evalml


Nameevalml JSON
Version 0.59.0 PyPI version JSON
download
home_pagehttps://github.com/alteryx/evalml/
SummaryEvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
upload_time2022-09-28 02:20:50
maintainer
docs_urlNone
authorAlteryx, Inc.
requires_python<4,>=3.8
licenseBSD 3-clause
keywords data science machine learning optimization automl
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <p align="center">
<img width=50% src="https://evalml-web-images.s3.amazonaws.com/evalml_horizontal.svg" alt="EvalML" />
</p>

<p align="center">
    <a href="https://github.com/alteryx/woodwork/actions?query=branch%3Amain+workflow%3ATests" target="_blank">
        <img src="https://github.com/alteryx/woodwork/workflows/Tests/badge.svg?branch=main" alt="Tests" />
    </a>
    <a href="https://codecov.io/gh/alteryx/evalml">
        <img src="https://codecov.io/gh/alteryx/evalml/branch/main/graph/badge.svg?token=JDc0Ib7kYL"/>
    </a>
    <a href="https://evalml.alteryx.com/en/latest/?badge=stable" target="_blank">
        <img src="https://readthedocs.com/projects/feature-labs-inc-evalml/badge/?version=stable" alt="Documentation Status" />
    </a>
    <a href="https://badge.fury.io/py/evalml" target="_blank">
        <img src="https://badge.fury.io/py/evalml.svg?maxAge=2592000" alt="PyPI Version" />
    </a>
    <a href="https://anaconda.org/conda-forge/evalml" target="_blank">
        <img src="https://anaconda.org/conda-forge/evalml/badges/version.svg" alt="Anaconda Version" />
    </a>
    <a href="https://pepy.tech/project/evalml" target="_blank">
        <img src="https://pepy.tech/badge/evalml/month" alt="PyPI Downloads" />
    </a>
</p>
<hr>

EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

**Key Functionality**

* **Automation** - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.
* **Data Checks** - Catches and warns of problems with your data and problem setup before modeling.
* **End-to-end** - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.
* **Model Understanding** - Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.
* **Domain-specific** - Includes repository of domain-specific objective functions and an interface to define your own.

## Installation 

Install from [PyPI](https://pypi.org/project/evalml/):

```bash
pip install evalml
```

or from the conda-forge channel on [conda](https://anaconda.org/conda-forge/evalml):

```bash
conda install -c conda-forge evalml
```

### Add-ons
**Update checker** - Receive automatic notifications of new Woodwork releases

PyPI:

```bash
pip install "evalml[update_checker]"
```
Conda:
```
conda install -c conda-forge alteryx-open-src-update-checker
```

## Start

#### Load and split example data 
```python
import evalml
X, y = evalml.demos.load_breast_cancer()
X_train, X_test, y_train, y_test = evalml.preprocessing.split_data(X, y, problem_type='binary')
```

#### Run AutoML
```python
from evalml.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()
```

#### View pipeline rankings
```python
automl.rankings
```

#### Get best pipeline and predict on new data
```python
pipeline = automl.best_pipeline
pipeline.predict(X_test)
```

## Next Steps

Read more about EvalML on our [documentation page](https://evalml.alteryx.com/):

* [Installation](https://evalml.alteryx.com/en/stable/install.html) and [getting started](https://evalml.alteryx.com/en/stable/start.html).
* [Tutorials](https://evalml.alteryx.com/en/stable/tutorials.html) on how to use EvalML.
* [User guide](https://evalml.alteryx.com/en/stable/user_guide.html) which describes EvalML's features.
* Full [API reference](https://evalml.alteryx.com/en/stable/api_reference.html)

## Support

The EvalML community is happy to provide support to users of EvalML. Project support can be found in four places depending on the type of question:
1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/evalml) with the `evalml` tag.
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/evalml/issues).
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

## Built at Alteryx

**EvalML** is an open source project built by [Alteryx](https://www.alteryx.com). 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>

            

