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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3.6",
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        "description": "![KTS logo](https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/banner_alpha.png)\n\n[![PyPI version](https://img.shields.io/pypi/v/kts.svg)](https://pypi.org/project/kts/)\n[![Docs](https://img.shields.io/badge/docs-draft%20version-orange)](https://docs.kts.ai/)\n[![CI](https://github.com/konodyuk/kts/workflows/CI/badge.svg)](https://github.com/konodyuk/kts/actions/)\n[![Codecov](https://img.shields.io/codecov/c/github/konodyuk/kts?label=core%20coverage)](https://codecov.io/gh/konodyuk/kts)\n[![CodeFactor](https://www.codefactor.io/repository/github/konodyuk/kts/badge)](https://www.codefactor.io/repository/github/konodyuk/kts)\n\n**An interactive environment for modular feature engineering, experiment tracking, feature selection and stacking.**\n\nInstall KTS with `pip install kts`. Compatible with Python 3.6+.\n\n## Modular Feature Engineering\n<p align=\"center\">\n    <br>\n    <img width=600 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/modularity_diagram.png\"/>\n    <br>\n    <br>\n    <strong>Define features as independent blocks to organize your projects.</strong>\n    <br>\n</p>\n\n## Source Code Tracking\n<p align=\"center\">\n    <br>\n    <img width=800 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_constructor.png\"/>\n    <br>\n    <br>\n    <strong>Track source code of every feature and experiment to make each of them reproducible.</strong>\n    <br>\n</p>\n\n## Parallel Computing and Caching\n<p align=\"center\">\n    <br>\n    <img width=800 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_computing.png\"/>\n    <br>\n    <br>\n    <strong>Compute independent features in parallel. Cache them to avoid repeated computations.</strong>\n    <br>\n</p>\n\n## Experiment Tracking\n<p align=\"center\">\n    <br>\n    <img width=800 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/leaderboard.png\"/>\n    <br>\n    <br>\n    <strong>Track your progress with local leaderboards.</strong>\n    <br>\n</p>\n\n## Feature Selection\n<p align=\"center\">\n    <br>\n    <img width=800 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_importances.png\"/>\n    <br><br>\n    <strong>Compute feature importances and select features from any experiment <br> with <code>experiment.feature_importances()</code> and <code>experiment.select()</code>.</strong>\n    <br>\n</p>\n\n## Interactivity and Rich Reports\n<p align=\"center\">\n    <br>\n    <img width=800 src=\"https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/fitting.png\"/>\n    <br>\n    <br>\n    <strong>Monitor the progress of everything going on in KTS with our interactive reports. <br> From model fitting to computing feature importances.</strong>\n    <br>\n</p>\n\n<br>\n\n# Getting Started\n## Titanic Tutorial\nStart exploring KTS with tutorial based on [Titanic dataset](https://www.kaggle.com/c/titanic). Run notebooks interactively in Binder or just read them in NBViewer.\n### 1. Feature Engineering\n[![nbviewer](https://img.shields.io/badge/render-nbviewer-orange)](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/konodyuk/kts/master?urlpath=/lab/tree/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb)\n- [Modular Feature Engineering in 30 seconds](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Modular-Feature-Engineering-in-30-seconds)\n- [Decorators reference](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Decorators)\n- [Feature Types](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Feature-Types)\n    - [Regular Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Regular-Features)\n    - [Features Using External Frames](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Features-Using-External-Frames)\n    - [Stateful Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Stateful-Features)\n    - [Generic Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Generic-Features)\n- [Standard Library](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Standard-Library)\n- [Feature Set](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Feature-Set)\n\n### 2. Modelling\n[![nbviewer](https://img.shields.io/badge/render-nbviewer-orange)](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/konodyuk/kts/master?urlpath=/lab/tree/tutorials/titanic/notebooks/2.%20Modelling.ipynb)\n- [Models](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Models)\n- [Validation](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Validation)\n- [Leaderboard](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Leaderboard)\n    - [Multiple Leaderboards](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Multiple-Leaderboards)\n- [Experiments](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Experiments)\n    - [Inference](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Inference)\n    - [Feature Importances](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Feature-Importances)\n- [Custom Models](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Custom-Models)\n\n### 3. Stacking\n[![nbviewer](https://img.shields.io/badge/render-nbviewer-orange)](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/konodyuk/kts/master?urlpath=/lab/tree/tutorials/titanic/notebooks/3.%20Stacking.ipynb)\n- [stl.stack](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#stl.stack)\n- [Anti-overfitting](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Anti-overfitting)\n    - [Noise](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Noise)\n    - [Refiner](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Refiner)\n- [Deep Stacking](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Deep-Stacking)\n## Documentation\nCheck out [docs.kts.ai](http://docs.kts.ai) for a more detailed description of KTS features and interfaces\n## Inline Docs\nMost of our functions and classes have rich docstrings. Read them right in your notebook, without interruption.\n\n---\n\n# Acknowledgements\nMVP of the project was designed and implemented by the team of [Mikhail Andronov](https://github.com/Academich), [Roman Gorb](https://github.com/rvg77) and [Nikita Konodyuk](https://github.com/konodyuk) under the mentorship of [Alexander Avdyushenko](https://github.com/avalur) during a project practice held by Yandex and Higher School of Economics on 1-14 February 2019 at Educational Center \u00abSirius\u00bb.\n\n\n",
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