Metadata-Version: 2.1
Name: kts
Version: 0.4.0
Summary: A framework for fast and interactive conducting machine learning experiments on tabular data
Home-page: https://github.com/konodyuk/kts
Author: Nikita Konodyuk
Author-email: konodyuk@gmail.com
License: UNKNOWN
Description: ![KTS logo](https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/banner_alpha.png)
        
        [![PyPI version](https://img.shields.io/pypi/v/kts.svg)](https://pypi.org/project/kts/)
        [![Docs](https://img.shields.io/badge/docs-draft%20version-orange)](https://docs.kts.ai/)
        [![CI](https://github.com/konodyuk/kts/workflows/CI/badge.svg)](https://github.com/konodyuk/kts/actions/)
        [![Codecov](https://img.shields.io/codecov/c/github/konodyuk/kts?label=core%20coverage)](https://codecov.io/gh/konodyuk/kts)
        [![CodeFactor](https://www.codefactor.io/repository/github/konodyuk/kts/badge)](https://www.codefactor.io/repository/github/konodyuk/kts)
        
        **An interactive environment for modular feature engineering, experiment tracking, feature selection and stacking.**
        
        Install KTS with `pip install kts`. Compatible with Python 3.6+.
        
        ## Modular Feature Engineering
        <p align="center">
            <br>
            <img width=600 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/modularity_diagram.png"/>
            <br>
            <br>
            <strong>Define features as independent blocks to organize your projects.</strong>
            <br>
        </p>
        
        ## Source Code Tracking
        <p align="center">
            <br>
            <img width=800 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_constructor.png"/>
            <br>
            <br>
            <strong>Track source code of every feature and experiment to make each of them reproducible.</strong>
            <br>
        </p>
        
        ## Parallel Computing and Caching
        <p align="center">
            <br>
            <img width=800 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_computing.png"/>
            <br>
            <br>
            <strong>Compute independent features in parallel. Cache them to avoid repeated computations.</strong>
            <br>
        </p>
        
        ## Experiment Tracking
        <p align="center">
            <br>
            <img width=800 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/leaderboard.png"/>
            <br>
            <br>
            <strong>Track your progress with local leaderboards.</strong>
            <br>
        </p>
        
        ## Feature Selection
        <p align="center">
            <br>
            <img width=800 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/feature_importances.png"/>
            <br><br>
            <strong>Compute feature importances and select features from any experiment <br> with <code>experiment.feature_importances()</code> and <code>experiment.select()</code>.</strong>
            <br>
        </p>
        
        ## Interactivity and Rich Reports
        <p align="center">
            <br>
            <img width=800 src="https://raw.githubusercontent.com/konodyuk/kts/master/docs/static/fitting.png"/>
            <br>
            <br>
            <strong>Monitor the progress of everything going on in KTS with our interactive reports. <br> From model fitting to computing feature importances.</strong>
            <br>
        </p>
        
        <br>
        
        # Getting Started
        ## Titanic Tutorial
        Start 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.
        ### 1. Feature Engineering
        [![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)
        [![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)
        - [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)
        - [Decorators reference](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Decorators)
        - [Feature Types](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Feature-Types)
            - [Regular Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Regular-Features)
            - [Features Using External Frames](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Features-Using-External-Frames)
            - [Stateful Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Stateful-Features)
            - [Generic Features](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Generic-Features)
        - [Standard Library](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Standard-Library)
        - [Feature Set](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/1.%20Feature%20Engineering.ipynb#Feature-Set)
        
        ### 2. Modelling
        [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange)](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/konodyuk/kts/master?urlpath=/lab/tree/tutorials/titanic/notebooks/2.%20Modelling.ipynb)
        - [Models](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Models)
        - [Validation](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Validation)
        - [Leaderboard](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Leaderboard)
            - [Multiple Leaderboards](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Multiple-Leaderboards)
        - [Experiments](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Experiments)
            - [Inference](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Inference)
            - [Feature Importances](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Feature-Importances)
        - [Custom Models](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/2.%20Modelling.ipynb#Custom-Models)
        
        ### 3. Stacking
        [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange)](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/konodyuk/kts/master?urlpath=/lab/tree/tutorials/titanic/notebooks/3.%20Stacking.ipynb)
        - [stl.stack](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#stl.stack)
        - [Anti-overfitting](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Anti-overfitting)
            - [Noise](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Noise)
            - [Refiner](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Refiner)
        - [Deep Stacking](https://nbviewer.jupyter.org/github/konodyuk/kts/blob/dev/tutorials/titanic/notebooks/3.%20Stacking.ipynb#Deep-Stacking)
        ## Documentation
        Check out [docs.kts.ai](http://docs.kts.ai) for a more detailed description of KTS features and interfaces
        ## Inline Docs
        Most of our functions and classes have rich docstrings. Read them right in your notebook, without interruption.
        
        ---
        
        # Acknowledgements
        MVP 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 «Sirius».
        
Keywords: Machine Learning,Parallel Computing,Feature Engineering
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
Provides-Extra: models
Provides-Extra: hpo
Provides-Extra: all
