Metadata-Version: 2.1
Name: composeml
Version: 0.5.0
Summary: UNKNOWN
Home-page: https://compose.alteryx.com
Author: Feature Labs, Inc.
Author-email: support@featurelabs.com
License: BSD 3-clause
Description: <p align="center"><img width=50% src="https://raw.githubusercontent.com/FeatureLabs/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://circleci.com/gh/FeatureLabs/compose/tree/main" target="_blank">
                <img src="https://circleci.com/gh/FeatureLabs/compose/tree/main.svg?" alt="CircleCI" />
            </a>
            <a href="https://codecov.io/gh/FeatureLabs/compose" target="_blank">
                <img src="https://codecov.io/gh/FeatureLabs/compose/branch/main/graph/badge.svg?" alt="Codecov" />
            </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/composeml" target="_blank">
                <img src="https://img.shields.io/badge/questions-on_stackoverflow-blue.svg?" alt="StackOverflow" />
            </a>
            <a href="https://pypistats.org/packages/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/FeatureLabs/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.
        
        ## Install
        Compose can be installed for Python 3.6 or later by running the following command:
        
        ```
        pip install composeml
        ```
        
        ## 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_entity="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/composeml) with the `composeml` tag.
        2. For bugs, issues, or feature requests start a Github [issue](https://github.com/FeatureLabs/compose/issues/new).
        3. For discussion regarding development on the core library, use [Slack](https://featuretools.slack.com/messages/CKP6D0KUP).
        
        ## 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:
        
        ```
        @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). 
        
        ## Innovation Labs
        
        <a href="https://www.alteryx.com/innovation-labs" target="_blank">
            <p align="left"><img width=40% src="https://raw.githubusercontent.com/FeatureLabs/compose/main/docs/source/images/innovation_labs.png" alt="Innovation Labs" /></p>
        </a>
        
        Compose has been developed and open sourced by Innovation Labs. We developed Compose to enable flexible definition of the machine learning task. To see the other open source projects we're working on visit [Innovation Labs](https://www.alteryx.com/innovation-labs).
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
