From 93db4f68a5b3a2d95b00b8eece7f0e0386e4f4ea Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 11 Apr 2023 20:57:07 +0000 Subject: automatic import of python-composeml --- .gitignore | 1 + python-composeml.spec | 799 ++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 801 insertions(+) create mode 100644 python-composeml.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..47bf111 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/composeml-0.10.1.tar.gz diff --git a/python-composeml.spec b/python-composeml.spec new file mode 100644 index 0000000..133139e --- /dev/null +++ b/python-composeml.spec @@ -0,0 +1,799 @@ +%global _empty_manifest_terminate_build 0 +Name: python-composeml +Version: 0.10.1 +Release: 1 +Summary: a framework for automated prediction engineering +License: BSD 3-clause +URL: https://pypi.org/project/composeml/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/98/d7/70264fb178f79f6c7b1981cf6780dc0a2feb4ed76bdea771882b38b971f7/composeml-0.10.1.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-tqdm +Requires: python3-matplotlib +Requires: python3-seaborn +Requires: python3-composeml[updater] +Requires: python3-codecov +Requires: python3-flake8 +Requires: python3-isort +Requires: python3-black +Requires: python3-nbsphinx +Requires: python3-pydata-sphinx-theme +Requires: python3-Sphinx +Requires: python3-sphinx-inline-tabs +Requires: python3-sphinx-copybutton +Requires: python3-myst-parser +Requires: python3-nbconvert +Requires: python3-ipython +Requires: python3-pygments +Requires: python3-jupyter +Requires: python3-pandoc +Requires: python3-ipykernel +Requires: python3-scikit-learn +Requires: python3-evalml +Requires: python3-pip +Requires: python3-pytest-cov +Requires: python3-pytest-xdist +Requires: python3-wheel +Requires: python3-featuretools +Requires: python3-woodwork +Requires: python3-pyarrow +Requires: python3-alteryx-open-src-update-checker + +%description +

Compose

+

"Build better training examples in a fraction of the time."

+

+ + Tests + + + + + + ReadTheDocs + + + PyPI Version + + + StackOverflow + + + PyPI Downloads + +

+
+ +[Compose](https://compose.alteryx.com) is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a *labeling function*, then runs a search to automatically extract training examples from historical data. Its result is then provided to [Featuretools](https://docs.featuretools.com/) for automated feature engineering and subsequently to [EvalML](https://evalml.alteryx.com/) for automated machine learning. The workflow of an applied machine learning engineer then becomes: + +

Compose


+ +By automating the early stage of the machine learning pipeline, our end user can easily define a task and solve it. See the [documentation](https://compose.alteryx.com) for more information. + +## Installation +Install with pip + +``` +python -m pip install composeml +``` + +or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/composeml): + +``` +conda install -c conda-forge composeml +``` + +### Add-ons + +**Update checker** - Receive automatic notifications of new Compose releases + +``` +python -m pip install "composeml[update_checker]" +``` + +## Example +> Will a customer spend more than 300 in the next hour of transactions? + +In this example, we automatically generate new training examples from a historical dataset of transactions. + +```python +import composeml as cp +df = cp.demos.load_transactions() +df = df[df.columns[:7]] +df.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
transaction_idsession_idtransaction_timeproduct_idamountcustomer_iddevice
29812014-01-01 00:00:005127.642desktop
1012014-01-01 00:09:45557.392desktop
49512014-01-01 00:14:05569.452desktop
460102014-01-01 02:33:505123.192tablet
302102014-01-01 02:37:05564.472tablet
+ +First, we represent the prediction problem with a labeling function and a label maker. + +```python +def total_spent(ds): + return ds['amount'].sum() + +label_maker = cp.LabelMaker( + target_dataframe_index="customer_id", + time_index="transaction_time", + labeling_function=total_spent, + window_size="1h", +) +``` + +Then, we run a search to automatically generate the training examples. + +```python +label_times = label_maker.search( + df.sort_values('transaction_time'), + num_examples_per_instance=2, + minimum_data='2014-01-01', + drop_empty=False, + verbose=False, +) + +label_times = label_times.threshold(300) +label_times.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
customer_idtimetotal_spent
12014-01-01 00:00:00True
12014-01-01 01:00:00True
22014-01-01 00:00:00False
22014-01-01 01:00:00False
32014-01-01 00:00:00False
+ +We now have labels that are ready to use in [Featuretools](https://docs.featuretools.com/) to generate features. + +## Support + +The Innovation Labs open source community is happy to provide support to users of Compose. Project support can be found in three places depending on the type of question: + +1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/compose-ml) with the `composeml` tag. +2. For bugs, issues, or feature requests start a Github [issue](https://github.com/alteryx/compose/issues/new). +3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). +4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com + +## Citing Compose +Compose is built upon a newly defined part of the machine learning process — prediction engineering. If you use Compose, please consider citing this paper: +James Max Kanter, Gillespie, Owen, Kalyan Veeramachaneni. [Label, Segment,Featurize: a cross domain framework for prediction engineering.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/Pred_eng1.pdf) IEEE DSAA 2016. + +BibTeX entry: + +```bibtex +@inproceedings{kanter2016label, + title={Label, segment, featurize: a cross domain framework for prediction engineering}, + author={Kanter, James Max and Gillespie, Owen and Veeramachaneni, Kalyan}, + booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, + pages={430--439}, + year={2016}, + organization={IEEE} +} +``` + +## Acknowledgements + +The open source development has been supported in part by DARPA's Data driven discovery of models program (D3M). + +## Alteryx + +**Compose** is an open source project maintained by [Alteryx](https://www.alteryx.com). We developed Compose to enable flexible definition of the machine learning task. To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch. + +

