%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
"Build better training examples in a fraction of the time."
[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:
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_id |
session_id |
transaction_time |
product_id |
amount |
customer_id |
device |
| 298 |
1 |
2014-01-01 00:00:00 |
5 |
127.64 |
2 |
desktop |
| 10 |
1 |
2014-01-01 00:09:45 |
5 |
57.39 |
2 |
desktop |
| 495 |
1 |
2014-01-01 00:14:05 |
5 |
69.45 |
2 |
desktop |
| 460 |
10 |
2014-01-01 02:33:50 |
5 |
123.19 |
2 |
tablet |
| 302 |
10 |
2014-01-01 02:37:05 |
5 |
64.47 |
2 |
tablet |
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_id |
time |
total_spent |
| 1 |
2014-01-01 00:00:00 |
True |
| 1 |
2014-01-01 01:00:00 |
True |
| 2 |
2014-01-01 00:00:00 |
False |
| 2 |
2014-01-01 01:00:00 |
False |
| 3 |
2014-01-01 00:00:00 |
False |
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.
%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
"Build better training examples in a fraction of the time."
[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:
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_id |
session_id |
transaction_time |
product_id |
amount |
customer_id |
device |
| 298 |
1 |
2014-01-01 00:00:00 |
5 |
127.64 |
2 |
desktop |
| 10 |
1 |
2014-01-01 00:09:45 |
5 |
57.39 |
2 |
desktop |
| 495 |
1 |
2014-01-01 00:14:05 |
5 |
69.45 |
2 |
desktop |
| 460 |
10 |
2014-01-01 02:33:50 |
5 |
123.19 |
2 |
tablet |
| 302 |
10 |
2014-01-01 02:37:05 |
5 |
64.47 |
2 |
tablet |
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_id |
time |
total_spent |
| 1 |
2014-01-01 00:00:00 |
True |
| 1 |
2014-01-01 01:00:00 |
True |
| 2 |
2014-01-01 00:00:00 |
False |
| 2 |
2014-01-01 01:00:00 |
False |
| 3 |
2014-01-01 00:00:00 |
False |
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.
%package help
Summary: Development documents and examples for composeml
Provides: python3-composeml-doc
%description help
"Build better training examples in a fraction of the time."
[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:
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_id |
session_id |
transaction_time |
product_id |
amount |
customer_id |
device |
| 298 |
1 |
2014-01-01 00:00:00 |
5 |
127.64 |
2 |
desktop |
| 10 |
1 |
2014-01-01 00:09:45 |
5 |
57.39 |
2 |
desktop |
| 495 |
1 |
2014-01-01 00:14:05 |
5 |
69.45 |
2 |
desktop |
| 460 |
10 |
2014-01-01 02:33:50 |
5 |
123.19 |
2 |
tablet |
| 302 |
10 |
2014-01-01 02:37:05 |
5 |
64.47 |
2 |
tablet |
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_id |
time |
total_spent |
| 1 |
2014-01-01 00:00:00 |
True |
| 1 |
2014-01-01 01:00:00 |
True |
| 2 |
2014-01-01 00:00:00 |
False |
| 2 |
2014-01-01 01:00:00 |
False |
| 3 |
2014-01-01 00:00:00 |
False |
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.
%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