automatic import of python-composeml

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+/composeml-0.10.1.tar.gz
diff --git a/python-composeml.spec b/python-composeml.spec
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+%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
+<p align="center"><img width=50% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/compose.png" alt="Compose" /></p>
+<p align="center"><i>"Build better training examples in a fraction of the time."</i></p>
+<p align="center">
+    <a href="https://github.com/alteryx/compose/actions?query=workflow%3ATests" target="_blank">
+        <img src="https://github.com/alteryx/compose/workflows/Tests/badge.svg" alt="Tests" />
+    </a>
+    <a href="https://codecov.io/gh/alteryx/compose">
+        <img src="https://codecov.io/gh/alteryx/compose/branch/main/graph/badge.svg?token=mDz4ueTUEO"/>
+    </a>
+    <a href="https://compose.alteryx.com/en/stable/?badge=stable" target="_blank">
+        <img src="https://readthedocs.com/projects/feature-labs-inc-compose/badge/?version=stable&token=5c3ace685cdb6e10eb67828a4dc74d09b20bb842980c8ee9eb4e9ed168d05b00"
+            alt="ReadTheDocs" />
+    </a>
+    <a href="https://badge.fury.io/py/composeml" target="_blank">
+        <img src="https://badge.fury.io/py/composeml.svg?maxAge=2592000" alt="PyPI Version" />
+    </a>
+    <a href="https://stackoverflow.com/questions/tagged/compose-ml" target="_blank">
+        <img src="https://img.shields.io/badge/questions-on_stackoverflow-blue.svg?" alt="StackOverflow" />
+    </a>
+    <a href="https://pepy.tech/project/composeml" target="_blank">
+        <img src="https://pepy.tech/badge/composeml/month" alt="PyPI Downloads" />
+    </a>
+</p>
+<hr>
+
+[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:
+
+<br><p align="center"><img width=90% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/workflow.png" alt="Compose" /></p><br>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>transaction_id</th>
+      <th>session_id</th>
+      <th>transaction_time</th>
+      <th>product_id</th>
+      <th>amount</th>
+      <th>customer_id</th>
+      <th>device</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>298</td>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>5</td>
+      <td>127.64</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>10</td>
+      <td>1</td>
+      <td>2014-01-01 00:09:45</td>
+      <td>5</td>
+      <td>57.39</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>495</td>
+      <td>1</td>
+      <td>2014-01-01 00:14:05</td>
+      <td>5</td>
+      <td>69.45</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>460</td>
+      <td>10</td>
+      <td>2014-01-01 02:33:50</td>
+      <td>5</td>
+      <td>123.19</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+    <tr>
+      <td>302</td>
+      <td>10</td>
+      <td>2014-01-01 02:37:05</td>
+      <td>5</td>
+      <td>64.47</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+  </tbody>
+</table>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>customer_id</th>
+      <th>time</th>
+      <th>total_spent</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>3</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+
+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.
+
+<p align="center">
+  <a href="https://www.alteryx.com/open-source">
+    <img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
+  </a>
+</p>
+
+
+%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
+<p align="center"><img width=50% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/compose.png" alt="Compose" /></p>
+<p align="center"><i>"Build better training examples in a fraction of the time."</i></p>
+<p align="center">
+    <a href="https://github.com/alteryx/compose/actions?query=workflow%3ATests" target="_blank">
+        <img src="https://github.com/alteryx/compose/workflows/Tests/badge.svg" alt="Tests" />
+    </a>
+    <a href="https://codecov.io/gh/alteryx/compose">
+        <img src="https://codecov.io/gh/alteryx/compose/branch/main/graph/badge.svg?token=mDz4ueTUEO"/>
+    </a>
+    <a href="https://compose.alteryx.com/en/stable/?badge=stable" target="_blank">
+        <img src="https://readthedocs.com/projects/feature-labs-inc-compose/badge/?version=stable&token=5c3ace685cdb6e10eb67828a4dc74d09b20bb842980c8ee9eb4e9ed168d05b00"
+            alt="ReadTheDocs" />
+    </a>
+    <a href="https://badge.fury.io/py/composeml" target="_blank">
+        <img src="https://badge.fury.io/py/composeml.svg?maxAge=2592000" alt="PyPI Version" />
+    </a>
+    <a href="https://stackoverflow.com/questions/tagged/compose-ml" target="_blank">
+        <img src="https://img.shields.io/badge/questions-on_stackoverflow-blue.svg?" alt="StackOverflow" />
+    </a>
+    <a href="https://pepy.tech/project/composeml" target="_blank">
+        <img src="https://pepy.tech/badge/composeml/month" alt="PyPI Downloads" />
+    </a>
+</p>
+<hr>
+
+[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:
+
+<br><p align="center"><img width=90% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/workflow.png" alt="Compose" /></p><br>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>transaction_id</th>
+      <th>session_id</th>
+      <th>transaction_time</th>
+      <th>product_id</th>
+      <th>amount</th>
+      <th>customer_id</th>
+      <th>device</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>298</td>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>5</td>
+      <td>127.64</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>10</td>
+      <td>1</td>
+      <td>2014-01-01 00:09:45</td>
+      <td>5</td>
+      <td>57.39</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>495</td>
+      <td>1</td>
+      <td>2014-01-01 00:14:05</td>
+      <td>5</td>
+      <td>69.45</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>460</td>
+      <td>10</td>
+      <td>2014-01-01 02:33:50</td>
+      <td>5</td>
+      <td>123.19</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+    <tr>
+      <td>302</td>
+      <td>10</td>
+      <td>2014-01-01 02:37:05</td>
+      <td>5</td>
+      <td>64.47</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+  </tbody>
+</table>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>customer_id</th>
+      <th>time</th>
+      <th>total_spent</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>3</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+
+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.