+ + Alteryx Open Source + +

+ + +%package -n python3-composeml +Summary: a framework for automated prediction engineering +Provides: python-composeml +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-composeml +

Compose

+

"Build better training examples in a fraction of the time."

+

+ + Tests + + + + + + ReadTheDocs + + + PyPI Version + + + StackOverflow + + + PyPI Downloads + +

+
+ +[Compose](https://compose.alteryx.com) is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a *labeling function*, then runs a search to automatically extract training examples from historical data. Its result is then provided to [Featuretools](https://docs.featuretools.com/) for automated feature engineering and subsequently to [EvalML](https://evalml.alteryx.com/) for automated machine learning. The workflow of an applied machine learning engineer then becomes: + +

Compose


+ +By automating the early stage of the machine learning pipeline, our end user can easily define a task and solve it. See the [documentation](https://compose.alteryx.com) for more information. + +## Installation +Install with pip + +``` +python -m pip install composeml +``` + +or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/composeml): + +``` +conda install -c conda-forge composeml +``` + +### Add-ons + +**Update checker** - Receive automatic notifications of new Compose releases + +``` +python -m pip install "composeml[update_checker]" +``` + +## Example +> Will a customer spend more than 300 in the next hour of transactions? + +In this example, we automatically generate new training examples from a historical dataset of transactions. + +```python +import composeml as cp +df = cp.demos.load_transactions() +df = df[df.columns[:7]] +df.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
transaction_idsession_idtransaction_timeproduct_idamountcustomer_iddevice
29812014-01-01 00:00:005127.642desktop
1012014-01-01 00:09:45557.392desktop
49512014-01-01 00:14:05569.452desktop
460102014-01-01 02:33:505123.192tablet
302102014-01-01 02:37:05564.472tablet
+ +First, we represent the prediction problem with a labeling function and a label maker. + +```python +def total_spent(ds): + return ds['amount'].sum() + +label_maker = cp.LabelMaker( + target_dataframe_index="customer_id", + time_index="transaction_time", + labeling_function=total_spent, + window_size="1h", +) +``` + +Then, we run a search to automatically generate the training examples. + +```python +label_times = label_maker.search( + df.sort_values('transaction_time'), + num_examples_per_instance=2, + minimum_data='2014-01-01', + drop_empty=False, + verbose=False, +) + +label_times = label_times.threshold(300) +label_times.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
customer_idtimetotal_spent
12014-01-01 00:00:00True
12014-01-01 01:00:00True
22014-01-01 00:00:00False
22014-01-01 01:00:00False
32014-01-01 00:00:00False
+ +We now have labels that are ready to use in [Featuretools](https://docs.featuretools.com/) to generate features. + +## Support + +The Innovation Labs open source community is happy to provide support to users of Compose. Project support can be found in three places depending on the type of question: + +1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/compose-ml) with the `composeml` tag. +2. For bugs, issues, or feature requests start a Github [issue](https://github.com/alteryx/compose/issues/new). +3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). +4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com + +## Citing Compose +Compose is built upon a newly defined part of the machine learning process — prediction engineering. If you use Compose, please consider citing this paper: +James Max Kanter, Gillespie, Owen, Kalyan Veeramachaneni. [Label, Segment,Featurize: a cross domain framework for prediction engineering.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/Pred_eng1.pdf) IEEE DSAA 2016. + +BibTeX entry: + +```bibtex +@inproceedings{kanter2016label, + title={Label, segment, featurize: a cross domain framework for prediction engineering}, + author={Kanter, James Max and Gillespie, Owen and Veeramachaneni, Kalyan}, + booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, + pages={430--439}, + year={2016}, + organization={IEEE} +} +``` + +## Acknowledgements + +The open source development has been supported in part by DARPA's Data driven discovery of models program (D3M). + +## Alteryx + +**Compose** is an open source project maintained by [Alteryx](https://www.alteryx.com). We developed Compose to enable flexible definition of the machine learning task. To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch. + +

+ + Alteryx Open Source + +

+ + +%package help +Summary: Development documents and examples for composeml +Provides: python3-composeml-doc +%description help +

Compose

+

"Build better training examples in a fraction of the time."

