives)
+
+feature_matrix[[
+ 'MEAN(transactions.amount)',
+ 'AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)',
+]].head()
+```
+```
+ MEAN(transactions.amount) AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 76.813125 0.044268
+2 74.696000 -0.053110
+3 88.600000 0.007520
+4 64.557200 -0.034542
+5 70.638182 -0.100571
+```
+Notice that tsfresh primtives are applied across relationships in an entityset generating many features that are otherwise not possible.
+
+```python
+feature_matrix[['customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)']].head()
+```
+```
+ customers.AGG_AUTOCORRELATION(transactions.amount, f_agg=mean, maxlag=5)
+session_id
+1 0.011102
+2 -0.001686
+3 -0.010679
+4 0.011204
+5 -0.010679
+```
+
+## Built at Alteryx Innovation Labs
+
+<a href="https://www.alteryx.com/innovation-labs">
+ <img src="https://evalml-web-images.s3.amazonaws.com/alteryx_innovation_labs.png" alt="Alteryx Innovation Labs" />
+</a>
+
+
+
+
+%prep
+%autosetup -n featuretools-tsfresh-primitives-1.0.2
+
+%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-featuretools-tsfresh-primitives -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..17b57da
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f659ae11749e184fec62ba97b2d5c6c7 featuretools_tsfresh_primitives-1.0.2.tar.gz
generated by cgit v1.2.3 (git 2.25.1) at 2025-06-25 18:53:43 +0000