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    "description": "<p align=\"center\">\n<img width=50% src=\"https://evalml-web-images.s3.amazonaws.com/evalml_horizontal.svg\" alt=\"EvalML\" />\n</p>\n\n<p align=\"center\">\n    <a href=\"https://github.com/alteryx/woodwork/actions?query=branch%3Amain+workflow%3ATests\" target=\"_blank\">\n        <img src=\"https://github.com/alteryx/woodwork/workflows/Tests/badge.svg?branch=main\" alt=\"Tests\" />\n    </a>\n    <a href=\"https://codecov.io/gh/alteryx/evalml\">\n        <img src=\"https://codecov.io/gh/alteryx/evalml/branch/main/graph/badge.svg?token=JDc0Ib7kYL\"/>\n    </a>\n    <a href=\"https://evalml.alteryx.com/en/latest/?badge=stable\" target=\"_blank\">\n        <img src=\"https://readthedocs.com/projects/feature-labs-inc-evalml/badge/?version=stable\" alt=\"Documentation Status\" />\n    </a>\n    <a href=\"https://badge.fury.io/py/evalml\" target=\"_blank\">\n        <img src=\"https://badge.fury.io/py/evalml.svg?maxAge=2592000\" alt=\"PyPI Version\" />\n    </a>\n    <a href=\"https://anaconda.org/conda-forge/evalml\" target=\"_blank\">\n        <img src=\"https://anaconda.org/conda-forge/evalml/badges/version.svg\" alt=\"Anaconda Version\" />\n    </a>\n    <a href=\"https://pepy.tech/project/evalml\" target=\"_blank\">\n        <img src=\"https://pepy.tech/badge/evalml/month\" alt=\"PyPI Downloads\" />\n    </a>\n</p>\n<hr>\n\nEvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.\n\n**Key Functionality**\n\n* **Automation** - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.\n* **Data Checks** - Catches and warns of problems with your data and problem setup before modeling.\n* **End-to-end** - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.\n* **Model Understanding** - Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.\n* **Domain-specific** - Includes repository of domain-specific objective functions and an interface to define your own.\n\n## Installation \n\nInstall from [PyPI](https://pypi.org/project/evalml/):\n\n```bash\npip install evalml\n```\n\nor from the conda-forge channel on [conda](https://anaconda.org/conda-forge/evalml):\n\n```bash\nconda install -c conda-forge evalml\n```\n\n### Add-ons\n**Update checker** - Receive automatic notifications of new Woodwork releases\n\nPyPI:\n\n```bash\npip install \"evalml[update_checker]\"\n```\nConda:\n```\nconda install -c conda-forge alteryx-open-src-update-checker\n```\n\n## Start\n\n#### Load and split example data \n```python\nimport evalml\nX, y = evalml.demos.load_breast_cancer()\nX_train, X_test, y_train, y_test = evalml.preprocessing.split_data(X, y, problem_type='binary')\n```\n\n#### Run AutoML\n```python\nfrom evalml.automl import AutoMLSearch\nautoml = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')\nautoml.search()\n```\n\n#### View pipeline rankings\n```python\nautoml.rankings\n```\n\n#### Get best pipeline and predict on new data\n```python\npipeline = automl.best_pipeline\npipeline.predict(X_test)\n```\n\n## Next Steps\n\nRead more about EvalML on our [documentation page](https://evalml.alteryx.com/):\n\n* [Installation](https://evalml.alteryx.com/en/stable/install.html) and [getting started](https://evalml.alteryx.com/en/stable/start.html).\n* [Tutorials](https://evalml.alteryx.com/en/stable/tutorials.html) on how to use EvalML.\n* [User guide](https://evalml.alteryx.com/en/stable/user_guide.html) which describes EvalML's features.\n* Full [API reference](https://evalml.alteryx.com/en/stable/api_reference.html)\n\n## Support\n\nThe EvalML community is happy to provide support to users of EvalML. Project support can be found in four places depending on the type of question:\n1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/evalml) with the `evalml` tag.\n2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/evalml/issues).\n3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).\n4. For everything else, the core developers can be reached by email at open_source_support@alteryx.com\n\n## Built at Alteryx\n\n**EvalML** is an open source project built by [Alteryx](https://www.alteryx.com). To see the other open source projects we\u2019re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). 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