+

+ + Tests + + + + + + ReadTheDocs + + + PyPI Version + + + StackOverflow + + + PyPI Downloads + +

+
+ +[Compose](https://compose.alteryx.com) is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a *labeling function*, then runs a search to automatically extract training examples from historical data. Its result is then provided to [Featuretools](https://docs.featuretools.com/) for automated feature engineering and subsequently to [EvalML](https://evalml.alteryx.com/) for automated machine learning. The workflow of an applied machine learning engineer then becomes: + +

Compose


+ +By automating the early stage of the machine learning pipeline, our end user can easily define a task and solve it. See the [documentation](https://compose.alteryx.com) for more information. + +## Installation +Install with pip + +``` +python -m pip install composeml +``` + +or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/composeml): + +``` +conda install -c conda-forge composeml +``` + +### Add-ons + +**Update checker** - Receive automatic notifications of new Compose releases + +``` +python -m pip install "composeml[update_checker]" +``` + +## Example +> Will a customer spend more than 300 in the next hour of transactions? + +In this example, we automatically generate new training examples from a historical dataset of transactions. + +```python +import composeml as cp +df = cp.demos.load_transactions() +df = df[df.columns[:7]] +df.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
transaction_idsession_idtransaction_timeproduct_idamountcustomer_iddevice
29812014-01-01 00:00:005127.642desktop
1012014-01-01 00:09:45557.392desktop
49512014-01-01 00:14:05569.452desktop
460102014-01-01 02:33:505123.192tablet
302102014-01-01 02:37:05564.472tablet
+ +First, we represent the prediction problem with a labeling function and a label maker. + +```python +def total_spent(ds): + return ds['amount'].sum() + +label_maker = cp.LabelMaker( + target_dataframe_index="customer_id", + time_index="transaction_time", + labeling_function=total_spent, + window_size="1h", +) +``` + +Then, we run a search to automatically generate the training examples. + +```python +label_times = label_maker.search( + df.sort_values('transaction_time'), + num_examples_per_instance=2, + minimum_data='2014-01-01', + drop_empty=False, + verbose=False, +) + +label_times = label_times.threshold(300) +label_times.head() +``` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
customer_idtimetotal_spent
12014-01-01 00:00:00True
12014-01-01 01:00:00True
22014-01-01 00:00:00False
22014-01-01 01:00:00False
32014-01-01 00:00:00False
+ +We now have labels that are ready to use in [Featuretools](https://docs.featuretools.com/) to generate features. + +## Support + +The Innovation Labs open source community is happy to provide support to users of Compose. Project support can be found in three places depending on the type of question: + +1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/compose-ml) with the `composeml` tag. +2. For bugs, issues, or feature requests start a Github [issue](https://github.com/alteryx/compose/issues/new). +3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). +4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com + +## Citing Compose +Compose is built upon a newly defined part of the machine learning process — prediction engineering. If you use Compose, please consider citing this paper: +James Max Kanter, Gillespie, Owen, Kalyan Veeramachaneni. [Label, Segment,Featurize: a cross domain framework for prediction engineering.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/Pred_eng1.pdf) IEEE DSAA 2016. + +BibTeX entry: + +```bibtex +@inproceedings{kanter2016label, + title={Label, segment, featurize: a cross domain framework for prediction engineering}, + author={Kanter, James Max and Gillespie, Owen and Veeramachaneni, Kalyan}, + booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, + pages={430--439}, + year={2016}, + organization={IEEE} +} +``` + +## Acknowledgements + +The open source development has been supported in part by DARPA's Data driven discovery of models program (D3M). + +## Alteryx + +**Compose** is an open source project maintained by [Alteryx](https://www.alteryx.com). We developed Compose to enable flexible definition of the machine learning task. To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch. + +

+ + Alteryx Open Source + +

+ + +%prep +%autosetup -n composeml-0.10.1 + +%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-composeml -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot - 0.10.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..8ede525 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +acb86c6efe955e5a43dcfed52a5d6278 composeml-0.10.1.tar.gz -- cgit v1.2.3