+
+<p align="center">
+  <a href="https://www.alteryx.com/open-source">
+    <img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
+  </a>
+</p>
+
+
+%package help
+Summary:	Development documents and examples for composeml
+Provides:	python3-composeml-doc
+%description help
+<p align="center"><img width=50% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/compose.png" alt="Compose" /></p>
+<p align="center"><i>"Build better training examples in a fraction of the time."</i></p>
+<p align="center">
+    <a href="https://github.com/alteryx/compose/actions?query=workflow%3ATests" target="_blank">
+        <img src="https://github.com/alteryx/compose/workflows/Tests/badge.svg" alt="Tests" />
+    </a>
+    <a href="https://codecov.io/gh/alteryx/compose">
+        <img src="https://codecov.io/gh/alteryx/compose/branch/main/graph/badge.svg?token=mDz4ueTUEO"/>
+    </a>
+    <a href="https://compose.alteryx.com/en/stable/?badge=stable" target="_blank">
+        <img src="https://readthedocs.com/projects/feature-labs-inc-compose/badge/?version=stable&token=5c3ace685cdb6e10eb67828a4dc74d09b20bb842980c8ee9eb4e9ed168d05b00"
+            alt="ReadTheDocs" />
+    </a>
+    <a href="https://badge.fury.io/py/composeml" target="_blank">
+        <img src="https://badge.fury.io/py/composeml.svg?maxAge=2592000" alt="PyPI Version" />
+    </a>
+    <a href="https://stackoverflow.com/questions/tagged/compose-ml" target="_blank">
+        <img src="https://img.shields.io/badge/questions-on_stackoverflow-blue.svg?" alt="StackOverflow" />
+    </a>
+    <a href="https://pepy.tech/project/composeml" target="_blank">
+        <img src="https://pepy.tech/badge/composeml/month" alt="PyPI Downloads" />
+    </a>
+</p>
+<hr>
+
+[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:
+
+<br><p align="center"><img width=90% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/workflow.png" alt="Compose" /></p><br>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>transaction_id</th>
+      <th>session_id</th>
+      <th>transaction_time</th>
+      <th>product_id</th>
+      <th>amount</th>
+      <th>customer_id</th>
+      <th>device</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>298</td>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>5</td>
+      <td>127.64</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>10</td>
+      <td>1</td>
+      <td>2014-01-01 00:09:45</td>
+      <td>5</td>
+      <td>57.39</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>495</td>
+      <td>1</td>
+      <td>2014-01-01 00:14:05</td>
+      <td>5</td>
+      <td>69.45</td>
+      <td>2</td>
+      <td>desktop</td>
+    </tr>
+    <tr>
+      <td>460</td>
+      <td>10</td>
+      <td>2014-01-01 02:33:50</td>
+      <td>5</td>
+      <td>123.19</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+    <tr>
+      <td>302</td>
+      <td>10</td>
+      <td>2014-01-01 02:37:05</td>
+      <td>5</td>
+      <td>64.47</td>
+      <td>2</td>
+      <td>tablet</td>
+    </tr>
+  </tbody>
+</table>
+
+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()
+```
+
+<table border="0" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th>customer_id</th>
+      <th>time</th>
+      <th>total_spent</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>2014-01-01 01:00:00</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <td>3</td>
+      <td>2014-01-01 00:00:00</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+
+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.
+
+<p align="center">
+  <a href="https://www.alteryx.com/open-source">
+    <img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/>
+  </a>
+</p>
+
+
+%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 <Python_Bot@openeuler.org> - 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