compose

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 514)

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning.

compose Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<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:

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.

compose

compose Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

compose

compose Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README